examples of experimental research designs

19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

Related posts:

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• 40+ Famous Psychologists (Images + Biographies)
• 11+ Psychology Experiment Ideas (Goals + Methods)
• The Little Albert Experiment
• 41+ White Collar Job Examples (Salary + Path)

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them.

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive.
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure.

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Experimental Research vs. Alternatives and When to Use Them

1. experimental research vs causal comparative.

Experimental research enables you to control variables and identify how the independent variable affects the dependent variable. Causal-comparative find out the cause-and-effect relationship between the variables by comparing already existing groups that are affected differently by the independent variable.

For example, in an experiment to see how K-12 education affects children and teenager development. An experimental research would split the children into groups, some would get formal K-12 education, while others won’t. This is not ethically right because every child has the right to education. So, what we do instead would be to compare already existing groups of children who are getting formal education with those who due to some circumstances can not.

Pros and Cons of Experimental vs Causal-Comparative Research

• Causal-Comparative:   Strengths:  More realistic than experiments, can be conducted in real-world settings.  Weaknesses:  Establishing causality can be weaker due to the lack of manipulation.

2. Experimental Research vs Correlational Research

When experimenting, you are trying to establish a cause-and-effect relationship between different variables. For example, you are trying to establish the effect of heat on water, the temperature keeps changing (independent variable) and you see how it affects the water (dependent variable).

For correlational research, you are not necessarily interested in the why or the cause-and-effect relationship between the variables, you are focusing on the relationship. Using the same water and temperature example, you are only interested in the fact that they change, you are not investigating which of the variables or other variables causes them to change.

Pros and Cons of Experimental vs Correlational Research

3. experimental research vs descriptive research.

With experimental research, you alter the independent variable to see how it affects the dependent variable, but with descriptive research you are simply studying the characteristics of the variable you are studying.

So, in an experiment to see how blown glass reacts to temperature, experimental research would keep altering the temperature to varying levels of high and low to see how it affects the dependent variable (glass). But descriptive research would investigate the glass properties.

Pros and Cons of Experimental vs Descriptive Research

4. experimental research vs action research.

Experimental research tests for causal relationships by focusing on one independent variable vs the dependent variable and keeps other variables constant. So, you are testing hypotheses and using the information from the research to contribute to knowledge.

However, with action research, you are using a real-world setting which means you are not controlling variables. You are also performing the research to solve actual problems and improve already established practices.

For example, if you are testing for how long commutes affect workers’ productivity. With experimental research, you would vary the length of commute to see how the time affects work. But with action research, you would account for other factors such as weather, commute route, nutrition, etc. Also, experimental research helps know the relationship between commute time and productivity, while action research helps you look for ways to improve productivity

Pros and Cons of Experimental vs Action Research

Conclusion  .

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

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Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

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• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

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Experimental design: Guide, steps, examples

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27 April 2023

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Experimental research design is a scientific framework that allows you to manipulate one or more variables while controlling the test environment. 

When testing a theory or new product, it can be helpful to have a certain level of control and manipulate variables to discover different outcomes. You can use these experiments to determine cause and effect or study variable associations. 

This guide explores the types of experimental design, the steps in designing an experiment, and the advantages and limitations of experimental design. 

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• What is experimental research design?

You can determine the relationship between each of the variables by: 

Manipulating one or more independent variables (i.e., stimuli or treatments)

Applying the changes to one or more dependent variables (i.e., test groups or outcomes)

With the ability to analyze the relationship between variables and using measurable data, you can increase the accuracy of the result. 

What is a good experimental design?

A good experimental design requires: 

Significant planning to ensure control over the testing environment

Sound experimental treatments

Properly assigning subjects to treatment groups

Without proper planning, unexpected external variables can alter an experiment's outcome. 

To meet your research goals, your experimental design should include these characteristics:

Provide unbiased estimates of inputs and associated uncertainties

Enable the researcher to detect differences caused by independent variables

Include a plan for analysis and reporting of the results

Provide easily interpretable results with specific conclusions

What's the difference between experimental and quasi-experimental design?

The major difference between experimental and quasi-experimental design is the random assignment of subjects to groups. 

A true experiment relies on certain controls. Typically, the researcher designs the treatment and randomly assigns subjects to control and treatment groups. 

However, these conditions are unethical or impossible to achieve in some situations.

When it's unethical or impractical to assign participants randomly, that’s when a quasi-experimental design comes in. 

This design allows researchers to conduct a similar experiment by assigning subjects to groups based on non-random criteria. 

Another type of quasi-experimental design might occur when the researcher doesn't have control over the treatment but studies pre-existing groups after they receive different treatments.

When can a researcher conduct experimental research?

Various settings and professions can use experimental research to gather information and observe behavior in controlled settings. 

Basically, a researcher can conduct experimental research any time they want to test a theory with variable and dependent controls. 

Experimental research is an option when the project includes an independent variable and a desire to understand the relationship between cause and effect. 

• The importance of experimental research design

Experimental research enables researchers to conduct studies that provide specific, definitive answers to questions and hypotheses. 

Researchers can test Independent variables in controlled settings to:

Test the effectiveness of a new medication

Design better products for consumers

Answer questions about human health and behavior

Developing a quality research plan means a researcher can accurately answer vital research questions with minimal error. As a result, definitive conclusions can influence the future of the independent variable. 

Types of experimental research designs

There are three main types of experimental research design. The research type you use will depend on the criteria of your experiment, your research budget, and environmental limitations. 

Pre-experimental research design

A pre-experimental research study is a basic observational study that monitors independent variables’ effects. 

During research, you observe one or more groups after applying a treatment to test whether the treatment causes any change. 

The three subtypes of pre-experimental research design are:

One-shot case study research design

This research method introduces a single test group to a single stimulus to study the results at the end of the application. 

After researchers presume the stimulus or treatment has caused changes, they gather results to determine how it affects the test subjects. 

One-group pretest-posttest design

This method uses a single test group but includes a pretest study as a benchmark. The researcher applies a test before and after the group’s exposure to a specific stimulus. 

Static group comparison design

This method includes two or more groups, enabling the researcher to use one group as a control. They apply a stimulus to one group and leave the other group static. 

A posttest study compares the results among groups. 

True experimental research design

A true experiment is the most common research method. It involves statistical analysis to prove or disprove a specific hypothesis . 

Under completely experimental conditions, researchers expose participants in two or more randomized groups to different stimuli. 

Random selection removes any potential for bias, providing more reliable results. 

These are the three main sub-groups of true experimental research design:

Posttest-only control group design

This structure requires the researcher to divide participants into two random groups. One group receives no stimuli and acts as a control while the other group experiences stimuli.

Researchers perform a test at the end of the experiment to observe the stimuli exposure results.

Pretest-posttest control group design

This test also requires two groups. It includes a pretest as a benchmark before introducing the stimulus. 

The pretest introduces multiple ways to test subjects. For instance, if the control group also experiences a change, it reveals that taking the test twice changes the results.

Solomon four-group design

This structure divides subjects into two groups, with two as control groups. Researchers assign the first control group a posttest only and the second control group a pretest and a posttest. 

The two variable groups mirror the control groups, but researchers expose them to stimuli. The ability to differentiate between groups in multiple ways provides researchers with more testing approaches for data-based conclusions. 

Quasi-experimental research design

Although closely related to a true experiment, quasi-experimental research design differs in approach and scope. 

Quasi-experimental research design doesn’t have randomly selected participants. Researchers typically divide the groups in this research by pre-existing differences. 

Quasi-experimental research is more common in educational studies, nursing, or other research projects where it's not ethical or practical to use randomized subject groups.

• 5 steps for designing an experiment

Experimental research requires a clearly defined plan to outline the research parameters and expected goals. 

Here are five key steps in designing a successful experiment:

Step 1: Define variables and their relationship

Your experiment should begin with a question: What are you hoping to learn through your experiment? 

The relationship between variables in your study will determine your answer.

Define the independent variable (the intended stimuli) and the dependent variable (the expected effect of the stimuli). After identifying these groups, consider how you might control them in your experiment. 

Could natural variations affect your research? If so, your experiment should include a pretest and posttest. 

Step 2: Develop a specific, testable hypothesis

With a firm understanding of the system you intend to study, you can write a specific, testable hypothesis. 

What is the expected outcome of your study? 

Develop a prediction about how the independent variable will affect the dependent variable. 

How will the stimuli in your experiment affect your test subjects? 

Your hypothesis should provide a prediction of the answer to your research question . 

Step 3: Design experimental treatments to manipulate your independent variable

Depending on your experiment, your variable may be a fixed stimulus (like a medical treatment) or a variable stimulus (like a period during which an activity occurs). 

Determine which type of stimulus meets your experiment’s needs and how widely or finely to vary your stimuli. 

Step 4: Assign subjects to groups

When you have a clear idea of how to carry out your experiment, you can determine how to assemble test groups for an accurate study. 

When choosing your study groups, consider: 

The size of your experiment

Whether you can select groups randomly

Your target audience for the outcome of the study

You should be able to create groups with an equal number of subjects and include subjects that match your target audience. Remember, you should assign one group as a control and use one or more groups to study the effects of variables. 

Step 5: Plan how to measure your dependent variable

This step determines how you'll collect data to determine the study's outcome. You should seek reliable and valid measurements that minimize research bias or error. 

You can measure some data with scientific tools, while you’ll need to operationalize other forms to turn them into measurable observations.

• Advantages of experimental research

Experimental research is an integral part of our world. It allows researchers to conduct experiments that answer specific questions. 

While researchers use many methods to conduct different experiments, experimental research offers these distinct benefits:

Researchers can determine cause and effect by manipulating variables.

It gives researchers a high level of control.

Researchers can test multiple variables within a single experiment.

All industries and fields of knowledge can use it. 

Researchers can duplicate results to promote the validity of the study .

Replicating natural settings rapidly means immediate research.

Researchers can combine it with other research methods.

It provides specific conclusions about the validity of a product, theory, or idea.

• Disadvantages (or limitations) of experimental research

Unfortunately, no research type yields ideal conditions or perfect results. 

While experimental research might be the right choice for some studies, certain conditions could render experiments useless or even dangerous. 

Before conducting experimental research, consider these disadvantages and limitations:

Required professional qualification

Only competent professionals with an academic degree and specific training are qualified to conduct rigorous experimental research. This ensures results are unbiased and valid. 

Limited scope

Experimental research may not capture the complexity of some phenomena, such as social interactions or cultural norms. These are difficult to control in a laboratory setting.

Resource-intensive

Experimental research can be expensive, time-consuming, and require significant resources, such as specialized equipment or trained personnel.

Limited generalizability

The controlled nature means the research findings may not fully apply to real-world situations or people outside the experimental setting.

Practical or ethical concerns

Some experiments may involve manipulating variables that could harm participants or violate ethical guidelines . 

Researchers must ensure their experiments do not cause harm or discomfort to participants. 

Sometimes, recruiting a sample of people to randomly assign may be difficult. 

• Experimental research design example

Experiments across all industries and research realms provide scientists, developers, and other researchers with definitive answers. These experiments can solve problems, create inventions, and heal illnesses. 

Product design testing is an excellent example of experimental research. 

A company in the product development phase creates multiple prototypes for testing. With a randomized selection, researchers introduce each test group to a different prototype. 

When groups experience different product designs , the company can assess which option most appeals to potential customers. 

Experimental research design provides researchers with a controlled environment to conduct experiments that evaluate cause and effect. 

Using the five steps to develop a research plan ensures you anticipate and eliminate external variables while answering life’s crucial questions.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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13 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

This post is really helpful.

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

This post has been very useful to me. Confusing areas have been cleared

This is very helpful and very useful!

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6.2 Experimental Design

Learning objectives.

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 college students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assign participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. Table 6.2 “Block Randomization Sequence for Assigning Nine Participants to Three Conditions” shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Table 6.2 Block Randomization Sequence for Assigning Nine Participants to Three Conditions

Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Treatment and Control Conditions

Between-subjects experiments are often used to determine whether a treatment works. In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008).

Placebo effects are interesting in their own right (see Note 6.28 “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Figure 6.2 Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This is what is shown by a comparison of the two outer bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a waitlist control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?”

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999). There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002). The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

Research has shown that patients with osteoarthritis of the knee who receive a “sham surgery” experience reductions in pain and improvement in knee function similar to those of patients who receive a real surgery.

Army Medicine – Surgery – CC BY 2.0.

Within-Subjects Experiments

In a within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in carryover effects. A carryover effect is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This is called a context effect . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is counterbalancing , which means testing different participants in different orders. For example, some participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 Is “Larger” Than 221

Researcher Michael Birnbaum has argued that the lack of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this, he asked one group of participants to rate how large the number 9 was on a 1-to-10 rating scale and another group to rate how large the number 221 was on the same 1-to-10 rating scale (Birnbaum, 1999). Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this is because participants spontaneously compared 9 with other one-digit numbers (in which case it is relatively large) and compared 221 with other three-digit numbers (in which case it is relatively small).

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. There are many ways to determine the order in which the stimuli are presented, but one common way is to generate a different random order for each participant.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often do exactly this.

Key Takeaways

• Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.
• Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.
• Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.

Discussion: For each of the following topics, list the pros and cons of a between-subjects and within-subjects design and decide which would be better.

• You want to test the relative effectiveness of two training programs for running a marathon.
• Using photographs of people as stimuli, you want to see if smiling people are perceived as more intelligent than people who are not smiling.
• In a field experiment, you want to see if the way a panhandler is dressed (neatly vs. sloppily) affects whether or not passersby give him any money.
• You want to see if concrete nouns (e.g., dog ) are recalled better than abstract nouns (e.g., truth ).
• Discussion: Imagine that an experiment shows that participants who receive psychodynamic therapy for a dog phobia improve more than participants in a no-treatment control group. Explain a fundamental problem with this research design and at least two ways that it might be corrected.

Birnbaum, M. H. (1999). How to show that 9 > 221: Collect judgments in a between-subjects design. Psychological Methods, 4 , 243–249.

Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88.

Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590.

Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

15 Experimental Design Examples

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Experimental design involves testing an independent variable against a dependent variable. It is a central feature of the scientific method .

A simple example of an experimental design is a clinical trial, where research participants are placed into control and treatment groups in order to determine the degree to which an intervention in the treatment group is effective.

There are three categories of experimental design . They are:

• Pre-Experimental Design: Testing the effects of the independent variable on a single participant or a small group of participants (e.g. a case study).
• Quasi-Experimental Design: Testing the effects of the independent variable on a group of participants who aren’t randomly assigned to treatment and control groups (e.g. purposive sampling).
• True Experimental Design: Testing the effects of the independent variable on a group of participants who are randomly assigned to treatment and control groups in order to infer causality (e.g. clinical trials).

A good research student can look at a design’s methodology and correctly categorize it. Below are some typical examples of experimental designs, with their type indicated.

Experimental Design Examples

The following are examples of experimental design (with their type indicated).

1. Action Research in the Classroom

Type: Pre-Experimental Design

A teacher wants to know if a small group activity will help students learn how to conduct a survey. So, they test the activity out on a few of their classes and make careful observations regarding the outcome.

The teacher might observe that the students respond well to the activity and seem to be learning the material quickly.

However, because there was no comparison group of students that learned how to do a survey with a different methodology, the teacher cannot be certain that the activity is actually the best method for teaching that subject.

2. Study on the Impact of an Advertisement

An advertising firm has assigned two of their best staff to develop a quirky ad about eating a brand’s new breakfast product.

The team puts together an unusual skit that involves characters enjoying the breakfast while engaged in silly gestures and zany background music. The ad agency doesn’t want to spend a great deal of money on the ad just yet, so the commercial is shot with a low budget. The firm then shows the ad to a small group of people just to see their reactions.

Afterwards they determine that the ad had a strong impact on viewers so they move forward with a much larger budget.

3. Case Study

A medical doctor has a hunch that an old treatment regimen might be effective in treating a rare illness.

The treatment has never been used in this manner before. So, the doctor applies the treatment to two of their patients with the illness. After several weeks, the results seem to indicate that the treatment is not causing any change in the illness. The doctor concludes that there is no need to continue the treatment or conduct a larger study with a control condition.

4. Fertilizer and Plant Growth Study

An agricultural farmer is exploring different combinations of nutrients on plant growth, so she does a small experiment.

Instead of spending a lot of time and money applying the different mixes to acres of land and waiting several months to see the results, she decides to apply the fertilizer to some small plants in the lab.

After several weeks, it appears that the plants are responding well. They are growing rapidly and producing dense branching. She shows the plants to her colleagues and they all agree that further testing is needed under better controlled conditions .

5. Mood States Study

A team of psychologists is interested in studying how mood affects altruistic behavior. They are undecided however, on how to put the research participants in a bad mood, so they try a few pilot studies out.

They try one suggestion and make a 3-minute video that shows sad scenes from famous heart-wrenching movies.

They then recruit a few people to watch the clips and measure their mood states afterwards.

The results indicate that people were put in a negative mood, but since there was no control group, the researchers cannot be 100% confident in the clip’s effectiveness.

6. Math Games and Learning Study

Type: Quasi-Experimental Design

Two teachers have developed a set of math games that they think will make learning math more enjoyable for their students. They decide to test out the games on their classes.

So, for two weeks, one teacher has all of her students play the math games. The other teacher uses the standard teaching techniques. At the end of the two weeks, all students take the same math test. The results indicate that students that played the math games did better on the test.

Although the teachers would like to say the games were the cause of the improved performance, they cannot be 100% sure because the study lacked random assignment . There are many other differences between the groups that played the games and those that did not.

Learn More: Random Assignment Examples

7. Economic Impact of Policy

An economic policy institute has decided to test the effectiveness of a new policy on the development of small business. The institute identifies two cities in a third-world country for testing.

The two cities are similar in terms of size, economic output, and other characteristics. The city in which the new policy was implemented showed a much higher growth of small businesses than the other city.

Although the two cities were similar in many ways, the researchers must be cautious in their conclusions. There may exist other differences between the two cities that effected small business growth other than the policy.

8. Parenting Styles and Academic Performance

Psychologists want to understand how parenting style affects children’s academic performance.

So, they identify a large group of parents that have one of four parenting styles: authoritarian, authoritative, permissive, or neglectful. The researchers then compare the grades of each group and discover that children raised with the authoritative parenting style had better grades than the other three groups. Although these results may seem convincing, it turns out that parents that use the authoritative parenting style also have higher SES class and can afford to provide their children with more intellectually enriching activities like summer STEAM camps.

9. Movies and Donations Study

Will the type of movie a person watches affect the likelihood that they donate to a charitable cause? To answer this question, a researcher decides to solicit donations at the exit point of a large theatre.

He chooses to study two types of movies: action-hero and murder mystery. After collecting donations for one month, he tallies the results. Patrons that watched the action-hero movie donated more than those that watched the murder mystery. Can you think of why these results could be due to something other than the movie?

10. Gender and Mindfulness Apps Study

Researchers decide to conduct a study on whether men or women benefit from mindfulness the most. So, they recruit office workers in large corporations at all levels of management.

Then, they divide the research sample up into males and females and ask the participants to use a mindfulness app once each day for at least 15 minutes.

At the end of three weeks, the researchers give all the participants a questionnaire that measures stress and also take swabs from their saliva to measure stress hormones.

The results indicate the women responded much better to the apps than males and showed lower stress levels on both measures.

Unfortunately, it is difficult to conclude that women respond to apps better than men because the researchers could not randomly assign participants to gender. This means that there may be extraneous variables that are causing the results.

11. Eyewitness Testimony Study

Type: True Experimental Design

To study the how leading questions on the memories of eyewitnesses leads to retroactive inference , Loftus and Palmer (1974) conducted a simple experiment consistent with true experimental design.

Research participants all watched the same short video of two cars having an accident. Each were randomly assigned to be asked either one of two versions of a question regarding the accident.

Half of the participants were asked the question “How fast were the two cars going when they smashed into each other?” and the other half were asked “How fast were the two cars going when they contacted each other?”

Participants’ estimates were affected by the wording of the question. Participants that responded to the question with the word “smashed” gave much higher estimates than participants that responded to the word “contacted.”

12. Sports Nutrition Bars Study

A company wants to test the effects of their sports nutrition bars. So, they recruited students on a college campus to participate in their study. The students were randomly assigned to either the treatment condition or control condition.

Participants in the treatment condition ate two nutrition bars. Participants in the control condition ate two similar looking bars that tasted nearly identical, but offered no nutritional value.

One hour after consuming the bars, participants ran on a treadmill at a moderate pace for 15 minutes. The researchers recorded their speed, breathing rates, and level of exhaustion.

The results indicated that participants that ate the nutrition bars ran faster, breathed more easily, and reported feeling less exhausted than participants that ate the non-nutritious bar.

13. Clinical Trials

Medical researchers often use true experiments to assess the effectiveness of various treatment regimens. For a simplified example: people from the population are randomly selected to participate in a study on the effects of a medication on heart disease.

Participants are randomly assigned to either receive the medication or nothing at all. Three months later, all participants are contacted and they are given a full battery of heart disease tests.

The results indicate that participants that received the medication had significantly lower levels of heart disease than participants that received no medication.

14. Leadership Training Study

A large corporation wants to improve the leadership skills of its mid-level managers. The HR department has developed two programs, one online and the other in-person in small classes.

HR randomly selects 120 employees to participate and then randomly assigned them to one of three conditions: one-third are assigned to the online program, one-third to the in-class version, and one-third are put on a waiting list.

The training lasts for 6 weeks and 4 months later, supervisors of the participants are asked to rate their staff in terms of leadership potential. The supervisors were not informed about which of their staff participated in the program.

The results indicated that the in-person participants received the highest ratings from their supervisors. The online class participants came in second, followed by those on the waiting list.

15. Reading Comprehension and Lighting Study

Different wavelengths of light may affect cognitive processing. To put this hypothesis to the test, a researcher randomly assigned students on a college campus to read a history chapter in one of three lighting conditions: natural sunlight, artificial yellow light, and standard fluorescent light.

At the end of the chapter all students took the same exam. The researcher then compared the scores on the exam for students in each condition. The results revealed that natural sunlight produced the best test scores, followed by yellow light and fluorescent light.

Therefore, the researcher concludes that natural sunlight improves reading comprehension.

See Also: Experimental Study vs Observational Study

Experimental design is a central feature of scientific research. When done using true experimental design, causality can be infered, which allows researchers to provide proof that an independent variable affects a dependent variable. This is necessary in just about every field of research, and especially in medical sciences.

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

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• Experimental Design | Types, Definition & Examples

Experimental Design | Types, Definition & Examples

Published on June 9, 2024 by Julia Merkus, MA . Revised on July 22, 2024.

An experimental design is a systematic plan for conducting an experiment that aims to test a hypothesis or answer a research question.

It involves manipulating one or more independent variables (IVs) and measuring their effect on one or more dependent variables (DVs) while controlling for other variables that could influence the outcome.

The goal of an experimental design is to isolate the effect of the independent variable on the dependent variable while controlling for other variables that could influence the outcome. By doing so, researchers can:

• Test causal relationships between variables
• Identify the effects of specific interventions or treatments
• Make predictions about future outcomes

Your sample needs to be representative to draw valid conclusions from your data. If it’s unethical, hard, or even impossible to randomly assign participants to a control or treatment group, it’s best to use an observational design instead.

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Table of contents

What is an experimental design, step 1: define your research question and variables, step 2: formulate a specific, testable hypothesis, step 3: develop experimental treatments, step 4: divide subjects between treatment and control groups, step 5: decide how to measure your dependent variable, types of experimental design, frequently asked questions about experimental design.

Experimental designs are used to investigate causal relationships by manipulating one or more independent variables and observing their impact on one or more dependent variables.

An experimental design involves a structured approach to testing a hypothesis. A thorough understanding of the study subject is essential for a well-designed experiment.

There are five crucial steps in designing an experiment:

• Define your research question and variables
• Formulate a specific, testable hypothesis
• Develop experimental treatments
• Divide subjects between treatment and control groups
• Decide how to measure your dependent variable

The first step is to formulate the research question for your experimental design.

Experimental design example: Research question

You’re interested in how studying influences test scores. Specifically, you want to know how the number of hours a person studies before a test affects their test score.

In order to formulate a hypothesis, you first need to understand what the independent and dependent variables of your study are.

• The independent variable is the variable you’re going to manipulate. In this case, it’s the number of hours someone studies before a test .
• The dependent variable is the variable you’re going to measure. In this case, it’s the test score .

To create your experimental design, you also need to take into account extraneous variables, such as confounding variables, because you need to control for these in your design.

• An extraneous variable is a variable you’re not interested in studying that can still affect your outcome. You need to control for them to make sure the observed effect is caused by the manipulation of your independent variable. In this case, an extraneous variable could be natural variation in intelligence among individuals.

To visualize your design, you can combine the variables into an experimental design diagram . You use solid and dotted line arrows to indicate possible relationships, as well as plus and minus signs to show the predicted effect.

Your conceptual understanding of the phenomenon you’re studying allows you to formulate a testable hypothesis for your research question.

In most cases, you write a null hypothesis (H0) that predicts no relationship between the independent and dependent variables and an alternate hypothesis (H1 or Ha) that predicts a relationship between the variables.

The number of hours someone studies before a test does not correlate with their test score.

Alternate hypothesis

The next steps describe how to design a controlled experiment, where you must be able to measure the dependent variable(s) precisely, control for any extraneous variables, and systematically and accurately manipulate the independent variable(s).

If your topic or set-up doesn’t allow for this, you should use a different type of research design instead.

The way you manipulate the independent variable(s) of your research affects the external validity (and thus, the generalizability) of the results.

You need to decide how widely and finely you want to vary your independent variable(s).

• You can choose to vary the number of study hours just slightly below or above the average studying time before a test.
• You can choose to vary the number of hours over a wider range to mimic more extreme scenarios.

How finely will you vary your independent variable?

You can treat studying as:

• a categorical variable, where you either allow a binary option (either you study or you don’t) or a variable with levels (no studying, a little, a lot).
• a continuous variable, where every value can occur (number of hours studied).

You can’t always choose how widely or finely you want to vary your independent variable. In some cases, it’s decided for you because of the topic or variables.

It’s important to take into account the sample size of your study. Usually, the experiment’s statistical power increases as the sample size increases, which means you can be more confident about your results. It’s best to use a sample size calculator to calculate the appropriate sample size.

Then, you need to establish which groups you’ll have for your study. An experiment should always contain at least one experimental group and one control group , but it’s possible to have more experimental groups if you have more levels of treatment (e.g., no studying at all, studying a little, studying a lot).

The control group shows us what the results for the experimental group would have been if we hadn’t manipulated the independent variable.

When you assign your participants to groups, you need to make two decisions:

• Completely randomized design vs. randomized block design
• Between-subjects design vs. within-subjects design

Completely randomized design vs randomized block design

An experiment is often completely randomized, but in some cases, you need to randomize within blocks (or strata).

• For example, you use a random number generator to assign all participants to a treatment group
• For example, you first group participants based on gender, and then you randomly assign study treatments within these groups.

When it’s not ethical or practical to use randomization, researchers might opt for a partially-random or non-random design. This is called a quasi-experimental design instead of an experimental design.

Between-subjects design vs within-subjects design

If you opt for a between-subjects design , participants only receive one of the possible levels of an experimental manipulation. This design is also called an independent measures design or a classic ANOVA design.

Subjects can be randomly assigned to one condition, or you can use matched pairs to ensure that each treatment group has the same variety of participants in the same proportions. This often happens in social or medical research.

If you opt for a within-subjects design , all participants experience every condition of the experimental manipulation consecutively, and you measure the outcome of each manipulation. This is also known as a repeated measures design.

For within-subjects designs, you often have to use counterbalancing , which is the act of randomizing the order of manipulations among participants. This way, you make sure the order of manipulations does not affect the findings.

Participants are randomly assigned to a level of studying (none, low, high).

Within-subjects design

For the last step, you have to choose one or multiple data collection methods to measure the outcomes for your dependent variable(s). You need to decide on reliable, valid measurements to reduce the risk of error or bias.

Some variables can be measured with scientific equipment, which typically makes for very reliable outcomes. An example of this is measuring time with a stopwatch. Other variables need to be operationalized before you can measure them.

Your choice of measurement affects the level of your data ( nominal , ordinal , interval, or ratio) and, therefore, the types of statistical analysis you can conduct to analyze your data.

There are three main types of experimental designs:

Between-subjects design

In a between-subjects design , each participant is exposed to only one level of the independent variable. This design is often used in surveys, interviews, and observational studies.

In a within-subjects design , each participant is exposed to multiple levels of the independent variable. This design is often used in laboratory studies, where participants are tested multiple times under different conditions.

Mixed-subjects design

In a mixed-subjects design, a combination of between-subjects and within-subjects designs, where participants are tested under different conditions at multiple points in time and then compared to each other.

Some people also make a distinction between factorial design, randomized controlled trials, and a cross-over design.

Factorial design

In a factorial design , two or more independent variables are manipulated simultaneously. This design is often used in laboratory studies, where researchers want to investigate the interaction between multiple variables.

Randomized controlled trial (RCT)

In a randomized controlled trial (RCT) , a type of between-subjects design that involves randomly assigning participants to treatment or control groups. This design is often used in medical and social science research to test the effectiveness of interventions.

Crossover design

In a crossover design , participants are randomly assigned to receive different levels of the independent variable at different times. This design is often used in clinical trials to test the effectiveness of multiple treatments.

Your choice of design depends on the research question and variables. Often, multiple designs are possible.

The four principles of experimental design are:

• Randomization: This principle involves randomly assigning participants to experimental conditions, ensuring that each participant has an equal chance of being assigned to any condition. Randomization helps to eliminate bias and ensures that the sample is representative of the population.
• Manipulation: This principle involves deliberately manipulating the independent variable to create different conditions or levels. Manipulation allows researchers to test the effect of the independent variable on the dependent variable.
• Control: This principle involves controlling for extraneous or confounding variables that could influence the outcome of the experiment. Control is achieved by holding constant all variables except for the independent variable(s) of interest.
• Replication: This principle involves having built-in replications in your experimental design so that outcomes can be compared. A sufficient number of participants should take part in the experiment to make sure that randomization allows for groups with a similar distribution. This increases the chance of detecting true differences.

In experimental design , the two main groups are:

• Treatment group: This group is exposed to the manipulated independent variable, and the researcher measures the effect of the treatment on the dependent variable.
• Control group: This group is not exposed to the manipulated independent variable (the variable being changed or tested). The control group serves as a reference point to compare the results of the experimental group to.

In other words, the control group is used as a baseline to compare with the treatment group, which receives the experimental treatment or intervention.

• The treatment group , who receives the new medication
• The control group , who receives a placebo

A within-participant design , also known as a repeated-measures design, is a type of experimental design where the same participants are assigned to multiple groups or conditions. Some advantages of this design are:

• Increased statistical power: By using the same participants across multiple conditions, you can reduce the number of participants needed to detect a significant effect, which can lead to increased statistical power.
• Reduced between-participants variability: Since each participant is tested multiple times, the variability between participants is reduced, which can result in more accurate and reliable estimates of the effect.
• Better control over extraneous variables: By using the same participants across multiple conditions, you can better control for extraneous variables that might affect the outcome, as these variables are likely to be constant across conditions.
• Increased precision: Within-participant designs can provide more precise estimates of the effect size, as the same participants are used across all conditions.
• Reduced sample size: Depending on the research question and design, a within-participant design can require fewer participants than a between-participants design, which can reduce costs and increase efficiency.

It’s important to note that within-participant designs also have some limitations, such as increased risk of order effects (where the order of conditions affects the outcome) and carryover effects (where the effects of one condition persist into another condition).

A pre-experimental design is a simple research process that happens before the actual experimental design takes place. The goal is to obtain preliminary results to gauge whether the financial and time investment of a true experiment will be worth it.

The students are asked to participate in a 30-minute meditation session once a week for 4 weeks. The students’ stress levels are measured before and after the meditation sessions with a standardized questionnaire.

Randomization is a crucial component of experimental design , and it’s important for several reasons:

• Prevents bias: Randomization ensures that each participant has an equal chance of being assigned to any condition, minimizing the potential for bias in the assignment process.
• Controls for confounding variables: Randomization helps to distribute confounding variables evenly across conditions, reducing the risk of spurious correlations between the independent variable and the outcome.
• Increases internal validity : By randomly assigning participants to conditions, you can increase the confidence that any observed differences between conditions are due to the independent variable and not some other factor.

An experimental design diagram is a visual representation of the research design, showing the relationships among the variables, conditions, and participants. It helps researchers to:

• Clarify the research question and hypotheses
• Identify the independent, dependent, and control variables
• Determine the experimental conditions and treatment levels
• Plan the sampling and data collection procedures
• Visualize the flow of participants through the study

Is this article helpful?

Julia Merkus, MA

Other students also liked, what is cluster sampling | examples & definition, what is simple random sampling | example & definition, what is stratified sampling | examples & definition.

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Home Market Research

Experimental Research: What it is + Types of designs

Any research conducted under scientifically acceptable conditions uses experimental methods. The success of experimental studies hinges on researchers confirming the change of a variable is based solely on the manipulation of the constant variable. The research should establish a notable cause and effect.

What is Experimental Research?

Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set. Quantitative research methods , for example, are experimental.

If you don’t have enough data to support your decisions, you must first determine the facts. This research gathers the data necessary to help you make better decisions.

You can conduct experimental research in the following situations:

• Time is a vital factor in establishing a relationship between cause and effect.
• Invariable behavior between cause and effect.
• You wish to understand the importance of cause and effect.

Experimental Research Design Types

The classic experimental design definition is: “The methods used to collect data in experimental studies.”

There are three primary types of experimental design:

• Pre-experimental research design
• True experimental research design
• Quasi-experimental research design

The way you classify research subjects based on conditions or groups determines the type of research design  you should use.

0 1. Pre-Experimental Design

A group, or various groups, are kept under observation after implementing cause and effect factors. You’ll conduct this research to understand whether further investigation is necessary for these particular groups.

You can break down pre-experimental research further into three types:

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

0 2. True Experimental Design

It relies on statistical analysis to prove or disprove a hypothesis, making it the most accurate form of research. Of the types of experimental design, only true design can establish a cause-effect relationship within a group. In a true experiment, three factors need to be satisfied:

• There is a Control Group, which won’t be subject to changes, and an Experimental Group, which will experience the changed variables.
• A variable that can be manipulated by the researcher
• Random distribution

This experimental research method commonly occurs in the physical sciences.

0 3. Quasi-Experimental Design

The word “Quasi” indicates similarity. A quasi-experimental design is similar to an experimental one, but it is not the same. The difference between the two is the assignment of a control group. In this research, an independent variable is manipulated, but the participants of a group are not randomly assigned. Quasi-research is used in field settings where random assignment is either irrelevant or not required.

Importance of Experimental Design

Experimental research is a powerful tool for understanding cause-and-effect relationships. It allows us to manipulate variables and observe the effects, which is crucial for understanding how different factors influence the outcome of a study.

But the importance of experimental research goes beyond that. It’s a critical method for many scientific and academic studies. It allows us to test theories, develop new products, and make groundbreaking discoveries.

For example, this research is essential for developing new drugs and medical treatments. Researchers can understand how a new drug works by manipulating dosage and administration variables and identifying potential side effects.

Similarly, experimental research is used in the field of psychology to test theories and understand human behavior. By manipulating variables such as stimuli, researchers can gain insights into how the brain works and identify new treatment options for mental health disorders.

It is also widely used in the field of education. It allows educators to test new teaching methods and identify what works best. By manipulating variables such as class size, teaching style, and curriculum, researchers can understand how students learn and identify new ways to improve educational outcomes.

In addition, experimental research is a powerful tool for businesses and organizations. By manipulating variables such as marketing strategies, product design, and customer service, companies can understand what works best and identify new opportunities for growth.

Advantages of Experimental Research

When talking about this research, we can think of human life. Babies do their own rudimentary experiments (such as putting objects in their mouths) to learn about the world around them, while older children and teens do experiments at school to learn more about science.

Ancient scientists used this research to prove that their hypotheses were correct. For example, Galileo Galilei and Antoine Lavoisier conducted various experiments to discover key concepts in physics and chemistry. The same is true of modern experts, who use this scientific method to see if new drugs are effective, discover treatments for diseases, and create new electronic devices (among others).

It’s vital to test new ideas or theories. Why put time, effort, and funding into something that may not work?

This research allows you to test your idea in a controlled environment before marketing. It also provides the best method to test your theory thanks to the following advantages:

• Researchers have a stronger hold over variables to obtain desired results.
• The subject or industry does not impact the effectiveness of experimental research. Any industry can implement it for research purposes.
• The results are specific.
• After analyzing the results, you can apply your findings to similar ideas or situations.
• You can identify the cause and effect of a hypothesis. Researchers can further analyze this relationship to determine more in-depth ideas.
• Experimental research makes an ideal starting point. The data you collect is a foundation for building more ideas and conducting more action research .

Whether you want to know how the public will react to a new product or if a certain food increases the chance of disease, experimental research is the best place to start. Begin your research by finding subjects using  QuestionPro Audience  and other tools today.

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• What is experimental research: Definition, types & examples

Defne Çobanoğlu

Life and its secrets can only be proven right or wrong with experimentation. You can speculate and theorize all you wish, but as William Blake once said, “ The true method of knowledge is experiment. ”

It may be a long process and time-consuming, but it is rewarding like no other. And there are multiple ways and methods of experimentation that can help shed light on matters. In this article, we explained the definition, types of experimental research, and some experimental research examples . Let us get started with the definition!

• What is experimental research?

Experimental research is the process of carrying out a study conducted with a scientific approach using two or more variables. In other words, it is when you gather two or more variables and compare and test them in controlled environments. 

With experimental research, researchers can also collect detailed information about the participants by doing pre-tests and post-tests to learn even more information about the process. With the result of this type of study, the researcher can make conscious decisions. 

The more control the researcher has over the internal and extraneous variables, the better it is for the results. There may be different circumstances when a balanced experiment is not possible to conduct. That is why are are different research designs to accommodate the needs of researchers.

• 3 Types of experimental research designs

There is more than one dividing point in experimental research designs that differentiates them from one another. These differences are about whether or not there are pre-tests or post-tests done and how the participants are divided into groups. These differences decide which experimental research design is used.

Types of experimental research designs

1 - Pre-experimental design

This is the most basic method of experimental study. The researcher doing pre-experimental research evaluates a group of dependent variables after changing the independent variables . The results of this scientific method are not satisfactory, and future studies are planned accordingly. The pre-experimental research can be divided into three types:

A. One shot case study research design

Only one variable is considered in this one-shot case study design. This research method is conducted in the post-test part of a study, and the aim is to observe the changes in the effect of the independent variable.

B. One group pre-test post-test research design

In this type of research, a single group is given a pre-test before a study is conducted and a post-test after the study is conducted. The aim of this one-group pre-test post-test research design is to combine and compare the data collected during these tests. 

C. Static-group comparison

In a static group comparison, 2 or more groups are included in a study where only a group of participants is subjected to a new treatment and the other group of participants is held static. After the study is done, both groups do a post-test evaluation, and the changes are seen as results.

2 - Quasi-experimental design

This research type is quite similar to the experimental design; however, it changes in a few aspects. Quasi-experimental research is done when experimentation is needed for accurate data, but it is not possible to do one because of some limitations. Because you can not deliberately deprive someone of medical treatment or give someone harm, some experiments are ethically impossible. In this experimentation method, the researcher can only manipulate some variables. There are three types of quasi-experimental design:

A. Nonequivalent group designs

A nonequivalent group design is used when participants can not be divided equally and randomly for ethical reasons. Because of this, different variables will be more than one, unlike true experimental research.

B. Regression discontinuity

In this type of research design, the researcher does not divide a group into two to make a study, instead, they make use of a natural threshold or pre-existing dividing point. Only participants below or above the threshold get the treatment, and as the divide is minimal, the difference would be minimal as well.

C. Natural Experiments

In natural experiments, random or irregular assignment of patients makes up control and study groups. And they exist in natural scenarios. Because of this reason, they do not qualify as true experiments as they are based on observation.

3 - True experimental design

In true experimental research, the variables, groups, and settings should be identical to the textbook definition. Grouping of the participant are divided randomly, and controlled variables are chosen carefully. Every aspect of a true experiment should be carefully designed and acted out. And only the results of a true experiment can really be fully accurate . A true experimental design can be divided into 3 parts:

A. Post-test only control group design

In this experimental design, the participants are divided into two groups randomly. They are called experimental and control groups. Only the experimental group gets the treatment, while the other one does not. After the experiment and observation, both groups are given a post-test, and a conclusion is drawn from the results.

B. Pre-test post-test control group

In this method, the participants are divided into two groups once again. Also, only the experimental group gets the treatment. And this time, they are given both pre-tests and post-tests with multiple research methods. Thanks to these multiple tests, the researchers can make sure the changes in the experimental group are directly related to the treatment.

C. Solomon four-group design

This is the most comprehensive method of experimentation. The participants are randomly divided into 4 groups. These four groups include all possible permutations by including both control and non-control groups and post-test or pre-test and post-test control groups. This method enhances the quality of the data.

• Advantages and disadvantages of experimental research

Just as with any other study, experimental research also has its positive and negative sides. It is up to the researchers to be mindful of these facts before starting their studies. Let us see some advantages and disadvantages of experimental research:

Advantages of experimental research:

• All the variables are in the researchers’ control, and that means the researcher can influence the experiment according to the research question’s requirements.
• As you can easily control the variables in the experiment, you can specify the results as much as possible.
• The results of the study identify a cause-and-effect relation .
• The results can be as specific as the researcher wants.
• The result of an experimental design opens the doors for future related studies.

Disadvantages of experimental research:

• Completing an experiment may take years and even decades, so the results will not be as immediate as some of the other research types.
• As it involves many steps, participants, and researchers, it may be too expensive for some groups.
• The possibility of researchers making mistakes and having a bias is high. It is important to stay impartial
• Human behavior and responses can be difficult to measure unless it is specifically experimental research in psychology.
• Examples of experimental research

When one does experimental research, that experiment can be about anything. As the variables and environments can be controlled by the researcher, it is possible to have experiments about pretty much any subject. It is especially crucial that it gives critical insight into the cause-and-effect relationships of various elements. Now let us see some important examples of experimental research:

An example of experimental research in science:

When scientists make new medicines or come up with a new type of treatment, they have to test those thoroughly to make sure the results will be unanimous and effective for every individual. In order to make sure of this, they can test the medicine on different people or creatures in different dosages and in different frequencies. They can double-check all the results and have crystal clear results.

An example of experimental research in marketing:

The ideal goal of a marketing product, advertisement, or campaign is to attract attention and create positive emotions in the target audience. Marketers can focus on different elements in different campaigns, change the packaging/outline, and have a different approach. Only then can they be sure about the effectiveness of their approaches. Some methods they can work with are A/B testing, online surveys , or focus groups .

• Frequently asked questions about experimental research

Is experimental research qualitative or quantitative?

Experimental research can be both qualitative and quantitative according to the nature of the study. Experimental research is quantitative when it provides numerical and provable data. The experiment is qualitative when it provides researchers with participants' experiences, attitudes, or the context in which the experiment is conducted.

What is the difference between quasi-experimental research and experimental research?

In true experimental research, the participants are divided into groups randomly and evenly so as to have an equal distinction. However, in quasi-experimental research, the participants can not be divided equally for ethical or practical reasons. They are chosen non-randomly or by using a pre-existing threshold.

• Wrapping it up

The experimentation process can be long and time-consuming but highly rewarding as it provides valuable as well as both qualitative and quantitative data. It is a valuable part of research methods and gives insight into the subjects to let people make conscious decisions.

In this article, we have gathered experimental research definition, experimental research types, examples, and pros & cons to work as a guide for your next study. You can also make a successful experiment using pre-test and post-test methods and analyze the findings. For further information on different research types and for all your research information, do not forget to visit our other articles!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

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8.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

• random assignment of participants into experimental and control groups
• a “treatment” (or intervention) provided to the experimental group
• measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963).  The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level research

You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals.  For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change.  There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).  Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments.  For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

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exam scientific experiment by mohamed_hassan CC-0

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Going beyond the comparison: toward experimental instructional design research with impact

• Methodology
• Published: 28 August 2024

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• Adam G. Gavarkovs 1 ,
• Rashmi A. Kusurkar 2 , 3 , 4 ,
• Kulamakan Kulasegaram 5 , 6 &
• Ryan Brydges 6 , 7  

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To design effective instruction, educators need to know what design strategies are generally effective and why these strategies work, based on the mechanisms through which they operate. Experimental comparison studies, which compare one instructional design against another, can generate much needed evidence in support of effective design strategies. However, experimental comparison studies are often not equipped to generate evidence regarding the mechanisms through which strategies operate. Therefore, simply conducting experimental comparison studies may not provide educators with all the information they need to design more effective instruction. To generate evidence for the what and the why of design strategies, we advocate for researchers to conduct experimental comparison studies that include mediation or moderation analyses, which can illuminate the mechanisms through which design strategies operate. The purpose of this article is to provide a conceptual overview of mediation and moderation analyses for researchers who conduct experimental comparison studies in instructional design. While these statistical techniques add complexity to study design and analysis, they hold great promise for providing educators with more powerful information upon which to base their instructional design decisions. Using two real-world examples from our own work, we describe the structure of mediation and moderation analyses, emphasizing the need to control for confounding even in the context of experimental studies. We also discuss the importance of using learning theories to help identify mediating or moderating variables to test.

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Research-Based Instructional Perspectives

To prove or improve, that is the question: the resurgence of comparative, confounded research between 2010 and 2019

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As an alternative to the regression approach, structural equation modelling (SEM) has gained popularity in the health professions education literature (Stoffels et al., 2023 ). SEM requires that a researcher make additional assumptions regarding the functional relationships between the covariates, the mediator(s), and the outcome(s) (VanderWeele, 2012 ). Though specifying these relationships can increase power, it comes with an increased risk of model misspecification (VanderWeele, 2012 ). Accordingly, we recommend that researchers beginning with experimental comparison studies involving a single mediator opt for using the regression-based approach with controls for mediator-outcome confounding (VanderWeele, 2012 ).

We did not actually analyze our data in the manner described below, for reasons described in our published manuscript. Here, we describe an alternative data analysis strategy for clarity.

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Adam G. Gavarkovs

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Gavarkovs, A.G., Kusurkar, R.A., Kulasegaram, K. et al. Going beyond the comparison: toward experimental instructional design research with impact. Adv in Health Sci Educ (2024). https://doi.org/10.1007/s10459-024-10365-9

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DOI : https://doi.org/10.1007/s10459-024-10365-9

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Herbal companion crops as an example of implementation of sustainable plant protection practices in soybean cultivation.

1. Introduction

2. materials and methods, 2.1. study location, 2.2. experimental design, 2.3. biometric analysis, 2.4. yield quality, 2.5. assessment of the incidence of cercospora leaf blight, fusarium wilt, ascochyta blight, and downy mildew (peronospora manshurica) in soybean plants, 2.6. statistical analysis, 3.1. weather conditions, 3.2. biometric traits, 3.3. seed yield and yield quality, 3.4. pathogen suppression, 4. discussion, 4.1. companion crop and seed yield, 4.2. companion crops and soybean health, 5. conclusions, author contributions, institutional review board statement, data availability statement, conflicts of interest.

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Sikora, A.; Dłużniewska, J.; Kulig, B.; Klimek-Kopyra, A. Herbal Companion Crops as an Example of Implementation of Sustainable Plant Protection Practices in Soybean Cultivation. Agriculture 2024 , 14 , 1485. https://doi.org/10.3390/agriculture14091485

Sikora A, Dłużniewska J, Kulig B, Klimek-Kopyra A. Herbal Companion Crops as an Example of Implementation of Sustainable Plant Protection Practices in Soybean Cultivation. Agriculture . 2024; 14(9):1485. https://doi.org/10.3390/agriculture14091485

Sikora, Adrian, Joanna Dłużniewska, Bogdan Kulig, and Agnieszka Klimek-Kopyra. 2024. "Herbal Companion Crops as an Example of Implementation of Sustainable Plant Protection Practices in Soybean Cultivation" Agriculture 14, no. 9: 1485. https://doi.org/10.3390/agriculture14091485

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• Quasi-Experimental Design | Definition, Types & Examples

Quasi-Experimental Design | Definition, Types & Examples

Published on July 31, 2020 by Lauren Thomas . Revised on January 22, 2024.

Like a true experiment , a quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable .

However, unlike a true experiment, a quasi-experiment does not rely on random assignment . Instead, subjects are assigned to groups based on non-random criteria.

Quasi-experimental design is a useful tool in situations where true experiments cannot be used for ethical or practical reasons.

Table of contents

Differences between quasi-experiments and true experiments, types of quasi-experimental designs, when to use quasi-experimental design, advantages and disadvantages, other interesting articles, frequently asked questions about quasi-experimental designs.

There are several common differences between true and quasi-experimental designs.

Example of a true experiment vs a quasi-experiment

However, for ethical reasons, the directors of the mental health clinic may not give you permission to randomly assign their patients to treatments. In this case, you cannot run a true experiment.

Instead, you can use a quasi-experimental design.

You can use these pre-existing groups to study the symptom progression of the patients treated with the new therapy versus those receiving the standard course of treatment.

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Many types of quasi-experimental designs exist. Here we explain three of the most common types: nonequivalent groups design, regression discontinuity, and natural experiments.

Nonequivalent groups design

In nonequivalent group design, the researcher chooses existing groups that appear similar, but where only one of the groups experiences the treatment.

In a true experiment with random assignment , the control and treatment groups are considered equivalent in every way other than the treatment. But in a quasi-experiment where the groups are not random, they may differ in other ways—they are nonequivalent groups .

When using this kind of design, researchers try to account for any confounding variables by controlling for them in their analysis or by choosing groups that are as similar as possible.

This is the most common type of quasi-experimental design.

Regression discontinuity

Many potential treatments that researchers wish to study are designed around an essentially arbitrary cutoff, where those above the threshold receive the treatment and those below it do not.

Near this threshold, the differences between the two groups are often so minimal as to be nearly nonexistent. Therefore, researchers can use individuals just below the threshold as a control group and those just above as a treatment group.

However, since the exact cutoff score is arbitrary, the students near the threshold—those who just barely pass the exam and those who fail by a very small margin—tend to be very similar, with the small differences in their scores mostly due to random chance. You can therefore conclude that any outcome differences must come from the school they attended.

Natural experiments

In both laboratory and field experiments, researchers normally control which group the subjects are assigned to. In a natural experiment, an external event or situation (“nature”) results in the random or random-like assignment of subjects to the treatment group.

Even though some use random assignments, natural experiments are not considered to be true experiments because they are observational in nature.

Although the researchers have no control over the independent variable , they can exploit this event after the fact to study the effect of the treatment.

However, as they could not afford to cover everyone who they deemed eligible for the program, they instead allocated spots in the program based on a random lottery.

Although true experiments have higher internal validity , you might choose to use a quasi-experimental design for ethical or practical reasons.

Sometimes it would be unethical to provide or withhold a treatment on a random basis, so a true experiment is not feasible. In this case, a quasi-experiment can allow you to study the same causal relationship without the ethical issues.

The Oregon Health Study is a good example. It would be unethical to randomly provide some people with health insurance but purposely prevent others from receiving it solely for the purposes of research.

However, since the Oregon government faced financial constraints and decided to provide health insurance via lottery, studying this event after the fact is a much more ethical approach to studying the same problem.

True experimental design may be infeasible to implement or simply too expensive, particularly for researchers without access to large funding streams.

At other times, too much work is involved in recruiting and properly designing an experimental intervention for an adequate number of subjects to justify a true experiment.

In either case, quasi-experimental designs allow you to study the question by taking advantage of data that has previously been paid for or collected by others (often the government).

Quasi-experimental designs have various pros and cons compared to other types of studies.

• Higher external validity than most true experiments, because they often involve real-world interventions instead of artificial laboratory settings.
• Higher internal validity than other non-experimental types of research, because they allow you to better control for confounding variables than other types of studies do.
• Lower internal validity than true experiments—without randomization, it can be difficult to verify that all confounding variables have been accounted for.
• The use of retrospective data that has already been collected for other purposes can be inaccurate, incomplete or difficult to access.

Prevent plagiarism. Run a free check.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

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• Open access
• Published: 31 August 2024

Effects of pecha kucha presentation pedagogy on nursing students’ presentation skills: a quasi-experimental study in Tanzania

• Setberth Jonas Haramba 1 ,
• Walter C. Millanzi 1 &
• Saada A. Seif 2  

BMC Medical Education volume  24 , Article number:  952 ( 2024 ) Cite this article

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Introduction

Ineffective and non-interactive learning among nursing students limits opportunities for students’ classroom presentation skills, creativity, and innovation upon completion of their classroom learning activities. Pecha Kucha presentation is the new promising pedagogy that engages students in learning and improves students’ speaking skills and other survival skills. It involves the use of 20 slides, each covering 20 seconds of its presentation. The current study examined the effect of Pecha Kucha’s presentation pedagogy on presentation skills among nursing students in Tanzania.

The aim of this study was to establish comparative nursing student’s presentation skills between exposure to the traditional PowerPoint presentations and Pecha Kucha presentations.

The study employed an uncontrolled quasi-experimental design (pre-post) using a quantitative research approach among 230 randomly selected nursing students at the respective training institution. An interviewer-administered structured questionnaire adopted from previous studies to measure presentation skills between June and July 2023 was used. The study involved the training of research assistants, pre-assessment of presentation skills, training of participants, assigning topics to participants, classroom presentations, and post-intervention assessment. A linear regression analysis model was used to determine the effect of the intervention on nursing students’ presentation skills using Statistical Package for Social Solution (SPSS) version 26, set at a 95% confidence interval and 5% significance level.

Findings revealed that 63 (70.87%) participants were aged ≤ 23 years, of which 151 (65.65%) and 189 (82.17%) of them were males and undergraduate students, respectively. Post-test findings showed a significant mean score change in participants’ presentation skills between baseline (M = 4.07 ± SD = 0.56) and end-line (M = 4.54 ± SD = 0.59) that accounted for 0.4717 ± 0.7793; p  < .0001(95%CI) presentation skills mean score change with a medium effect size of 0.78. An increase in participants’ knowledge of Pecha Kucha presentation was associated with a 0.0239 ( p  < .0001) increase in presentation skills.

Pecha Kucha presentations have a significant effect on nursing students’ presentation skills as they enhance inquiry and mastery of their learning content before classroom presentations. The pedagogical approach appeared to enhance nursing students’ confidence during the classroom presentation. Therefore, there is a need to incorporate Pecha Kucha presentation pedagogy into nursing curricula and nursing education at large to promote student-centered teaching and learning activities and the development of survival skills.

Trial registration

It was not applicable as it was a quasi-experimental study.

Peer Review reports

The nursing students need to have different skills acquired during the learning process in order to enable them to provide quality nursing care and management in the society [ 1 ]. The referred nursing care and management practices include identifying, analyzing, synthesizing, and effective communication within and between healthcare professionals [ 1 ]. Given an increasing global economy and international competition for jobs and opportunities, the current traditional classroom learning methods are insufficient to meet such 21st - century challenges and demands [ 2 ]. The integration of presentation skills, creativity, innovation, collaboration, information, and media literacy skills helps to overcome the noted challenges among students [ 2 , 3 , 4 ]. The skills in question constitute the survival skills that help the students not only for career development and success but also for their personal, social and public quality of life as they enable students to overcome 21st challenges upon graduation [ 2 ].

To enhance the nursing students’ participation in learning, stimulating their presentation skills, critical thinking, creativity, and innovation, a combination of teaching and learning pedagogy should be employed [ 5 , 6 , 7 , 8 ]. Among others, classroom presentations, group discussions, problem-based learning, demonstrations, reflection, and role-play are commonly used for those purposes [ 5 ]. However, ineffective and non-interactive learning which contribute to limited presentation skills, creativity, and innovation, have been reported by several scholars [ 9 , 10 , 11 ]. For example, poor use and design of student PowerPoint presentations led to confusing graphics due to the many texts in the slides and the reading of about 80 slides [ 12 , 13 , 14 ]. Indeed, such non-interactive learning becomes boring and tiresome among the learners, and it is usually evidenced by glazing eyes, long yawning, occasional snoring, the use of a phone and frequent trips to the bathroom [ 12 , 14 ].

With an increasing number of nursing students in higher education institutions in Tanzania, the students’ traditional presentation pedagogy is insufficient to stimulate their presentation skills. They limit nursing student innovation, creativity, critical thinking, and meaningful learning in an attempt to solve health challenges [ 15 , 16 ].These hinder nursing students ability to communicate effectively by being able to demonstrate their knowledge and mastery of learning content [ 17 , 18 ]. Furthermore, it affects their future careers by not being able to demonstrate and express their expertise clearly in a variety of workplace settings, such as being able to present at scientific conferences, participating in job interviews, giving clinic case reports, handover reports, and giving feedback to clients [ 17 , 18 , 19 ].

Pecha Kucha presentation is a new promising approach for students’ learning in the classroom context as it motivates learners’ self-directed and collaborative learning, learner creativity, and presentation skills [ 20 , 21 , 22 ]. It encourages students to read more materials, enhances cooperative learning among learners, and is interesting and enjoyable among students [ 23 ].

Pecha Kucha presentation originated from the Japanese word “ chit chat , ” which represents the fast-paced presentation used in different fields, including teaching, marketing, advertising, and designing [ 24 , 25 , 26 ]. It involves 20 slides, where each slide covers 20 s, thus making a total of 6 min and 40 s for the whole presentation [ 22 ]. For effective learning through Pecha Kucha presentations, the design and format of the presentation should be meaningfully limited to 20 slides and targeted at 20 s for each slide, rich in content of the presented topic using high-quality images or pictures attuned to the content knowledge and message to be delivered to the target audiences [ 14 , 16 ]. Each slide should contain a primordial message with well-balanced information. In other words, the message should be simple in the sense that each slide should contain only one concept or idea with neither too much nor too little information, thus making it easy to be grasped by the audience [ 14 , 17 , 19 ].

The “true spirit” of Pecha Kucha is that it mostly consists of powerful images and meaningful specific text rather than the text that is being read by the presenter from the slides, an image, and short phrases that should communicate the core idea while the speaker offers well-rehearsed and elaborated comments [ 22 , 28 ]. The presenter should master the subject matter and incorporate the necessary information from classwork [ 14 , 20 ]. The audience’s engagement in learning by paying attention and actively listening to the Pecha Kucha presentation was higher compared with that in traditional PowerPoint presentations [ 29 ]. The creativity and collaboration during designing and selecting the appropriate images and contents, rehearsal before the presentation, and discussion after each presentation made students satisfied by enjoying Pecha Kucha presentations compared with traditional presentations [ 21 , 22 ]. Time management and students’ self-regulation were found to be significant through the Pecha Kucha presentation among the students and teachers or instructors who could appropriately plan the time for classroom instruction [ 22 , 23 ].

However, little is known about Pecha Kucha presentation in nursing education in Sub-Saharan African countries, including Tanzania, since there is insufficient evidence for the research(s) that have been published on the description of its effects on enhancing students’ presentation skills. Thus, this study assessed the effect of Pecha Kucha’s presentation pedagogy on enhancing presentation skills among nursing students. In particular, the study largely focused on nursing students’ presentation skills during the preparation and presentation of the students’ assignments, project works, case reports, or field reports.

The study answered the null hypothesis H 0  = H 1, which hypothesized that there is no significant difference in nursing students’ classroom presentation skills scores between the baseline and end-line assessments. The association between nursing students’ presentation skills and participants’ sociodemographic characteristics was formulated and analyzed before and after the intervention. This study forms the basis for developing new presentation pedagogy among nursing students in order to stimulate effective learning and the development of presentation skills during the teaching and learning process and the acquisition of 21st - century skills, which are characterized by an increased competitive knowledge-based society due to changing nature and technological eruptions.

The current study also forms the basis for re-defining classroom practices in an attempt to enhance and transform nursing students’ learning experiences. This will cultivate the production of graduates nurses who will share their expertise and practical skills in the health care team by attending scientific conferences, clinical case presentations, and job interviews in the global health market. To achieve this, the study determined the baseline and end-line nursing students’ presentation skills during the preparation and presentation of classroom assignments using the traditional PowerPoint presentation and Pecha Kucha presentation format.

Methods and materials

This study was conducted in health training institutions in Tanzania. Tanzania has a total of 47 registered public and private universities and university colleges that offer health programs ranging from certificate to doctorate degrees [ 24 , 25 ]. A total of seven [ 7 ] out of 47 universities offer a bachelor of science in nursing, and four [ 4 ] universities offer master’s to doctorate degree programs in nursing and midwifery sciences [ 24 , 26 ]. To enhance the representation of nursing students in Tanzania, this study was conducted in Dodoma Municipal Council, which is one of Tanzania’s 30 administrative regions [ 33 ]. Dodoma Region has two [ 2 ] universities that offer nursing programs at diploma and degree levels [ 34 ]. The referred universities host a large number of nursing students compared to the other five [ 5 ] universities in Tanzania, with traditional students’ presentation approaches predominating nursing students’ teaching and learning processes [ 7 , 32 , 35 ].

The two universities under study include the University of Dodoma and St. John’s University of Tanzania, which are located in Dodoma Urban District. The University of Dodoma is a public university that provides 142 training programs at the diploma, bachelor degree, and master’s degree levels with about 28,225 undergraduate students and 724 postgraduate students [ 26 , 27 ]. The University of Dodoma also has 1,031 nursing students pursuing a Bachelor of Science in Nursing and 335 nursing students pursuing a Diploma in Nursing in the academic year 2022–2023 [ 33 ]. The St. John’s University of Tanzania is a non-profit private university that is legally connected with the Christian-Anglican Church [ 36 ]. It has student enrollment ranging from 5000 to 5999 and it provides training programs leading to higher education degrees in a variety of fields, including diplomas, bachelor degrees, and master’s degrees [ 37 ]. It hosts 766 nursing students pursuing a Bachelor of Science in Nursing and 113 nursing students pursuing a Diploma in Nursing in the academic year 2022–2023 [ 30 , 31 ].

Study design and approach

An uncontrolled quasi-experimental design with a quantitative research approach was used to establish quantifiable data on the participants’ socio-demographic profiles and outcome variables under study. The design involved pre- and post-tests to determine the effects of the intervention on the aforementioned outcome variable. The design involved three phases, namely the baseline data collection process (pre-test via a cross-sectional survey), implementation of the intervention (process), and end-line assessment (post-test), as shown in Fig.  1 [ 7 ].

A flow pattern of study design and approach

Target population

The study involved nursing students pursuing a Diploma in nursing and a bachelor of science in nursing in Tanzania. The population was highly expected to demonstrate competences and mastery of different survival and life skills in order to enable them to work independent at various levels of health facilities within and outside Tanzania. This cohort of undergraduate nursing students also involved adult learners who can set goals, develop strategies to achieve their goals, and hence achieve positive professional behavioral outcomes [ 7 ]. Moreover, as per annual data, the average number of graduate nursing students ranges from 3,500 to 4,000 from all colleges and universities in the country [ 38 ].

Study population

The study involved first- and third-year nursing students pursuing a Diploma in Nursing and first-, second-, and third-year nursing students pursuing a Bachelor of Science in Nursing at the University of Dodoma. The population had a large number of enrolled undergraduate nursing students, thus making it an ideal population for intervention, and it approximately served as a good representation of the universities offering nursing programs [ 11 , 29 ].

Inclusion criteria

The study included male and female nursing students pursuing a Diploma in nursing and a bachelor of science in nursing at the University of Dodoma. The referred students included those who were registered at the University of Dodoma during the time of study. Such students live on or off campus, and they were not exposed to PK training despite having regular classroom attendance. This enhanced enrollment of adequate study samples from each study program, monitoring of study intervention, and easy control of con-founders.

Exclusion criteria

All students recruited in the study were assessed at baseline, exposed to a training package and obtained their post-intervention learning experience. None of the study participants, who either dropped out of the study or failed to meet the recruitment criteria.

Sample size determination

A quasi-experimental study on Pecha Kucha as an alternative to traditional PowerPoint presentations at Worcester University, United States of America, reported significant student engagement during Pecha Kucha presentations compared with traditional PowerPoint presentations [ 29 ]. The mean score for the classroom with the traditional PowerPoint presentation was 2.63, while the mean score for the Pecha Kucha presentation was 4.08. This study adopted the formula that was used to calculate the required sample size for an uncontrolled quasi-experimental study among pre-scholars [ 39 ]. The formula is stated as:

Where: Zα was set at 1.96 from the normal distribution table.

Zβ was set at 0.80 power of the study.

Mean zero (π0) was the mean score of audiences’ engagement in using PowerPoint presentation = 2.63.

Mean one (π1) was the mean score of audience’s engagement in using Pecha Kucha presentation = 4.08.

Sampling technique

Given the availability of higher-training institutions in the study area that offer undergraduate nursing programs, a simple random sampling technique was used, whereby two cards, one labelled “University of Dodoma” and the other being labelled “St. Johns University of Tanzania,” were prepared and put in the first pot. The other two cards, one labelled “yes” to represent the study setting and the other being labelled “No” to represent the absence of study setting, were put in the second pot. Two research assistants were asked to select a card from each pot, and consequently, the University of Dodoma was selected as the study setting.

To obtain the target population, the study employed purposive sampling techniques to select the school of nursing and public health at the University of Dodoma. Upon arriving at the School of Nursing and Public Health of the University of Dodoma, the convenience sampling technique was employed to obtain the number of classes for undergraduate nursing students pursuing a Diploma in Nursing and a Bachelor of Science in Nursing. The study sample comprised the students who were available at the time of study. A total of five [ 5 ] classes of Diploma in Nursing first-, second-, and third-years and Bachelor of Science in Nursing first-, second-, and third-years were obtained.

To establish the representation for a minimum sample from each class, the number of students by sex was obtained from each classroom list using the proportionate stratified sampling technique (sample size/population size× stratum size) as recommended by scholars [ 40 ]. To recruit the required sample size from each class by gender, a simple random sampling technique through the lottery method was employed to obtain the required sample size from each stratum. During this phase, the student lists by gender from each class were obtained, and cards with code numbers, which were mixed with empty cards depending on the strata size, were allocated for each class and strata. Both labeled and empty cards were put into different pots, which were labeled appropriately by their class and strata names. Upon arriving at the specific classroom and after the introduction, the research assistant asked each nursing student to pick one card from the respective strata pot. Those who selected cards with code numbers were recruited in the study with their code numbers as their participation identity numbers. The process continued for each class until the required sample size was obtained.

To ensure the effective participation of nursing students in the study, the research assistant worked hand in hand with the facilitators and lecturers of the respective classrooms, the head of the department, and class representatives. The importance, advantages, and disadvantages of participating in the study were given to study participants during the recruitment process in order to create awareness and remove possible fears. During the intervention, study participants were also given pens and notebooks in an attempt to enable them to take notes. Moreover, the bites were provided during the training sessions. The number of participants from each classroom and the sampling process are shown in Fig.  2 [ 7 ].

Flow pattern of participants sampling procedures

Data collection tools

The study adapted and modified the students’ questionnaire on presentation skills from scholars [ 20 , 23 , 26 , 27 , 28 , 29 ]. The modification involved rephrasing the question statement, breaking down items into specific questions, deleting repeated items that were found to measure the same variables, and improving language to meet the literacy level and cultural norms of study participants.

The data collection tool consisted of 68 question items that assessed the socio-demographic characteristics of the study participants and 33 question items rated on a five-point Likert scale, which ranges from 5 = strongly agree, 4 = agree, 3 = not sure, 2 = disagree, and 1 = strongly disagree. The referred tool was used to assess the students’ skills during the preparation and presentation of the assignments using the traditional PowerPoint presentation and Pecha Kucha presentation formats.

The students’ assessment specifically focused on the students’ ability to prepare the presentation content, master the learning content, share presentation materials, and communicate their understanding to audiences in the classroom context.

Validity and reliability of research instruments

Validity of the research instrument refers to whether the instrument measures the behaviors or qualities that are intended to be measured, and it is a measure of how well the measuring instrument performs its function [ 41 ]. The structured questionnaire, which intends to assess the participants’ presentation skills was validated for face and content validity. The principal investigator initially adapted the question items for different domains of students’ learning when preparing and presenting their assignment in the classroom.

The items were shared and discussed by two [ 2 ] educationists, two [ 2 ] research experts, one [ 1 ] statistician, and supervisors in order to ensure clarity, appropriateness, adequacy, and coverage of the presentation skills using Pecha Kucha presentation format. The content validity test was used until the saturation of experts’ opinions and inputs was achieved. The inter-observer rating scale on a five-point Likert scale ranging from 5-points = very relevant to 1-point = not relevant was also used.

The process involved addition, input deletion, correction, and editing for relevance, appropriateness, and scope of the content for the study participants. Some of the question items were broken down into more specific questions, and new domains evolved. Other question items that were found to measure the same variables were also deleted to ease the data collection and analysis. Moreover, the grammar and language issues were improved for clarity based on the literacy level of the study participants.

Reliability of the research instruments refers to the ability of the research instruments or tools to provide similar and consistent results when applied at different times and circumstances [ 41 ]. This study adapted the tools and question items used by different scholars to assess the impact of PKP on student learning [ 12 , 15 , 18 ].

To ensure the reliability of the tools, a pilot study was conducted in one of the nursing training institutions in order to assess the complexity, readability, clarity, completeness, length, and duration of the tool. Ambiguous and difficult (left unanswered) items were modified or deleted based on the consensus that was reached with the consulted experts and supervisor before subjecting the questionnaires to a pre-test.

The study involved 10% of undergraduate nursing students from an independent geographical location for a pilot study. The findings from the pilot study were subjected to explanatory factor analysis (Set a ≥ 0.3) and scale analysis in order to determine the internal consistency of the tools using the Cronbach alpha of ≥ 0.7, which was considered reliable [ 42 , 43 , 44 ]. Furthermore, after the data collection, the scale analysis was computed in an attempt to assess their internal consistency using SPPSS version 26, whereby the Cronbach alpha for question items that assessed the participants’ presentation skills was 0.965.

Data collection method

The study used the researcher-administered questionnaire to collect the participants’ socio-demographic information, co-related factors, and presentation skills as nursing students prepare and present their assignments in the classroom. This enhanced the clarity and participants’ understanding of all question items before providing the appropriate responses. The data were collected by the research assistants in the classroom with the study participants sitting distantly to ensure privacy, confidentiality, and the quality of the information that was provided by the research participants. The research assistant guided and led the study participants to answer the questions and fill in information in the questionnaire for each section, domain, and question item. The research assistant also collected the baseline information (pre-test) before the intervention, which was then compared with the post-intervention information. This was done in the first week of June 2023, after training and orientation of the research assistant on the data collection tools and recruitment of the study participants.

Using the researcher-administered questionnaire, the research assistant also collected the participants’ information related to presentation skills as they prepared and presented their given assignments after the intervention during the second week of July 2023. The participants submitted their presentations to the principle investigator and research assistant to assess the organization, visual appeal and creativity, content knowledge, and adherence to Pecha Kucha presentation requirements. Furthermore, the evaluation of the participants’ ability to share and communicate the given assignment was observed in the classroom presentation using the Pecha Kucha presentation format.

Definitions of variables

Pecha kucha presentation.

It refers to a specific style of presentation whereby the presenter delivers the content using 20 slides that are dominated by images, pictures, tables, or figures. Each slide is displayed for 20 s, thus making a total of 400 s (6 min and 40 s) for the whole presentation.

Presentation skills in this study

This involved students’ ability to plan, prepare, master learning content, create presentation materials, and share them with peers or the audience in the classroom. They constitute the learning activities that stimulate creativity, innovation, critical thinking, and problem-solving skills.

Measurement of pecha kucha preparation and presentation skills

The students’ presentation skills were measured using the four [ 4 ] learning domains. The first domain constituted the students’ ability to plan and prepare the presentation content. It consisted of 17 question items that assessed the students’ ability to gather and select information, search for specific content to be presented in the classroom, find out the learning content from different resources, and search for literature materials for the preparation of the assignment using traditional PowerPoint presentations and Pecha Kucha formats. It also aimed to ascertain a deeper understanding of the contents or topic, learning ownership and motivation to learn the topics with clear understanding and the ability to identify the relevant audience, segregate, and remove unnecessary contents using the Pecha Kucha format.

The second domain constituted the students’ mastery of learning during the preparation and presentation of their assignment before the audience in the classroom. It consisted of six [ 6 ] question items that measured the students’ ability to read several times, rehearse before the classroom presentation, and practice the assignment and presentation harder. It also measures the students’ ability to evaluate the selected information and content before their actual presentation and make revisions to the selected information and content before the presentation using the Pecha Kucha format.

The third domain constituted the students’ ability to prepare the presentation materials. It consisted of six [ 6 ] question items that measured the students’ ability to organize the information and contents, prepare the classroom presentation, revise and edit presentation resources, materials, and contents, and think about the audience and classroom design. The fourth domain constituted the students’ ability to share their learning. It consisted of four [ 4 ] question items that measured the students’ ability to communicate their learning with the audience, present a new understanding to the audience, transfer the learning to the audience, and answer the questions about the topic or assignment given. The variable was measured using a 5-point Likert scale. The average scores were computed for each domain, and an overall mean score was calculated across all domains. Additionally, an encompassing skills score was derived from the cumulative scores of all four domains, thus providing a comprehensive evaluation of the overall skills level.

Implementation of intervention

The implementation of the study involved the training of research assistants, sampling of the study participants, setting of the venue, pre-assessment of the students’ presentation skills using traditional PowerPoint presentations, training and demonstration of Pecha Kucha presentations to study participants, and assigning the topics to study participants. The implementation of the study also involved the participants’ submission of their assignments to the Principal Investigator for evaluation, the participants’ presentation of their assigned topic using the Pecha Kucha format, post-intervention assessment of the students’ presentation skills, data analysis, and reporting [ 7 ]. The intervention involved Principal Investigator and two [ 2 ] trained research assistants. The intervention in question was based on the concept of multimedia theory of cognitive learning (MTCL) for enhancing effective leaning in 21st century.

Training of research assistants

Two research assistants were trained with regard to the principles, characteristics, and format of Pecha Kucha presentations using the curriculum from the official Pecha Kucha website. Also, research assistants were oriented to the data collection tools and methods in an attempt to guarantee the relevancy and appropriate collection of the participants’ information.

Schedule and duration of training among research assistants

The PI prepared the training schedule and venue after negotiation and consensus with the research assistants. Moreover, the Principle Investigator trained the research assistants to assess the learning, learn how to collect the data using the questionnaire, and maintain the privacy and confidentiality of the study participants.

Descriptions of interventions

The intervention was conducted among the nursing students at the University of Dodoma, which is located in Dodoma Region, Tanzania Mainland, after obtaining their consent. The participants were trained regarding the concepts, principles, and characteristics of Pecha Kucha presentations and how to prepare and present their assignments using the Pecha Kucha presentation format. The study participants were also trained regarding the advantages and disadvantages of Pecha Kucha presentations. The training was accompanied by one example of an ideal Pecha Kucha presentation on the concepts of pressure ulcers. The teaching methods included lecturing, brainstorming, and small group discussion. After the training session, the evaluation was conducted to assess the participants’ understanding of the Pecha Kucha conceptualization, its characteristics, and its principles.

Each participant was given a topic as an assignment from the fundamentals of nursing, medical nursing, surgical nursing, community health nursing, mental health nursing, emergency critical care, pediatric, reproductive, and child health, midwifery, communicable diseases, non-communicable diseases, orthopedics and cross-cutting issues in nursing as recommended by scholars [ 21 , 38 ]. The study participants were given 14 days for preparation, rehearsal of their presentation using the Pecha Kucha presentation format, and submission of the prepared slides to the research assistant and principle investigator for evaluation and arrangement before the actual classroom presentation. The evaluation of the participants’ assignments involved the number of slides, quality of images used, number of words, organization of content and messages to be delivered, slide transition, duration of presentation, flow, and organization of slides.

Afterwards, each participant was given 6 min and 40 s for the presentation and 5 min to 10 min for answering the questions on the topic presented as raised by other participants. An average of 4 participants obtained the opportunity to present their assignments in the classroom every hour. After the completion of all presentations, the research assistants assessed the participant’s presentation skills using the researcher-administered questionnaire. The collected data were entered in SPSS version 26 and analyzed in an attempt to compare the mean score of participants’ presentation skills with the baseline mean score. The intervention sessions were conducted in the selected classrooms, which were able to accommodate all participants at the time that was arranged by the participant’s coordinators, institution administrators, and subject facilitators of the University of Dodoma, as described in Table  1 [ 7 ].

Evaluation of intervention

During the classroom presentation, there were 5 to 10 min for classroom discussion and reflection on the content presented, which was guided by the research assistant. During this time, the participants were given the opportunity to ask the questions, get clarification from the presenter, and provide their opinion on how the instructional messages were presented, content coverage, areas of strength and weakness for improvement, and academic growth. After the completion of the presentation sessions, the research assistant provided the questionnaire to participants in order to determine their presentation skills during the preparation of their assignments and classroom presentations using the Pecha Kucha presentation format.

Data analysis

The findings from this study were analyzed using the Statistical Package for Social Science (SPSS) computer software program version 26. The percentages, frequencies, frequency distributions, means, standard deviations, skewness, and kurtosis were calculated, and the results were presented using the figures, tables, and graphs. The mean score analysis was computed, and descriptive statistical analysis was used to analyze the demographic information of the participants in an attempt to determine the frequencies, percentages, and mean scores of their distributions. A paired sample t-test was used to compare the mean score differences of the presentation skills within the groups before and after the intervention. The mean score differences were determined based on the baseline scores against the post-intervention scores in order to establish any change in terms of presentation skills among the study participants.

The association between the Pecha Kucha presentation and the development of participants’ presentation skills was established using linear regression analysis set at a 95% confidence interval and 5% (≤ 0.05) significance level in an attempt to accept or reject the null hypothesis.

However, N-1 dummy variables were formed for the categorical independent variables so as to run the linear regression for the factors associated with the presentation skills. The linear regression equation with dummy variables is presented as follows:

Β 0 is the intercept.

Β 1 , Β 2 , …. Β k-1 are the coefficients which correspond to the dummy variables representing the levels of X 1 .

Β k is the coefficient which corresponds to the dummy variable representing the levels of X 2 .

Β k+1 is the coefficient which corresponds to the continuous predictor X 3 .

X 1,1 , X 1,2 ,……. X 1,k-1 are the dummy variables corresponding to the different levels of X 1 .

ε represents the error term.

The coefficients B1, B2… Bk indicate the change in the expected value of Y for each category relative to the reference category. If the Beta estimate is positive for the categorical or dummy variables, it means that the corresponding covariate has a positive impact on the outcome variable compared to reference category. However, if the beta estimate is positive for the case of continuous covariates, it means that the corresponding covariate has direct proportion effect on the outcome variables.

The distribution of the outcome variables was approximately normally distributed since the normality of the data is one of the requirements for parametric analysis. A paired t test was performed to compare the presentation skills of nursing students before and after the intervention.

Social-demographic characteristics of the study participants

The study involved a total of 230 nursing students, of whom 151 (65.65%) were male and the rest were female. The mean age of study participants was 23.03 ± 2.69, with the minimum age being 19 and the maximum age being 37. The total of 163 (70.87%) students, which comprised a large proportion of respondents, were aged less than or equal to 23, 215 (93.48%) participants were living on campus, and 216 (93.91) participants were exposed to social media.

A large number of study participants (82.17%) were pursuing a bachelor of Science in Nursing, with the majority being first-year students (30.87%). The total of 213 (92.61%) study participants had Form Six education as their entry qualification, with 176 (76.52%) participants being the product of public secondary schools and interested in the nursing profession. Lastly, the total of 121 (52.61%) study participants had never been exposed to any presentation training; 215 (93.48%) students had access to individual classroom presentations; and 227 (98.70%) study participants had access to group presentations during their learning process. The detailed findings for the participants’ social demographic information are indicated in Table  2 [ 46 ].

Baseline nursing students’ presentation skills using traditional powerPoint presentations

The current study assessed the participant’s presentation skills when preparing and presenting the materials before the audience using traditional PowerPoint presentations. The study revealed that the overall mean score of the participants’ presentation skills was 4.07 ± 0.56, including a mean score of 3.98 ± 0.62 for the participants’ presentation skills during the preparation of presentation content before the classroom presentation and a mean score of 4.18 ± 0.78 for the participants’ mastery of learning content before the classroom presentation. Moreover, the study revealed a mean score of 4.07 ± 0.71 for participants’ ability to prepare presentation materials for classroom presentations and a mean score of 4.04 ± 0.76 for participants’ ability to share the presentation materials in the classroom, as indicated in Table  3 [ 46 ].

Factors Associated with participants’ presentation skills through traditional powerPoint presentation

The current study revealed that the participants’ study program has a significant effect on their presentation skills, whereby being the bachelor of science in nursing was associated with a 0.37561 (P value < 0.027) increase in the participants’ presentation skills.The year of study also had significant effects on the participants’ presentation skills, whereby being a second-year bachelor student was associated with a 0.34771 (P value < 0.0022) increase in the participants’ presentation skills compared to first-year bachelor students and diploma students. Depending on loans as a source of student income retards presentation skills by 0.24663 (P value < 0.0272) compared to those who do not depend on loans as the source of income. Furthermore, exposure to individual presentations has significant effects on the participants’ presentation skills, whereby obtaining an opportunity for individual presentations was associated with a 0.33732 (P value 0.0272) increase in presentation skills through traditional PowerPoint presentations as shown in Table  4 [ 46 ].

Nursing student presentation skills through pecha kucha presentations

The current study assessed the participant’s presentation skills when preparing and presenting the materials before the audience using Pecha Kucha presentations. The study revealed that the overall mean score and standard deviation of participants’ presentation skills using the Pecha Kucha presentation format were 4.54 ± 0.59, including a mean score of 4.49 ± 0.66 for participant’s presentation skills during preparation of the content before classroom presentation and a mean score of 4.58 ± 0.65 for participants’ mastery of learning content before classroom presentation. Moreover, the study revealed a mean score of 4.58 ± 0.67 for participants ability to prepare the presentation materials for classroom presentation and a mean score of 4.51 ± 0.72 for participants ability to share the presentation materials in the classroom using Pecha Kucha presentation format as indicated in Table  5 [ 46 ].

Comparing Mean scores of participants’ presentation skills between traditional PowerPoint presentation and pecha kucha Presentation

The current study computed a paired t-test to compare and determine the mean change, effect size, and significance associated with the participants’ presentation skills when using the traditional PowerPoint presentation and Pecha Kucha presentation formats. The study revealed that the mean score of the participants’ presentation skills through the Pecha Kucha presentation was 4.54 ± 0.59 (p value < 0.0001) compared to the mean score of 4.07 ± 0.56 for the participants’ presentation skills using the traditional power point presentation with an effect change of 0.78. With regard to the presentation skills during the preparation of presentation content before the classroom presentation, the mean score was 4.49 ± 0.66 using the Pecha Kucha presentation compared to the mean score of 3.98 ± 0.62 for the traditional PowerPoint presentation. Its mean change was 0.51 ± 0.84 ( p  < .0001) with an effect size of 0.61.

Regarding the participants’ mastery of learning content before the classroom presentation, the mean score was 4.58 ± 0.65 when using the Pecha Kucha presentation format, compared to the mean score of 4.18 ± 0.78 when using the traditional power point presentation. Its mean change was 0.40 ± 0.27 ( p  < .0001) with an effect size of 1.48. Regarding the ability of the participants to prepare the presentation materials for classroom presentations, the mean score was 4.58 ± 0.67 when using the Pecha Kucha presentation format, compared to 4.07 ± 0.71 when using the traditional PowerPoint presentation. Its mean change was 0.51 ± 0.96 ( p  < .0001) with an effect size of 0.53.

Regarding the participants’ presentation skills when sharing the presentation material in the classroom, the mean score was 4.51 ± 0.72 when using the Pecha Kucha presentation format, compared to 4.04 ± 0.76 when using the traditional PowerPoint presentations. Its mean change was 0.47 ± 0.10, with a large effect size of 4.7. Therefore, Pecha Kucha presentation pedagogy has a significant effect on the participants’ presentation skills than the traditional PowerPoint presentation as shown in Table  6 [ 46 ].

Factors associated with presentation skills among nursing students through pecha kucha presentation

The current study revealed that the participant’s presentation skills using the Pecha Kucha presentation format were significantly associated with knowledge of the Pecha Kucha presentation format, whereby increase in knowledge was associated with a 0.0239 ( p  < .0001) increase in presentation skills. Moreover, the current study revealed that the presentation through the Pecha Kucha presentation format was not influenced by the year of study, whereby being a second-year student could retard the presentation skills by 0.23093 (p 0.039) compared to a traditional PowerPoint presentation. Other factors are shown in Table  7 [ 46 ].

Social-demographic characteristics profiles of participants

The proportion of male participants was larger than the proportion of female participants in the current study. This was attributable to the distribution of sex across the nursing students at the university understudy, whose number of male nursing students enrolled was higher than female students. This demonstrates the high rate of male nursing students’ enrolment in higher training institutions to pursue nursing and midwifery education programs. Different from the previous years, the nursing training institutions were predominantly comprised of female students and female nurses in different settings. This significant increase in male nursing students’ enrollment in nursing training institutions predicts a significant increase in the male nursing workforce in the future in different settings.

These findings on Pecha Kucha as an alternative to PowerPoint presentations in Massachusetts, where the proportion of female participants was large as compared to male participants, are different from the experimental study among English language students [ 29 ]. The referred findings are different from the results of the randomized control study among the nursing students in Anakara, Turkey, where a large proportion of participants were female nursing students [ 47 ]. This difference in participants’ sex may be associated with the difference in socio-cultural beliefs of the study settings, country’s socio-economic status, which influence the participants to join the nursing profession on the basis of securing employment easily, an opportunity abroad, or pressure from peers and parents. Nevertheless, such differences account for the decreased stereotypes towards male nurses in the community and the better performance of male students in science subjects compared to female students in the country.

The mean age of the study participants was predominantly young adults with advanced secondary education. Their ages reflect adherence to national education policy by considering the appropriate age of enrollment of the pupils in primary and secondary schools, which comprise the industries for students at higher training institutions. This age range of the participants in the current study suits the cognitive capability expected from the participants in order to demonstrate different survival and life skills by being able to set learning goals and develop strategies to achieve their goals according to Jean Piaget’s theory of cognitive learning [ 41 , 42 ].

Similar age groups were noted in the study among nursing students in a randomized control study in Anakara Turkey where the average age was 19.05 ± 0.2 [ 47 ]. A similar age group was also found in a randomized control study among liberal arts students in Anakara, Turkey, on differences in instructor, presenter, and audience ratings of Pecha Kucha presentations and traditional student presentations where the ages of the participants ranged between 19 and 22 years [ 49 ].

Lastly, a large proportion of the study participants had the opportunity for individual and group presentations in the classroom despite having not been exposed to any presentation training before. This implies that the teaching and learning process in a nursing education program is participatory and student-centered, thus giving the students the opportunity to interact with learning contents, peers, experts, webpages, and other learning resources to become knowledgeable. These findings fit with the principle that guides and facilitates the student’s learning from peers and teachers according to the constructivism theory of learning by Lev Vygotsky [ 48 ].

Effects of pecha kucha presentation pedagogy on participants’ presentation skills

The participants’ presentation skills were higher for Pecha Kucha presentations compared with traditional PowerPoint presentations. This display of the Pecha Kucha presentation style enables the nursing students to prepare the learning content, master their learning content before classroom presentations, create good presentation materials and present the materials, before the audience in the classroom. This finding was similar to that at Padang State University, Indonesia, among first-year English and literature students whereby the Pecha Kucha Presentation format helped the students improve their skills in presentation [ 20 ]. Pecha Kucha was also found to facilitate careful selection of the topic, organization and outlining of the students’ ideas, selection of appropriate images, preparation of presentations, rehearsing, and delivery of the presentations before the audience in a qualitative study among English language students at the Private University of Manila, Philippines [ 23 ].

The current study found that Pecha Kucha presentations enable the students to perform literature searches from different webpages, journals, and books in an attempt to identify specific contents during the preparation of the classroom presentations more than traditional PowerPoint presentations. This is triggered by the ability of the presentation format to force the students to filter relevant and specific information to be included in the presentation and search for appropriate images, pictures, or figures to be presented before the audience. Pecha Kucha presentations were found to increase the ability to perform literature searches before classroom presentations compared to traditional PowerPoint presentations in an experimental study among English language students at Worcester State University [ 29 ].

The current study revealed that Pecha Kucha presentations enable the students to create a well-structured classroom presentation effectively by designing 20 meaningful and content-rich slides containing 20 images, pictures, or figures and a transitional flow of 20 s for each slide, more than the traditional PowerPoint presentation with an unlimited number of slides containing bullets with many texts or words. Similarly, in a cross-sectional study of medical students in India, Pecha Kucha presentations were found to help undergraduate first-year medical students learn how to organize knowledge in a sequential fashion [ 26 ].

The current study revealed that Pecha Kucha presentations enhance sound mastery of the learning contents and presentation materials before the classroom presentation compared with traditional PowerPoint presentations. This is hastened by the fact that there is no slide reading during the classroom Pecha Kucha presentation, thus forcing students to read several times, rehearse, and practice harder the presentation contents and materials before the classroom presentation. Pecha Kucha presentation needed first year English and literature students to practice a lot before their classroom presentation in a descriptive qualitative study at Padang State University-Indonesia [ 20 ].

The current study revealed that the participants became more confident in answering the questions about the topic during the classroom presentation using the Pecha Kucha presentation style than during the classroom presentation using the tradition PowerPoint presentation. This is precipitated by the mastery level of the presentation contents and materials through rehearsal, re-reading, and material synthesis before the classroom presentations. Moreover, Pecha Kucha was found to significantly increase the students’ confidence during classroom presentation and preparation in a qualitative study among English language students at the Private University of Manila, Philippines [ 23 ].

Hence, there was enough evidence to reject the null hypothesis in that there was no significant difference in nursing students’ presentation skills between the baseline and end line. The Pecha Kucha presentation format has a significant effect on nursing student’s classroom presentation skills as it enables them to prepare the learning content, have good mastery of the learning contents, create presentation materials, and confidently share their learning with the audience in the classroom.

The current study’s findings complement the available pieces of evidence on the effects of Pecha Kucha presentations on the students’ learning and development of survival life skills in the 21st century. Pecha kucha presentations have more significant effects on the students’ presentation skills compared with traditional PowerPoint presentations. It enables the students to select the topic carefully, organize and outline the presentation ideas, select appropriate images, create presentations, rehearse the presentations, and deliver them confidently before an audience. It also enables the students to select and organize the learning contents for classroom presentations more than traditional PowerPoint presentations.

Pecha Kucha presentations enhance the mastery of learning content by encouraging the students to read the content several times, rehearse, and practice hard before the actual classroom presentation. It increases the students’ ability to perform literature searches before the classroom presentation compared to a traditional PowerPoint presentation. Pecha Kucha presentations enable the students to create well-structured classroom presentations more effectively compared to traditional PowerPoint presentations. Furthermore, Pecha Kucha presentations make the students confident during the presentation of their assignments and project works before the audience and during answering the questions.

Lastly, Pecha Kucha presentations enhance creativity among the students by providing the opportunity for them to decide on the learning content to be presented. Specifically, they are able to select the learning content, appropriate images, pictures, or figures, organize and structure the presentation slides into a meaningful and transitional flow of ideas, rehearse and practice individually before the actual classroom presentation.

Strength of the study

This study has addressed the pedagogical gap in nursing training and education by providing new insights on the innovative students’ presentation format that engages students actively in their learning to bring about meaningful and effective students’ learning. It has also managed to recruit, asses, and provide intended intervention to 230 nursing students without dropout.

Study limitation

The current study has pointed out some of the strengths of the PechaKucha presentations on the students’ presentation skills over the traditional students’ presentations. However, the study had the following limitations: It involved one group of nursing students from one of the public training institutions in Tanzania. The use of one university may obscure the interpretation of the effects of the size of the intervention on the outcome variables of interest, thus limiting the generalization of the study findings to all training institutions in Tanzania. Therefore, the findings from this study need to be interpreted by considering this limitation. The use of one group of nursing students from one university to explore their learning experience through different presentation formats may also limit the generalization of the study findings to all nursing students in the country. The limited generalization may be attributed to differences in socio-demographic characteristics, learning environments, and teaching and learning approaches. Therefore, the findings from this study need to be interpreted by considering this limitation.

Suggestions for future research

The future research should try to overcome the current study limitations and shortcomings and extend the areas assessed by the study to different study settings and different characteristics of nursing students in Tanzania as follows: To test rigorously the effects of Pecha Kucha presentations in enhancing the nursing students’ learning, the future studies should involve nursing students’ different health training institutions rather than one training institution. Future studies should better use the control students by randomly allocating the nursing students or training institutions in the intervention group or control group in order to assess the students’ learning experiences through the use of Pecha Kucha presentations and PowerPoint presentations consecutively. Lastly, future studies should focus on nursing students’ mastery of content knowledge and students’ classroom performance through the use of the Pecha Kucha presentation format in the teaching and learning process.

Data availability

The datasets generated and analyzed by this study can be obtained from the corresponding author on reasonable request through [email protected] & [email protected].

Abbreviations

Doctor (PhD)

Multimedia Theory of Cognitive Learning

National Council for Technical and Vocational Education and Training

Principle Investigator

Pecha Kucha presentation

Statistical Package for Social Sciences

Tanzania Commission for Universities

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Acknowledgements

The supervisors at the University of Dodoma, statisticians, my employer, family members, research assistants and postgraduate colleagues are acknowledged for their support in an attempt to facilitate the development and completion of this manuscript.

The source of funds to conduct this study was the registrar, Tanzania Nursing and Midwifery Council (TNMC) who is the employer of the corresponding author. The funds helped the author in developing the protocol, printing the questionnaires, and facilitating communication during the data collection and data analysis and manuscript preparation.

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S.J.H: conceptualization, proposal development, data collection, data entry, data cleaning and analysis, writing the original draft of the manuscript W.C.M: Conceptualization, supervision, review, and editing of the proposal, and the final manuscript S.S.A: Conceptualization, supervision, review, and editing of the proposal and the final manuscript.

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All methods were carried out under the relevant guidelines and regulations. Since the study involved the manipulation of human behaviors and practices and the exploration of human internal learning experiences, there was a pressing need to obtain ethical clearance and permission from the University of Dodoma (UDOM) Institution of Research Review Ethics Committee (IRREC) in order to conduct this study. The written informed consents were obtained from all the participants, after explaining to them the purpose, the importance of participating in the study, the significance of the study findings to students’ learning, and confidentiality and privacy of the information that will be provided. The nursing students who participated in this study benefited from the knowledge of the Pecha Kucha presentation format and how to prepare and present their assignments using the Pecha Kucha presentation format.

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Haramba, S.J., Millanzi, W.C. & Seif, S.A. Effects of pecha kucha presentation pedagogy on nursing students’ presentation skills: a quasi-experimental study in Tanzania. BMC Med Educ 24 , 952 (2024). https://doi.org/10.1186/s12909-024-05920-2

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DOI : https://doi.org/10.1186/s12909-024-05920-2

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