research design assignment

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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10 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

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?

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• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative vs. quantitative data.

Also, see; Research methods, design, and analysis .

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Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

You May Also Like

Repository of ten perfect research question examples will provide you a better perspective about how to create research questions.

Make sure that your selected topic is intriguing, manageable, and relevant. Here are some guidelines to help understand how to find a good dissertation topic.

How to write a hypothesis for dissertation,? A hypothesis is a statement that can be tested with the help of experimental or theoretical research.

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Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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Research Assignment Design: Overview

• Student Learning Outcomes
• Evaluating Student Work
• Generative AI

Prioritize your learning outcomes

Students can't do it all. Pick what to focus on. For the beginning researcher, research can be a complicated process with many steps to master effectively. Your assignment might want to prioritize some of those over others.

Students experience a greater cognitive load when researching because they lack domain knowledge. You can help students focus their energies by ensuring your assignment matches your priorities.

For example, to prioritize synthesizing arguments, design an assignment around reading and writing with sources, and limit the need for finding sources. To prioritize identifying the scope of research on a topic, require searching for sources.

How do I do this?

• Determine and prioritize  learning goals specific to the research process . 
• Imagine a student working through the assignment. Are there parts of it that demand a lot of work, but that don't match your priorities? If so, rethink the assignment.

Focus on the research and writing process

Prompts should address both the steps along the way (picking a topic, collecting data, synthesizing sources) and the completed assignment. When instructions focus only on the final product, students will view them as a checklist to complete.

For example, requiring a certain number of sources for a paper directs students' attention to the end product. Students will pick the first sources they find, rather than understanding the process of finding many possible sources, then selecting the best ones.

• Give clear and concise directions, with explanations and examples, about why you want something a certain way.
• Make learning objectives explicit, and provide feedback for each step of the research experience.
• Provide opportunities for students to reflect on their learning.
• Allow students time to explore and reframe as they research.
• Discuss how students will know they've found enough information.

Scaffold learning

Break down and explicitly teach the different aptitudes students need to be successful. Research can overwhelm students, especially those new to the process or discipline.

• Break your assignment down into smaller tasks to ensure that students reach learning objectives successively and successfully. 
• Approach this as an opportunity to help students develop research skills. Don't assume students already know how to do research. Learning is iterative, so even if they've had a library research session, a review is useful.
• Recognize the emotional toll of research and give students the time they need to experience the full spectrum of feelings, as part of the instructional design.
• Provide worksheets, handouts, or activities that help students navigate specific aspects of the research process. 
• Assist students over common stumbling blocks. What will get them past bottlenecks to learning in your discipline?

Create an authentic learning experience

Make your assignment relevant to real life experiences and skills. Students learn best and successfully transfer what they're learning when they connect with the assignment, feel the excitement of discovery, or solve challenges. Through disciplinary and experiential learning, students develop different perspectives from which to view the world.

• Encourage curiosity. Give students the chance to experience some of the messiness of research, while limiting how far off track they can get through periodic check-ins.
• Show students how to practice reading, research, and writing in your discipline. All these require interrelated, separate skills.
• Address how students can transfer knowledge and skills.
• Consider problem-based learning, have students examine real-world issues.

Need More Help?

Ways librarians can help.

• Discuss your learning objectives and options for assignments with you
• "Test-drive" your assignment to ensure students will be successful
• Identify why students struggle and how to help them
• Ensure appropriate resources are available
• Identify library instructional resources to link in Canvas
• Provide research instruction for your class
• Research Assignment Stipend Support for your collaboration with a librarian on a new assignment.
• How to Write an Effective Assignment Harvard University Derek Bok Center for Teaching and Learning

See Example Assignments

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5 Research design

Research design is a comprehensive plan for data collection in an empirical research project. It is a ‘blueprint’ for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes: the data collection process, the instrument development process, and the sampling process. The instrument development and sampling processes are described in the next two chapters, and the data collection process—which is often loosely called ‘research design’—is introduced in this chapter and is described in further detail in Chapters 9–12.

Broadly speaking, data collection methods can be grouped into two categories: positivist and interpretive. Positivist methods , such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data. In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data. Often times, these methods are incorrectly equated with quantitative and qualitative research. Quantitative and qualitative methods refers to the type of data being collected—quantitative data involve numeric scores, metrics, and so on, while qualitative data includes interviews, observations, and so forth—and analysed (i.e., using quantitative techniques such as regression or qualitative techniques such as coding). Positivist research uses predominantly quantitative data, but can also use qualitative data. Interpretive research relies heavily on qualitative data, but can sometimes benefit from including quantitative data as well. Sometimes, joint use of qualitative and quantitative data may help generate unique insight into a complex social phenomenon that is not available from either type of data alone, and hence, mixed-mode designs that combine qualitative and quantitative data are often highly desirable.

Key attributes of a research design

The quality of research designs can be defined in terms of four key design attributes: internal validity, external validity, construct validity, and statistical conclusion validity.

Internal validity , also called causality, examines whether the observed change in a dependent variable is indeed caused by a corresponding change in a hypothesised independent variable, and not by variables extraneous to the research context. Causality requires three conditions: covariation of cause and effect (i.e., if cause happens, then effect also happens; if cause does not happen, effect does not happen), temporal precedence (cause must precede effect in time), and spurious correlation, or there is no plausible alternative explanation for the change. Certain research designs, such as laboratory experiments, are strong in internal validity by virtue of their ability to manipulate the independent variable (cause) via a treatment and observe the effect (dependent variable) of that treatment after a certain point in time, while controlling for the effects of extraneous variables. Other designs, such as field surveys, are poor in internal validity because of their inability to manipulate the independent variable (cause), and because cause and effect are measured at the same point in time which defeats temporal precedence making it equally likely that the expected effect might have influenced the expected cause rather than the reverse. Although higher in internal validity compared to other methods, laboratory experiments are by no means immune to threats of internal validity, and are susceptible to history, testing, instrumentation, regression, and other threats that are discussed later in the chapter on experimental designs. Nonetheless, different research designs vary considerably in their respective level of internal validity.

External validity or generalisability refers to whether the observed associations can be generalised from the sample to the population (population validity), or to other people, organisations, contexts, or time (ecological validity). For instance, can results drawn from a sample of financial firms in the United States be generalised to the population of financial firms (population validity) or to other firms within the United States (ecological validity)? Survey research, where data is sourced from a wide variety of individuals, firms, or other units of analysis, tends to have broader generalisability than laboratory experiments where treatments and extraneous variables are more controlled. The variation in internal and external validity for a wide range of research designs is shown in Figure 5.1.

Some researchers claim that there is a trade-off between internal and external validity—higher external validity can come only at the cost of internal validity and vice versa. But this is not always the case. Research designs such as field experiments, longitudinal field surveys, and multiple case studies have higher degrees of both internal and external validities. Personally, I prefer research designs that have reasonable degrees of both internal and external validities, i.e., those that fall within the cone of validity shown in Figure 5.1. But this should not suggest that designs outside this cone are any less useful or valuable. Researchers’ choice of designs are ultimately a matter of their personal preference and competence, and the level of internal and external validity they desire.

Construct validity examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organisational learning are difficult to define, much less measure. For instance, construct validity must ensure that a measure of empathy is indeed measuring empathy and not compassion, which may be difficult since these constructs are somewhat similar in meaning. Construct validity is assessed in positivist research based on correlational or factor analysis of pilot test data, as described in the next chapter.

Statistical conclusion validity examines the extent to which conclusions derived using a statistical procedure are valid. For example, it examines whether the right statistical method was used for hypotheses testing, whether the variables used meet the assumptions of that statistical test (such as sample size or distributional requirements), and so forth. Because interpretive research designs do not employ statistical tests, statistical conclusion validity is not applicable for such analysis. The different kinds of validity and where they exist at the theoretical/empirical levels are illustrated in Figure 5.2.

Improving internal and external validity

The best research designs are those that can ensure high levels of internal and external validity. Such designs would guard against spurious correlations, inspire greater faith in the hypotheses testing, and ensure that the results drawn from a small sample are generalisable to the population at large. Controls are required to ensure internal validity (causality) of research designs, and can be accomplished in five ways: manipulation, elimination, inclusion, and statistical control, and randomisation.

In manipulation , the researcher manipulates the independent variables in one or more levels (called ‘treatments’), and compares the effects of the treatments against a control group where subjects do not receive the treatment. Treatments may include a new drug or different dosage of drug (for treating a medical condition), a teaching style (for students), and so forth. This type of control is achieved in experimental or quasi-experimental designs, but not in non-experimental designs such as surveys. Note that if subjects cannot distinguish adequately between different levels of treatment manipulations, their responses across treatments may not be different, and manipulation would fail.

The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socioeconomic status. In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender (male versus female). Such technique allows for greater generalisability, but also requires substantially larger samples. In statistical control , extraneous variables are measured and used as covariates during the statistical testing process.

Finally, the randomisation technique is aimed at cancelling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random (non-systematic) nature. Two types of randomisation are: random selection , where a sample is selected randomly from a population, and random assignment , where subjects selected in a non-random manner are randomly assigned to treatment groups.

Randomisation also ensures external validity, allowing inferences drawn from the sample to be generalised to the population from which the sample is drawn. Note that random assignment is mandatory when random selection is not possible because of resource or access constraints. However, generalisability across populations is harder to ascertain since populations may differ on multiple dimensions and you can only control for a few of those dimensions.

Popular research designs

As noted earlier, research designs can be classified into two categories—positivist and interpretive—depending on the goal of the research. Positivist designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalised patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research, while examples of interpretive designs include case research, phenomenology, and ethnography. Note that case research can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research. Following are brief descriptions of some of these designs. Additional details are provided in Chapters 9–12.

Experimental studies are those that are intended to test cause-effect relationships (hypotheses) in a tightly controlled setting by separating the cause from the effect in time, administering the cause to one group of subjects (the ‘treatment group’) but not to another group (‘control group’), and observing how the mean effects vary between subjects in these two groups. For instance, if we design a laboratory experiment to test the efficacy of a new drug in treating a certain ailment, we can get a random sample of people afflicted with that ailment, randomly assign them to one of two groups (treatment and control groups), administer the drug to subjects in the treatment group, but only give a placebo (e.g., a sugar pill with no medicinal value) to subjects in the control group. More complex designs may include multiple treatment groups, such as low versus high dosage of the drug or combining drug administration with dietary interventions. In a true experimental design , subjects must be randomly assigned to each group. If random assignment is not followed, then the design becomes quasi-experimental . Experiments can be conducted in an artificial or laboratory setting such as at a university (laboratory experiments) or in field settings such as in an organisation where the phenomenon of interest is actually occurring (field experiments). Laboratory experiments allow the researcher to isolate the variables of interest and control for extraneous variables, which may not be possible in field experiments. Hence, inferences drawn from laboratory experiments tend to be stronger in internal validity, but those from field experiments tend to be stronger in external validity. Experimental data is analysed using quantitative statistical techniques. The primary strength of the experimental design is its strong internal validity due to its ability to isolate, control, and intensively examine a small number of variables, while its primary weakness is limited external generalisability since real life is often more complex (i.e., involving more extraneous variables) than contrived lab settings. Furthermore, if the research does not identify ex ante relevant extraneous variables and control for such variables, such lack of controls may hurt internal validity and may lead to spurious correlations.

Field surveys are non-experimental designs that do not control for or manipulate independent variables or treatments, but measure these variables and test their effects using statistical methods. Field surveys capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or less frequently, through a structured interview. In cross-sectional field surveys , independent and dependent variables are measured at the same point in time (e.g., using a single questionnaire), while in longitudinal field surveys , dependent variables are measured at a later point in time than the independent variables. The strengths of field surveys are their external validity (since data is collected in field settings), their ability to capture and control for a large number of variables, and their ability to study a problem from multiple perspectives or using multiple theories. However, because of their non-temporal nature, internal validity (cause-effect relationships) are difficult to infer, and surveys may be subject to respondent biases (e.g., subjects may provide a ‘socially desirable’ response rather than their true response) which further hurts internal validity.

Secondary data analysis is an analysis of data that has previously been collected and tabulated by other sources. Such data may include data from government agencies such as employment statistics from the U.S. Bureau of Labor Services or development statistics by countries from the United Nations Development Program, data collected by other researchers (often used in meta-analytic studies), or publicly available third-party data, such as financial data from stock markets or real-time auction data from eBay. This is in contrast to most other research designs where collecting primary data for research is part of the researcher’s job. Secondary data analysis may be an effective means of research where primary data collection is too costly or infeasible, and secondary data is available at a level of analysis suitable for answering the researcher’s questions. The limitations of this design are that the data might not have been collected in a systematic or scientific manner and hence unsuitable for scientific research, since the data was collected for a presumably different purpose, they may not adequately address the research questions of interest to the researcher, and interval validity is problematic if the temporal precedence between cause and effect is unclear.

Case research is an in-depth investigation of a problem in one or more real-life settings (case sites) over an extended period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building). The strength of this research method is its ability to discover a wide variety of social, cultural, and political factors potentially related to the phenomenon of interest that may not be known in advance. Analysis tends to be qualitative in nature, but heavily contextualised and nuanced. However, interpretation of findings may depend on the observational and integrative ability of the researcher, lack of control may make it difficult to establish causality, and findings from a single case site may not be readily generalised to other case sites. Generalisability can be improved by replicating and comparing the analysis in other case sites in a multiple case design .

Focus group research is a type of research that involves bringing in a small group of subjects (typically six to ten people) at one location, and having them discuss a phenomenon of interest for a period of one and a half to two hours. The discussion is moderated and led by a trained facilitator, who sets the agenda and poses an initial set of questions for participants, makes sure that the ideas and experiences of all participants are represented, and attempts to build a holistic understanding of the problem situation based on participants’ comments and experiences. Internal validity cannot be established due to lack of controls and the findings may not be generalised to other settings because of the small sample size. Hence, focus groups are not generally used for explanatory or descriptive research, but are more suited for exploratory research.

Action research assumes that complex social phenomena are best understood by introducing interventions or ‘actions’ into those phenomena and observing the effects of those actions. In this method, the researcher is embedded within a social context such as an organisation and initiates an action—such as new organisational procedures or new technologies—in response to a real problem such as declining profitability or operational bottlenecks. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may cause the desired change. The researcher then observes the results of that action, modifying it as necessary, while simultaneously learning from the action and generating theoretical insights about the target problem and interventions. The initial theory is validated by the extent to which the chosen action successfully solves the target problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from all other research methods, and hence, action research is an excellent method for bridging research and practice. This method is also suited for studying unique social problems that cannot be replicated outside that context, but it is also subject to researcher bias and subjectivity, and the generalisability of findings is often restricted to the context where the study was conducted.

Ethnography is an interpretive research design inspired by anthropology that emphasises that research phenomenon must be studied within the context of its culture. The researcher is deeply immersed in a certain culture over an extended period of time—eight months to two years—and during that period, engages, observes, and records the daily life of the studied culture, and theorises about the evolution and behaviours in that culture. Data is collected primarily via observational techniques, formal and informal interaction with participants in that culture, and personal field notes, while data analysis involves ‘sense-making’. The researcher must narrate her experience in great detail so that readers may experience that same culture without necessarily being there. The advantages of this approach are its sensitiveness to the context, the rich and nuanced understanding it generates, and minimal respondent bias. However, this is also an extremely time and resource-intensive approach, and findings are specific to a given culture and less generalisable to other cultures.

Selecting research designs

Given the above multitude of research designs, which design should researchers choose for their research? Generally speaking, researchers tend to select those research designs that they are most comfortable with and feel most competent to handle, but ideally, the choice should depend on the nature of the research phenomenon being studied. In the preliminary phases of research, when the research problem is unclear and the researcher wants to scope out the nature and extent of a certain research problem, a focus group (for an individual unit of analysis) or a case study (for an organisational unit of analysis) is an ideal strategy for exploratory research. As one delves further into the research domain, but finds that there are no good theories to explain the phenomenon of interest and wants to build a theory to fill in the unmet gap in that area, interpretive designs such as case research or ethnography may be useful designs. If competing theories exist and the researcher wishes to test these different theories or integrate them into a larger theory, positivist designs such as experimental design, survey research, or secondary data analysis are more appropriate.

Regardless of the specific research design chosen, the researcher should strive to collect quantitative and qualitative data using a combination of techniques such as questionnaires, interviews, observations, documents, or secondary data. For instance, even in a highly structured survey questionnaire, intended to collect quantitative data, the researcher may leave some room for a few open-ended questions to collect qualitative data that may generate unexpected insights not otherwise available from structured quantitative data alone. Likewise, while case research employ mostly face-to-face interviews to collect most qualitative data, the potential and value of collecting quantitative data should not be ignored. As an example, in a study of organisational decision-making processes, the case interviewer can record numeric quantities such as how many months it took to make certain organisational decisions, how many people were involved in that decision process, and how many decision alternatives were considered, which can provide valuable insights not otherwise available from interviewees’ narrative responses. Irrespective of the specific research design employed, the goal of the researcher should be to collect as much and as diverse data as possible that can help generate the best possible insights about the phenomenon of interest.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Designing Research Assignments: Assignment Ideas

• Student Research Needs
• Assignment Guidelines
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• BEAM Method

Assignment Templates

Research diaries offer students an opportunity to reflect on the research process, think about how they will address challenges they encounter, and encourage students to think about and adjust their strategies. 

• Research Diary Template
• Research Diary Instructions

Alternative Assignments

There are many different types of assignments that can help your students develop their information literacy and research skills. 

The assignments listed below target different skills, and some may be more suitable for certain courses than others.

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Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the data collection methods , such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

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Project Planning for the Beginner: Research Design

• Defining a Topic
• Reviewing the Literature
• Developing a Researchable Question

Research Design

• Planning, Data, Writing and Dissemination

What Is a Research Plan?

This refers to the overall plan for your research, and will be used by you and your supervisor to indicate your intentions for your research and the method(s) you’ll use to carry it out. It includes:

• A specification of your research questions

• An outline of your proposed research methods

• A timetable for doing the work

What Is Research Design?

The term “ research design “ is usually used in reference to experimental research, and refers to the design of your experiment. However, you will also see the term “research design” used in other types of research. Below is a list of possible research designs you might encounter or adopt for your research:

• Descriptive or exploratory (e.g., case study , naturalistic observation )

• Correlational (e.g., case-control study, observational study )

• Quasi-experimental (e.g., field experiment , quasi-experiment )

• Experimental (experiment with random allocation and a control and test group )

• Review (e.g. literature review , systematic review )

• Meta-analytic (e.g. meta-analysis )

Research Design Choices

How do i match my research method to my research question.

The method(s) you use must be capable of answering the research questions you have set. Here are some things you may have to consider:

• Often questions can be answered in different ways using different methods

• You may be working with multiple methods

• Methods can answer different sorts of questions

• Questions can be answered in different ways.

The matching of method(s) to questions always matters . Some methods work better for particular sorts of questions.

If your question is a hypothesis which must be falsifiable, you can answer it using the following possible methods:

• An experimental method using statistical methods to test your hypothesis.

• Survey data (either generated by you or secondary data) using statistical methods to test your hypothesis.

If your question requires you to describe a social context and/or process, then you can answer it using the following possible methods:

• You can use data from your own surveys and/or secondary data to carry out descriptive statistics and numerical taxonomy methods for classification .

• You can use qualitative material derived from:

• Documentary research

• Qualitative interviews

• Focus groups

• Visual research

• Ethnographic methods

• Any combination of the above may be deployed.

If your question(s) require you to make causal statements about how certain things have come to be as they are, then you might consider using the following:

• You can build quantitative causal models using techniques which derive from statistical regression analysis and seeing if the models “fit” your quantitative data set.

• You can do this through building simulations .

• You can do this by using figurational methods, particularly qualitative comparative analysis , which start either with the construction of quantitative descriptions of cases from qualitative accounts of those cases, or with an existing data set which contains quantitative descriptions of cases. 

• You can combine both approaches.

If your question(s) require you to produce interpretive accounts of human social actions with a focus on the meanings actors have attached to those actions, then you might consider using the following:

• You can use documentary resources which include accounts of action(s) and the meanings actors have attached to those actions. This is a key approach in historical research.

• You can conduct qualitative interviews .

• You can hold focus groups .

• You can do this using ethnographic observation .

• You can combine any or all of above approaches.

If your question(s) are evaluative, this could mean that you have to find out if some intervention has worked, how it has worked if it has, and why it didn’t work if it didn’t. You might then consider using the following:

• Any combination of quantitative and qualitative methods which fit the data you have.

• You should always use process tracing to generate a careful historical account of the intervention and its context(s). 

Checklist: Question to Ask When Deciding On a Method

Here are seven questions you should be able to answer about the methods you have chosen for your research. 

• Does your method/do your methods fit the research question(s)?
• Do you understand how the methods relate to your methodological position?
• Do you know how to use the method(s)  ?  If not, can you learn how to use the method(s)?
• Do you have the resources you need to use the methods? For example:

• statistical software

• qualitative data analysis software

• an adequate computer

• access to secondary data sets

• audio-visual equipment

• language training

• transport You need to work through this list and add anything else that you need.

• If you are using multiple methods, do you know how you are going to combine them to carry out the research?
• If you are using multiple methods, do you know how you are going to combine the  products of using them when writing up your research? 
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Faculty Toolkit: Designing Research Assignments

It's Complicated: What Students Say About Research and Writing Assignments from Project Information Literacy

How Librarians Can Help

Librarians are available to consult with faculty and instructors to create or revise effective research assignments and classroom activities that foster critical thinking, evaluation skills, and promote lifelong learning.

Librarians can help you:

• Understand students' research capabilities.
• Create, revise, or offer suggestions on your research-based assignments.
• Talk about alternatives to traditional research papers or presentations.
• Identify and discuss library resources suitable for an online class research guide
• Provide individualized training on library resources.

Provide Tools & Support

• Provide copies of research assignments to your librarian so we are better prepared to assist your students when they need help.
• Consider putting materials on reserve that will be needed by large numbers of students to ensure all students will have access to them.

Consider Alternatives to the Research Paper

• Explore the library as an "Ethnographer" (Library Discovery Tour not to be confused with a scavenger hunt)
• Generate a shared bibliography of readings (see " How to get students to find and read 94 articles before the next class ")
• Compare disciplinary perspectives on the same topic
• Find and compare articles on oil spills in the news and the scientific literature
• Read a short article from the popular press (provided by professor) dealing with results of original research. Locate the original research findings on which the article was based, discuss the relationship between the popular article and the original research, and critique the accuracy of the popular article
• Find facts to support or contradict an editorial
• Research the publications and career of a prominent scholar
• Compile an annotated bibliography
• Prepare a literature review
• Find book reviews on a text used in class
• Evaluate a web site
• Find and summarize recent news related to a class topic, discuss in class (one-time or recurring).
• Research a topic and present findings as a poster session for classmates or larger group.
• Research a topic or event using information published in different decades. Compare and discuss what changes occurred in the literature and why.

Tips for Designing Library Research Assignments

• Address Learning Goals Related to the Research Process . Consider what research skills you would like students to develop in completing the assignment and discuss with your students the importance of developing those skills.
• Be Clear about Your Expectations . Remember that your students may not have prior experience with scholarly journals, monographs, or academic libraries. Spend time in class discussing how research is produced and disseminated in your discipline and how you expect your students to participate in academic discourse in the context of your class.
• Scaffolding your Assignment Brings Focus to the Research Process . Breaking a complex research assignment down into a sequence of smaller, more manageable parts has a number of benefits: it models how to approach a research question and effective time management, it gives students the opportunity to focus on and master key research skills, it provides opportunities for feedback, and it can be an effective deterrent to plagiarism.
• Devote Class Time to Discussion of the Assignment in Progress . Periodic discussions in class can help students reflect on the research process and its importance, encourage questions, and help students develop a sense that what they are doing is a transferable process that they can use for other assignments.
• Criteria for Assessment . In your criteria for assessment (i.e. written instructions, rubrics), make expectations related to the research process explicit. For example, are there specific expectations for the types of resources students should use and how they should be cited? Research shows that students tend to use more scholarly sources when faculty provide them with clear guidelines regarding the types of sources that should be used.
• Test Your Assignment . In testing an assignment yourself, you may uncover practical roadblocks (e.g., too few copies of a book for too many students, a source is no longer available online). Librarians can help with testing your assignment, suggest strategies for mitigating roadblocks (i.e. place books on reserve for your students, suggest other resources), or design customized supporting materials (i.e. handouts or web pages).
• Collaborate with Librarians . Librarians can help you design an effective research assignment that helps students develop the research skills you value and introduces your students to the most useful resources. We also can work with you to develop and teach a library instruction session for your students that will help them learn the strategies they will need in order to complete your assignment.
• Make sure they know how and where to get help from librarians.
• Librarians will meet with students to help them develop their topics and teach them how to find and evaluate sources.

Some content is adapted from University of Wisconsin - Madison Libraries

Common Problems to Avoid

• Waiting until a couple days before the class to ask for an instruction session doesn't allow librarians adequate time to prepare and reserve a classroom.
• Sending (or bringing) an entire class to the Library for research time without notice. The Tioga Library Building is for Quiet Study.  In the Snoqualmie Building, there is a limited number of computer workstations and small group study spaces. The staffing at the Reference desk cannot adequately accommodate working with classes.
• Assigning Scavenger hunts - Roaming around the library looking for trivia is not research and is often seen as busy work by students that is disconnected from their research assignments.
• Be sure the library has the resources your students need!  Avoid requiring students to use resources the library does not own or have in your preferred format (e.g. print journal articles) and cannot obtain within a reasonable timeframe.
• Avoid having each student research the same topic.  This tends to stretch library resources too thin, especially when printed materials or limited connections to a key database are involved.
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Designing Effective Research Assignments

What to expect in this guide, assignment design tips, research process foundations.

• Evidence-Based Information on Student Research Behaviors
• Topic Exploration Sample In-Class Exercises
• Searching for Sources Sample In-Class Exercises
• Reading & Evaluation Sample In-Class Exercises
• Instructor Supports

Permission to Share

This guide was created by Anne-Marie Deitering , and since adapted by other OSU Librarians. It is licensed by Oregon State University Library under a Creative Commons Attribution-Noncommercial 3.0 United States License .  You may reproduce any part of it for noncommercial purposes as long as credit is included. You are encouraged to license your derivative works under Creative Commons as well to encourage sharing and reuse of educational materials.

This guide is intended to help Oregon State University instructors as they design or revise assignments and projects that require outside sources — both scholarly and non-scholarly. The guide includes resources to help you think about

• assignment design ,
• research evidence to better understand student learning and information needs ,
• example assignment prompts on topic exploration , searching for sources , and reading and evaluation , 
• access to OSU librarians' support for instructors.

Before assigning research in your class, consider these questions:

• Will finding, using, and learning from outside sources help students be successful in my class or meet my learning objectives?
• What do I want students to be able to do with their research and the evidence they find?
• What do I have the capacity to support?

Try to avoid...

• Assignments that require students to use, locate, or manipulate something that our library does not have access to.
• Assignments that require students to find sources in an outdated or inefficient way.
• Assignments with source requirements that don't make sense for the intended audience or rhetorical purpose.

The pedagogical base for research using sources is commonly referred to as information literacy. The basic steps of information literacy include:

• identifying and exploring the information need or topic,
• searching for and finding information related to that need,
• reading and evaluating the information for characteristics like relevance or credibility,
• applying information in a way that is appropriate for the specific context,
• using information ethically.

The rest of this guide will share strategies and tools you can use with students during each step of the information literacy — or research — cycle. Notice that the steps are meant to be iterative and will often loop back and inform other stages of the research process. The guide will share strategies to encourage students to engage in these iterative behaviors. 

• Next: Evidence-Based Information on Student Research Behaviors >>
• Last Updated: Sep 6, 2023 4:22 PM
• URL: https://guides.library.oregonstate.edu/effectiveresearchassignments

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More From Forbes

5 highest paying skills for project managers in 2024, from research.

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To remain competitive and gain an edge as a project management professional, it helps to learn one ... [+] or more of these five critical skills this year

Within a competitive job market in which 25 million project managers are projected to be in demand over the next few years, it's critical for aspiring and existing project management professionals to stay ahead of the curve so they can secure the highest-paying roles. While the project management profession in itself is a well-remunerated industry, with U.S. median salaries jumping to as high as $120,000, that doesn't necessarily mean that you can sit back, relax, and assume you're worthy of a six-figure salary.

There are things that lie within your power, which you can do to dramatically skyrocket your earnings.

When exploring how to increase and even multiply your salary as a project manager, it's best to look into what are the highest-paying, in-demand skills within the project management industry, so that you can position yourself as best suited for a promotion, or as a star candidate when job-searching.

Highest-Paying In-Demand Skills For Project Managers

The compensation and salary data company, PayScale, compiled a list of high-paying skills which include a combination of hard, technical skills, and soft or power skills for those within the project industry.

It's worth noting that there are some industries in which the impact of these skills on your pay increase will be more visible than others. For example, considering programme/project management within the technology industry, you can expect to earn more for your skill set, as the tech industry is renowned for offering the highest salaries to project and program management professionals. As such, some of the skills listed below are more specific to the technology industry.

Best High-Yield Savings Accounts Of 2024

Best 5% interest savings accounts of 2024.

However, many of these skills are equally in demand in other industries as well, so they can still be applicable and relevant to you.

These competencies include:

1. Agile Software Development

Using the agile software development approach for building and delivering software products entails prioritising flexibility responsiveness and adaptability to change as well as collaboration. Following this framework, you and your team would be working in sprints, (short iterations) which allows you to make incremental progress and quickly respond to stakeholder requirements and feedback to meet their needs.

This is the highest-paying skill for project managers, with Payscale data revealing that it can boost your earnings by 47%. Coursera and Skillsoft are two of the amazing resources that are available for you to learn this skill.

The tech industry offers the highest salaries for PMs

2. Business Analysis

Analytical thinking is listed in the World Economic Forum's Future of Jobs Report 2023 as the number one skill needed by professionals over the next few years.

Business analysis, in simple terms, is when you identify business needs and research solutions for them. This skill comes handy in your PM role, as you will naturally be working with stakeholders on a regular basis to gather and analyze data on their needs and define requirements, and then propose the best solution with your program or project. This skill improves salary prospects by up to 43%.

3. Engineering Design

Next on the list is engineering design, which is of course more specifically focused on the technology industry. This involves taking the results of your business analysis, and creating detailed plans and specifications, including prototyping and testing, to ensure you deliver a high-quality product that satisfies your stakeholders' needs. This skill is more relatable to product managers, and provides a 14% uptake in salary.

4. Risk Management/Risk Control

With all the changes that are occurring to disrupt industries in 2024, it's no wonder that risk management and risk control are highly in-demand skills for project and program managers to possess. This skill also has the potential for a 14% salary boost, according to Payscale analysis.

You can learn more about how to comprehensively manage and plan for risk by undertaking courses and certifications such as those offered by the Institute of Risk Management, which are globally recognized.

5. Strategy

Strategic thinking is a trademark of leadership, and of exceptional program and project management. You need to be able to steer your project team in the right direction to achieve successful project outcomes, and this means you'll need to have a solid strategy that is clearly articulated so that everyone is on the same page. Setting clear goals and objectives, and assigning the right team members to fulfil each task according to their strengths requires some practice to get it right, but it can be achieved if you're persistent and intentional. Possessing and showcasing this skill results in a 12% salary increase.

Risk management skills helps ensure your project is prepared for worst-case scenarios and minimizes ... [+] negative outcomes, including those that could affect your job directly

By investing in yourself this year by upskilling in one or more of these five essential competencies, you can position yourself to successfully meet the evolving demands of your stakeholders and the project management industry, and unlock numerous career-building and salary-boosting opportunities.

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Leveraging collective action and environmental literacy to address complex sustainability challenges

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• Open access
• Published: 09 August 2022
• Volume 52 , pages 30–44, ( 2023 )

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You have full access to this open access article

• Nicole M. Ardoin   ORCID: orcid.org/0000-0002-3290-8211 1 ,
• Alison W. Bowers 2 &
• Mele Wheaton 3  

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Developing and enhancing societal capacity to understand, debate elements of, and take actionable steps toward a sustainable future at a scale beyond the individual are critical when addressing sustainability challenges such as climate change, resource scarcity, biodiversity loss, and zoonotic disease. Although mounting evidence exists for how to facilitate individual action to address sustainability challenges, there is less understanding of how to foster collective action in this realm. To support research and practice promoting collective action to address sustainability issues, we define the term “collective environmental literacy” by delineating four key potent aspects: scale, dynamic processes, shared resources, and synergy. Building on existing collective constructs and thought, we highlight areas where researchers, practitioners, and policymakers can support individuals and communities as they come together to identify, develop, and implement solutions to wicked problems. We close by discussing limitations of this work and future directions in studying collective environmental literacy.

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Introduction

For socio-ecologically intertwined issues—such as climate change, land conversion, biodiversity loss, resource scarcity, and zoonotic diseases—and their associated multi-decadal timeframes, individual action is necessary, yet not sufficient, for systemic, sustained change (Amel et al. 2017 ; Bodin 2017 ; Niemiec et al. 2020 ; Spitzer and Fraser 2020 ). Instead, collective action, or individuals working together toward a common good, is essential for achieving the scope and scale of solutions to current sustainability challenges. To support communities as they engage in policy and action for socio-environmental change, communicators, land managers, policymakers, and other practitioners need an understanding of how communities coalesce and leverage their shared knowledge, skills, connections, and experiences.

Engagement efforts, such as those grounded in behavior-change approaches or community-based social marketing initiatives, that address socio-environmental issues have often emphasized individuals as the pathway to change. Such efforts address a range of domains including, but not limited to, residential energy use, personal transportation choices, and workplace recycling efforts, often doing so in a stepwise fashion, envisioning each setting or suite of behaviors as discrete spheres of action and influence (Heimlich and Ardoin 2008 ; McKenzie-Mohr 2011 ). In this way, specific actions are treated incrementally and linearly, considering first the individual barriers to be removed and then the motivations to be activated (and, sometimes, sustained; Monroe 2003 ; Gifford et al. 2011 ). Once each behavior is successfully instantiated, the next barrier is then addressed. Proceeding methodically from one action to the next, such initiatives often quite successfully alter a series of actions or group of related behaviors (at least initially) by addressing them incrementally, one at a time (Byerly et al. 2018 ). Following this aspirational logic chain, many resources have been channeled into such programs under the assumption that, by raising awareness and knowledge, such information, communication, and educational outreach efforts will shift attitudes and behaviors to an extent that, ultimately, mass-scale change will follow. (See discussion in Wals et al. 2014 .)

Numerous studies have demonstrated, however, that challenges arise with these stepwise approaches, particularly with regard to their ability to address complex issues and persist over time (Heimlich and Ardoin 2008 ; Wals et al. 2014 ). Such approaches place a tremendous—and unrealistic—burden on individuals, ignoring key aspects not only of behavioral science but also of social science more broadly, including the view that humans exist nested within socio-ecological systems and, thus, are most successful at achieving lasting change when it is meaningful, relevant, and undertaken within a supportive context (Swim et al. 2011 ; Feola 2015 ). Individualized approaches often require multiple steps or nudges (Byerly et al. 2018 ), or ongoing reminders to retain their salience (Stern et al. 2008 ). Because of the emphasis on decontextualized action, such approaches can miss, ignore, obfuscate, or minimize the importance of the bigger picture, which includes the sociocultural, biophysical, and political economic contexts (Ardoin 2006 ; Amel et al. 2017 ). Although the tightly trained focus on small, actionable steps and reliance on individual willpower may help in initially achieving success with initial habit formation (Carden and Wood 2018 ), it becomes questionable in terms of bringing about a wave of transformation on larger scales in the longer term. For those decontextualized actions to persist, they require continued prompting, constancy, and support in the social and biophysical context (Schultz 2014 ; Manfredo et al. 2016 ; Wood and Rünger 2016 ).

Less common in practice are theoretically based initiatives that embrace the holistic nature of the human experience, which occurs within complex systems spanning time and space in a multidimensional, weblike fashion (Bronfenbrenner 1979 ; Rogoff 2003 ; Barron 2006 ; DeCaro and Stokes 2008 ; Gould et al. 2019 ; Hovardas 2020 ). These systems-thinking approaches, while varying across disciplines and epistemological perspectives, envision human experiences, including learning and behavior, as occurring within a milieu that include the social, political, cultural, and historical contexts (Rogoff 2003 ; Roth and Lee 2007 ; Swim et al. 2011 ; Gordon 2019 ). In such a view, people’s everyday practices continuously reflect and grow out of past learning and experiences, not only at the individual, but also at the collective level (Lave 1991 ; Gutiérrez and Rogoff 2003 ; Nasir et al. 2020 ; Ardoin and Heimlich 2021 ). The multidimensional context in which we exist—including the broader temporal and spatial ecosystem—both facilitates and constrains our actions.

Scholars across diverse areas of study discuss the need for and power of collective thought and action, using various conceptual frames, models, and terms, such as collective action, behavior, impact, and intelligence; collaborative governance; communities of practice; crowdsourcing; and social movement theory; among many others (Table 1 ). These scholars acknowledge and explore the influence of our multidimensional context on collective thought and action. In this paper, we explore the elements and processes that constitute collective environmental literacy . We draw on the vast, relevant literature and, in so doing, we attempt to invoke the power of the collective: by reviewing and synthesizing ideas from a variety of fields, we strive to leverage existing constructs and perspectives that explore notions of the “collective” (see Table 1 for a summary of constructs and theories reviewed to develop our working definition of collective environmental literacy). A primary goal of this paper is to dialogue with other researchers and practitioners working in this arena who are eager to uncover and further explore related avenues.

First, we present a formal definition of collective environmental literacy. Next, we briefly review the dominant view of environmental literacy at the individual level and, in support of a collective take on environmental literacy, we examine various collective constructs. We then delve more deeply into the definition of collective environmental literacy by outlining four key aspects: scale, dynamic processes, shared resources, and synergy. We conclude by providing suggestions for future directions in studying collective environmental literacy.

Defining collective environmental literacy

Decades of research in political science, economics, anthropology, sociology, psychology, and the learning sciences, among other fields (Chawla and Cushing 2007 ; Ostrom 2009 ; Sawyer 2014 ; Bamberg et al. 2015 ; Chan 2016 ; Jost et al. 2017 ) repeatedly demonstrates the effectiveness, and indeed necessity of, collective action when addressing problems that are inherently social in nature. Yet theoretical frameworks and empirical documentation emphasize that such collective activities rarely arise spontaneously and, when they do, are a result of preconditions that have sown fertile ground (van Zomeren et al. 2008 ; Duncan 2018 ). Persistent and effective collective action then requires scaffolding in the form of institutional, sociocultural, and political economic structure that provides ongoing support. To facilitate discussions of how to effectively support collective action around sustainability issues, we suggest the concept of “collective environmental literacy.” We conceptualize collective environmental literacy as more than collective action; rather, we suggest that the term encapsulates action along with its various supporting structures and resources. Additionally, we employ the word “literacy” as it connotes learning, intention, and the idea that knowledge, skills, attitudes, and behaviors can be enhanced iteratively over time. By using “literacy,” we strive to highlight the efforts, often unseen, that lead to effective collective action in communities. We draw on scholarship in science and health education, areas that have begun over the past two decades to theorize about related areas of collective science literacy (Roth and Lee 2002 , 2004 ; Lee and Roth 2003 ; Feinstein 2018 ) and health literacy (Freedman et al. 2009 ; Papen 2009 ; Chinn 2011 ; Guzys et al. 2015 ). Although these evolving constructs lack consensus definitions, they illuminate affordances and constraints that exist when conceptualizing collective environmental literacy (National Academies of Sciences, Engineering, and Medicine [NASEM] 2016 ).

Some of the key necessary—but not sufficient—conditions that facilitate aligned, collective actions include a common body of decision-making information; shared attitudes, values, and beliefs toward a motivating issue or concern; and efficacy skills that facilitate change-making (Sturmer and Simon 2004 ; van Zomeren et al. 2008 ; Jagers et al. 2020 ). In addition, other contextual factors are essential, such as trust, reciprocity, collective efficacy, and communication among group members and societal-level facilitators, such as social norms, institutions, and technology (Bandura 2000 ; Ostrom 2010 ; McAdam and Boudet 2012 ; Jagers et al. 2020 ). Taken together, we term this body of knowledge, dispositions, skills, and the context in which they flourish collective environmental literacy . More formally, we define collective environmental literacy as: a dynamic, synergistic process that occurs as group members develop and leverage shared resources to undertake individual and aggregate actions over time to address sustainability issues within the multi-scalar context of a socio-environmental system (Fig.  1 ).

Key elements of collective environmental literacy

Environmental literacy: Historically individual, increasingly collective

Over the past five decades, the term “environmental literacy” has come into increasingly frequent use. Breaking from the traditional association of “literacy” with reading and writing in formal school contexts, environmental literacy emphasizes associations with character and behavior, often in the form of responsible environmental stewardship (Roth 1992 ). Footnote 1 Such perspectives define the concept as including affective (attitudinal), cognitive (knowledge-based), and behavioral domains, emphasizing that environmental literacy is both a process and outcome that develops, builds, and morphs over time (Hollweg et al. 2011 ; Wheaton et al. 2018 ; Clark et al. 2020 ).

The emphasis on defining, measuring, and developing interventions to bring about environmental literacy has primarily remained at the individual scale, as evidenced by frequent descriptions of an environmentally literate person (Roth 1992 ; Hollweg et al. 2011 among others) rather than community or community member. In most understandings, discussions, and manifestations of environmental literacy, the implicit assumption remains that the unit of action, intervention, and therefore analysis occurs at the individual level. Yet instinctively and perhaps by nature, community members often seek information and, as a result, take action collectively, sharing what some scholars call “the hive mind” or “group mind,” relying on each other for distributed knowledge, expertise, motivation, and support (Surowiecki 2005 ; Sunstein 2008 ; Sloman and Fernbach 2017 ; Paul 2021 ).

As with the proverbial elephant (Saxe, n.d.), each person, household, or neighborhood group may understand or “see” a different part of an issue or challenge, bring a novel understanding to the table, and have a certain perspective or skill to contribute. Although some environmental literacy discussions allude to a collective lens (e.g., Hollweg et al. 2011 ; Ardoin et al. 2013 ; Wheaton et al. 2018 ; Bey et al. 2020 ), defining, developing frameworks, and creating measures to assess the efficacy of such collective-scale sustainability-related endeavors has remained elusive. Footnote 2 Looking to related fields and disciplines—such as ecosystem theory, epidemiology and public health, sociology, network theory, and urban planning, among others—can provide insight, theoretical frames, and empirical examples to assist in such conceptualizations (McAdam and Boudet 2012 ; National Research Council 2015 ) (See Table 1 for an overview of some of the many areas of study that informed our conceptualization of collective environmental literacy).

Seeking the essence of the collective: Looking to and learning from others

The social sciences have long focused on “the kinds of activities engaged in by sizable but loosely organized groups of people” (Turner et al. 2020 , para. 1) and addressed various collective constructs, such as collective behavior, action, intelligence, and memory (Table 1 ). Although related constructs in both the social and natural sciences—such as communities of practice (Wenger and Snyder 2000 ), collaborative governance (Ansell and Gash 2008 ; Emerson et al. 2012 ), and the collaboration–coordination continuum (Sadoff and Grey 2005 ; Prager 2015 ), as well as those from social movement theory and related areas (McAdam and Boudet 2012 ; de Moor and Wahlström 2019 )—lack the word “collective” in name, they too leverage the benefits of collectivity. A central tenet connects all of these areas: powerful processes, actions, and outcomes can arise when individuals coalesce around a common purpose or cause. This notion of a dynamic, potent force transcending the individual to enhance the efficacy of outcomes motivates the application of a collective lens to the environmental literacy concept.

Dating to the 1800s, discussions of collective behavior have explored connections to social order, structures, and norms (Park 1927 ; Smelser 2011 /1962; Turner and Killian 1987 ). Initially, the focus emphasized spontaneous, often violent crowd behaviors, such as riots, mobs, and rebellions. More contemporarily, sociologists, political scientists, and others who study social movements and collective behaviors acknowledge that such phenomena may take many forms, including those occurring in natural ecosystems, such as ant colonies, bird flocks, and even the human brain (Gordon 2019 ). In sociology, collective action represents a paradigm shift highlighting coordinated, purposeful pro-social movements, while de-emphasizing aroused emotions and crowd behavior (Miller 2014 ). In political science, Ostrom’s ( 1990 , 2000 , 2010 ) theory of collective action in the context of the management of shared resources extends the concept’s reach to economics and other fields. In education and the learning sciences, social learning and sociocultural theories tap into the idea of learning as a social-cognitive-cultural endeavor (Vygotsky 1980 ; Lave and Wenger 1991 ; Tudge and Winterhoff 1993 ; Rogoff 2003 ; Reed et al. 2010 ).

Collective action, specifically, and collective constructs, generally, have found their way into the research and practice in the fields of conservation, natural resources, and environmental management. Collective action theory has been applied in a range of settings and scenarios, including agriculture (Mills et al. 2011 ), invasive species management (Marshall et al. 2016 ; Sullivan et al. 2017 ; Lubeck et al. 2019 ; Clarke et al. 2021 ), fire management (Canadas et al. 2016 ; Charnley et al. 2020 ), habitat conservation (Raymond 2006 ; Niemiec et al. 2020 ), and water governance (Lopez-Gunn 2003 ; Baldwin et al. 2018 ), among others. Frameworks and methods that emphasize other collective-related ideas—like collaboration, co-production, and group learning—are also ubiquitous in natural resource and environmental management. These constructs include community-based conservation (DeCaro and Stokes 2008 ; Niemiec et al. 2016 ), community natural resource management (Kellert et al. 2000 ; Dale et al. 2020 ), collaboration/coordination (Sadoff and Grey 2005 ; Prager 2015 ), polycentricity (Galaz et al. 2012 ; Heikkila et al. 2018 ), knowledge co-production (Armitage et al. 2011 ; Singh et al. 2021 ), and social learning (Reed et al. 2010 ; Hovardas 2020 ). Many writings on collective efforts in the social sciences broadly, and applied in the area of environment specifically, provide insights into collective action’s necessary preconditions, which prove invaluable to further defining and later operationalizing collective environmental literacy.

Unpacking the definition of collective environmental literacy: Anchoring principles

As described, we propose the following working definition of collective environmental literacy drawing on our analysis of related literatures and informed by scholarly and professional experience in the sustainability and conservation fields: a dynamic, synergistic process that occurs as group members develop and leverage shared resources to undertake individual and aggregate actions over time to address sustainability issues within the multi-scalar context of a socio-environmental system (Fig.  1 ). This definition centers on four core, intertwined ideas: the scale of the group involved; the dynamic nature of the process; shared resources brought by, available to, and needed by the group; and the synergy that arises from group interaction.

Multi-scalar

When transitioning from the focus on individual to collective actions—and, herein, principles of environmental literacy—the most obvious and primary requisite shift is one of scale. Yet, moving to a collective scale does not mean abandoning action at the individual scale; rather, success at the collective level is intrinsically tied to what occurs at an individual level. Such collective-scale impacts leverage the power of the hive, harnessing people’s willingness, ability, and motivation to take action alongside others, share their ideas and resources to build collective ideas and resources, contribute to making a difference in an impactful way, and participate communally in pro-social activities.

Collective environmental literacy is likely dynamic in its orientation to scale, incorporating place-based notions, such as ecoregional or community-level environmental literacy (with an emphasis on geographic boundaries). On the other hand, it may encapsulate environmental literacy of a group or organization united by a common identity (e.g., organizational membership) or cause (e.g., old-growth forests, coastal protection), rather than solely or even primarily by geography. Although shifting scales can make measuring collective environmental literacy more difficult, dynamic levels may be a benefit when addressing planetary boundary issues such as climate change, biodiversity, and ocean acidification (Galaz et al. 2012 ). Some scholars have called for a polycentric approach to these large-scale issues in response to a perceived failure of global-wide, top-down solutions (Ostrom 2010 , 2012 ; Jordan et al. 2018 ). Conceptualizing and consequently supporting collective environmental literacy at multiple scales can facilitate such desired polycentricity.

Rather than representing a static outcome, environmental literacy is a dynamic process that is fluctuating and complex, reflective of iterative interactions among community members, whose discussions and negotiations reflect the changing context of sustainability issues. Footnote 3 Such open-minded processes allow for, and indeed welcome, adaptation in a way that builds social-ecological resilience (Berkes and Jolly 2002 ; Adger et al. 2005 ; Berkes 2007 ). Additionally, this dynamism allows for collective development and maturation, supporting community growth in collective knowledge, attitudes, skills, and actions via new experiences, interactions, and efforts (Berkman et al. 2010 ). With this mindset, and within a sociocultural perspective, collective environmental literacy evolves through drawing on and contributing to the community’s funds of knowledge (González et al. 2006 ). Movement and actions within and among groups impact collective literacy, as members share knowledge and other resources, shifting individuals and the group in the course of their shared practices (Samerski 2019 ).

In a collective mode, effectiveness is heightened as shared resources are streamlined, waste is minimized, and innovation maximized. Rather than each group member developing individual expertise in every matter of concern, the shared knowledge, skills, and behaviors can be distributed, pursued, and amplified among group members efficiently and effectively, with collective literacy emerging from the process of pooling diverse forms of capital and aggregating resources. This perspective builds on ideas of social capital as a collective good (Ostrom 1990 ; Putnam 2020 ), wherein relationships of trust and reciprocity are both inputs and outcomes (Pretty and Ward 2001 ). The shared resources then catalyze and sustain action as they are reassembled and coalesced at the group level for collective impact.

The pooled resources—likely vast—may include, but are not limited to, physical and human resources, funding, time, energy, and space and place (physical or digital). Shared resources may also include forms of theorized capital, such as intellectual and social (Putnam 2020 ). Also of note is the recognition that these resources extend far beyond information and knowledge. Of particular interest when building collective environmental literacy are resources previously ignored or overlooked by those in power in prior sustainability efforts. For example, collective environmental literacy can draw strength from shared resources unique to the community or even subgroups within the larger community. Discussions of Indigenous knowledge (Gadgil et al. 1993 ) and funds of knowledge (González et al. 2006 ; Cruz et al. 2018 ) suggest critical, shared resources that highlight strengths of an individual community and its members. Another dimension of shared resources relates to the strength of institutional connections, such as the benefits that accrue from leveraging the collective knowledge, expertise, and resources of organizational collaborators working in adjacent areas to further and amplify each other’s impact (Wojcik et al. 2021 ).

Synergistic

Finally, given the inherent complexities related to defining, deploying, implementing, and measuring these dynamic, at-times ephemeral processes, resources, and outcomes at a collective scale, working in such a manner must be clearly advantageous to pressing sustainability issues at hand. Numerous related constructs and approaches from a range of fields emphasize the benefits of diverse collaboration to collective thought and action, including improved solutions, more effective and fair processes, and more socioculturally just outcomes (Klein 1990 ; Jörg 2011 ; Wenger and Snyder 2000 ; Djenontin and Meadow 2018 ). These benefits go beyond efficient aggregation and distribution of resources, invoking an almost magical quality that defines synergy, resulting in robust processes and outcomes that are more than the sum of the parts.

This synergy relies on the diversity of a group across various dimensions, bringing power, strength, and insight to a decision-making process (Bear and Woolley 2011 ; Curşeu and Pluut 2013 ; Freeman and Huang 2015 ; Lu et al. 2017 ; Bendor and Page 2019 ). Individuals are limited not only to singular knowledge-perspectives and skillsets, but also to their own experiences, which influence their self-affirming viewpoints and tendencies to seek out confirmatory information for existing beliefs (Kahan et al. 2011 ). Although the coming together of those from different racial, cultural, social, and economic backgrounds facilitates a collective literacy process that draws on a wider range of resources and equips a gestalt, it also sets up the need to consider issues of power, privilege, voice, and representation (Bäckstrand 2006 ) and the role of social capital, leading to questions related to trust and reciprocity in effective collectives (Pretty and Ward 2001 ; Folke et al. 2005 ).

Leveraging the ‘Hive’: Proceeding with collective environmental literacy

This paper presents one conceptualization of collective environmental literacy, with the understanding that numerous ways exist to envision its definition, formation, deployment, and measurement. Characterized by a collective effort, such literacies at scale offer a way to imagine, measure, and support the synergy that occurs when the emphasis moves from an individual to a larger whole. By expanding the scale and focusing on shared responsibility among actors at the systems level, opportunities arise for inspiring and enabling a broader contribution to a sustainable future. These evolving notions serve to invite ongoing conversation, both in research and practice, about how to enact our collective responsibility toward, as well as vision of, a thriving future.

Emerging from the many discussions of shared and collaborative efforts to address socio-environmental issues, our conceptualization of collective environmental literacy is a first step toward supporting communities as they work to identify, address, and solve sustainability problems. We urge continued discussions on this topic, with the goal of understanding the concept of collective environmental literacy, how to measure it, and the implications of this work for practitioners. The conceptual roots of collective environmental literacy reach into countless fields of study and, as such, a transdisciplinary approach, which includes an eye toward practice, is necessary to fully capture and maximize the tremendous amount of knowledge, wisdom, and experience around this topic. Specifically, next steps to evolve the concept include engaging sustainability researchers and practitioners in discussions of the saliency of the presented definition of collective environmental literacy. These discussions include verifying the completeness of the definition and ensuring a thorough review of relevant research: Are parts of the definition missing or unclear? What are the “blank, blind, bald, and bright spots” in the literature (Reid 2019 p. 158)? Additionally, recognizing and leveraging literacy at a collective scale most certainly is not unique to environmental work, nor is adopting literacy-related language to conceptualize and measure process outcomes, although the former has consistently proven more challenging. Moreover, although we (the authors) appreciate the connotations and structures gained by using a literacy framework, we struggle with whether “environmental literacy” is the most appropriate and useful term for the conceptualizations as described herein; we, thus, welcome lively discussions about the need for new terminology.

Even at this early stage of conceptualization, this work has implications for practitioners. For scientists, communicators, policymakers, land managers, and other professionals desiring to work with communities to address sustainability issues, a primary take-away message concerns the holistic nature of what is needed for effective collective action in the environmental realm. Many previous efforts have focused on conveying information and, while a lack of knowledge and awareness may be a barrier to action in some cases, the need for a more holistic lens is increasingly clear. This move beyond an individually focused, information-deficit model is essential for effective impact (Bolderdijk et al. 2013 ; van der Linden 2014 ; Geiger et al. 2019 ). The concept of collective environmental literacy suggests a role for developing shared resources that can foster effective collective action. When working with communities, a critical early step includes some form of needs assessment—a systematic, in-depth process that allows for meaningfully gauging gaps in shared resources required to tackle sustainability issues (Braus 2011). Following this initial, evaluative step, an understanding of the components of collective environmental literacy, as outlined in this paper, can be used to guide the development of interventions to support communities in their efforts to address those issues.

Growing discussion of collective literacy constructs, and related areas, suggests researchers, practitioners, and policymakers working in pro-social areas recognize and value collective efforts, despite the need for clearer definitions and effective measures. This definitional and measurement work, in both research and practice, is not easy. The ever-changing, dynamic contexts in which collective environmental literacy exists make defining the concept a moving target, compounded by a need to draw upon work in countless, often distinct academic fields of study. Furthermore, the hard-to-see, inner workings of collective constructs make measurement difficult. Yet, the “power of the hive” is intriguing, as the synergism that arises from communities working in an aligned manner toward a unified vision suggests a potency and wave of motivated action essential to coalescing and leveraging individual goodwill, harnessing its power and potential toward effective sustainability solutions.

See Stables and Bishop’s ( 2001 ) idea of defining environmental literacy by viewing the environment as “text.”

The climate change education literature also includes a nascent, but growing, discussion of collective-lens thinking and literacy. See, for example, Waldron et al. ( 2019 ), Mochizuki and Bryan ( 2015 ), and Kopnina ( 2016 ).

This conceptualization is similar to how some scholars describe collective health literacy (Berkman et al., 2010 ; Mårtensson and Hensing, 2012 ).

Adger, W.N. 2003. Social capital, collective action, and adaptation to climate change. Economic Geography 79: 387–404.

Article   Google Scholar  

Adger, W.N., T.P. Hughes, C. Folke, S.R. Carpenter, and J. Rockström. 2005. Social-ecological resilience to coastal disasters. Science 309: 1036–1039. https://doi.org/10.1126/science.1112122 .

Article   CAS   Google Scholar  

Adler, P.S., and S.-W. Kwon. 2002. Social capital: Prospects for a new concept. Academy of Management Review 27: 17–40. https://doi.org/10.5465/amr.2002.5922314 .

Agrawal, A. 1995. Dismantling the divide between Indigenous and scientific knowledge. Development and Change 26: 413–439. https://doi.org/10.1111/j.1467-7660.1995.tb00560.x .

Aguilar, O.M. 2018. Examining the literature to reveal the nature of community EE/ESD programs and research. Environmental Education Research 24: 26–49. https://doi.org/10.1080/13504622.2016.1244658 .

Aguilar, O., A. Price, and M. Krasny. 2015. Perspectives on community environmental education. In M.C. Monroe & M.E. Krasny (Eds.), Across the spectrum: Resources for environmental educators (3rd edn., pp. 235–249). North American Association for Environmental Education.

Aldrich, D.P., and M.A. Meyer. 2015. Social capital and community resilience. American Behavioral Scientist 59: 254–269. https://doi.org/10.1177/0002764214550299 .

Amel, E., C. Manning, B. Scott, and S. Koger. 2017. Beyond the roots of human inaction: Fostering collective effort toward ecosystem conservation. Science 356: 275–279. https://doi.org/10.1126/science.aal1931 .

Ansell, C., and A. Gash. 2008. Collaborative governance in theory and practice. Journal of Public Administration Research and Theory 18: 543–571. https://doi.org/10.1093/jopart/mum032 .

Ardoin, N.M. 2006. Toward an interdisciplinary understanding of place: Lessons for environmental education. Canadian Journal of Environmental Education 11: 112–126.

Google Scholar  

Ardoin, N.M., and J.E. Heimlich. 2021. Environmental learning in everyday life: Foundations of meaning and a context for change. Environmental Education Research 27: 1681–1699. https://doi.org/10.1080/13504622.2021.1992354 .

Ardoin, N.M., C. Clark, and E. Kelsey. 2013. An exploration of future trends in environmental education research. Environmental Education Research 19: 499–520. https://doi.org/10.1080/13504622.2012.709823 .

Armitage, D., F. Berkes, A. Dale, E. Kocho-Schellenberg, and E. Patton. 2011. Co-management and the co-production of knowledge: Learning to adapt in Canada’s Arctic. Global Environmental Change 21: 995–1004. https://doi.org/10.1016/j.gloenvcha.2011.04.006 .

Assis Neto, F.R., and C.A.S. Santos. 2018. Understanding crowdsourcing projects: A systematic review of tendencies, workflow, and quality management. Information Processing & Management 54: 490–506. https://doi.org/10.1016/j.ipm.2018.03.006 .

Bäckstrand, K. 2006. Multi-stakeholder partnerships for sustainable development: Rethinking legitimacy, accountability and effectiveness. European Environment 16: 290–306. https://doi.org/10.1002/eet.425 .

Baldwin, E., P. McCord, J. Dell’Angelo, and T. Evans. 2018. Collective action in a polycentric water governance system. Environmental Policy and Governance 28: 212–222. https://doi.org/10.1002/eet.1810 .

Bamberg, S., J. Rees, and S. Seebauer. 2015. Collective climate action: Determinants of participation intention in community-based pro-environmental initiatives. Journal of Environmental Psychology 43: 155–165. https://doi.org/10.1016/j.jenvp.2015.06.006 .

Bandura, A. 1977. Social learning theory . Englewood Cliffs: Prentice Hall.

Bandura, A. 2000. Exercise of human agency through collective efficacy. Current Directions in Psychological Science 9: 75–78. https://doi.org/10.1111/1467-8721.00064 .

Barron, B. 2006. Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development 49: 193–224. https://doi.org/10.1159/000094368 .

Barry, M.M., M. D’Eath, and J. Sixsmith. 2013. Interventions for improving population health literacy: Insights from a rapid review of the evidence. Journal of Health Communication 18: 1507–1522. https://doi.org/10.1080/10810730.2013.840699 .

Barton, A.C., and E. Tan. 2009. Funds of knowledge and discourses and hybrid space. Journal of Research in Science Teaching 46: 50–73. https://doi.org/10.1002/tea.20269 .

Bear, J.B., and A.W. Woolley. 2011. The role of gender in team collaboration and performance. Interdisciplinary Science Reviews 36: 146–153. https://doi.org/10.1179/030801811X13013181961473 .

Bendor, J., and S.E. Page. 2019. Optimal team composition for tool-based problem solving. Journal of Economics & Management Strategy 28: 734–764. https://doi.org/10.1111/jems.12295 .

Berkes, F. 2007. Understanding uncertainty and reducing vulnerability: Lessons from resilience thinking. Natural Hazards 41: 283–295. https://doi.org/10.1007/s11069-006-9036-7 .

Berkes, F., and D. Jolly. 2002. Adapting to climate change: Social-ecological resilience in a Canadian western Arctic community. Conservation Ecology 5: 45.

Berkes, F., and H. Ross. 2013. Community resilience: Toward an integrated approach. Society & Natural Resources 26: 5–20. https://doi.org/10.1080/08941920.2012.736605 .

Berkes, F., M.K. Berkes, and H. Fast. 2007. Collaborative integrated management in Canada’s north: The role of local and traditional knowledge and community-based monitoring. Coastal Management 35: 143–162.

Berkman, N.D., T.C. Davis, and L. McCormack. 2010. Health literacy: What is it? Journal of Health Communication 15: 9–19. https://doi.org/10.1080/10810730.2010.499985 .

Bey, G., C. McDougall, and S. Schoedinger. 2020. Report on the NOAA office of education environmental literacy program community resilience education theory of change. National Oceanic and Atmospheric Administration . https://doi.org/10.25923/mh0g-5q69 .

Blumer, H. 1971. Social problems as collective behavior. Social Problems 18: 298–306.

Bodin, Ö. 2017. Collaborative environmental governance: Achieving collective action in social-ecological systems. Science . https://doi.org/10.1126/science.aan1114 .

Bolderdijk, J.W., M. Gorsira, K. Keizer, and L. Steg. 2013. Values determine the (in)effectiveness of informational interventions in promoting pro-environmental behavior. PLoS ONE 8: e83911. https://doi.org/10.1371/journal.pone.0083911 .

Brabham, D.C. 2013. Crowdsourcing . Cambridge: MIT Press.

Book   Google Scholar  

Braus, J. (Ed.). 2011. Tools of engagement: A toolkit for engaging people in conservation. NAAEE/Audubon. https://cdn.naaee.org/sites/default/files/eepro/resource/files/toolsofengagement.pdf .

Brieger, S.A. 2019. Social identity and environmental concern: The importance of contextual effects. Environment and Behavior 51: 828–855. https://doi.org/10.1177/0013916518756988 .

Briggs, J. 2005. The use of Indigenous knowledge in development: Problems and challenges. Progress in Development Studies 5: 99–114. https://doi.org/10.1191/1464993405ps105oa .

Briggs, J., and J. Sharp. 2004. Indigenous knowledges and development: A postcolonial caution. Third World Quarterly 25: 661–676. https://doi.org/10.1080/01436590410001678915 .

Bronfenbrenner, U. 1979. The ecology of human development: Experiments by nature and design . Cambridge: Harvard University Press.

Bruce, C., and P. Chesterton. 2002. Constituting collective consciousness: Information literacy in university curricula. International Journal for Academic Development 7: 31–40. https://doi.org/10.1080/13601440210156457 .

Byerly, H., A. Balmford, P.J. Ferraro, C.H. Wagner, E. Palchak, S. Polasky, T.H. Ricketts, A.J. Schwartz, et al. 2018. Nudging pro-environmental behavior: Evidence and opportunities. Frontiers in Ecology and the Environment 16: 159–168. https://doi.org/10.1002/fee.1777 .

Canadas, M.J., A. Novais, and M. Marques. 2016. Wildfires, forest management and landowners’ collective action: A comparative approach at the local level. Land Use Policy 56: 179–188. https://doi.org/10.1016/j.landusepol.2016.04.035 .

Carden, L., and W. Wood. 2018. Habit formation and change. Current Opinion in Behavioral Sciences 20: 117–122. https://doi.org/10.1016/j.cobeha.2017.12.009 .

Chan, M. 2016. Psychological antecedents and motivational models of collective action: Examining the role of perceived effectiveness in political protest participation. Social Movement Studies 15: 305–321. https://doi.org/10.1080/14742837.2015.1096192 .

Charnley, S., E.C. Kelly, and A.P. Fischer. 2020. Fostering collective action to reduce wildfire risk across property boundaries in the American West. Environmental Research Letters 15: 025007. https://doi.org/10.1088/1748-9326/ab639a .

Chawla, L., and D.F. Cushing. 2007. Education for strategic environmental behavior. Environmental Education Research 13: 437–452. https://doi.org/10.1080/13504620701581539 .

Chinn, D. 2011. Critical health literacy: A review and critical analysis. Social Science & Medicine 73: 60–67. https://doi.org/10.1016/j.socscimed.2011.04.004 .

Clark, C.R., J.E. Heimlich, N.M. Ardoin, and J. Braus. 2020. Using a Delphi study to clarify the landscape and core outcomes in environmental education. Environmental Education Research 26: 381–399. https://doi.org/10.1080/13504622.2020.1727859 .

Clarke, M., Z. Ma, S.A. Snyder, and K. Floress. 2021. Factors influencing family forest owners’ interest in community-led collective invasive plant management. Environmental Management 67: 1088–1099. https://doi.org/10.1007/s00267-021-01454-1 .

Cruz, A.R., S.T. Selby, and W.H. Durham. 2018. Place-based education for environmental behavior: A ‘funds of knowledge’ and social capital approach. Environmental Education Research 24: 627–647. https://doi.org/10.1080/13504622.2017.1311842 .

Curşeu, P.L., and H. Pluut. 2013. Student groups as learning entities: The effect of group diversity and teamwork quality on groups’ cognitive complexity. Studies in Higher Education 38: 87–103. https://doi.org/10.1080/03075079.2011.565122 .

Cutter, S.L., L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate, and J. Webb. 2008. A place-based model for understanding community resilience to natural disasters. Global Environmental Change 18: 598–606. https://doi.org/10.1016/j.gloenvcha.2008.07.013 .

Dale, A., K. Vella, S. Ryan, K. Broderick, R. Hill, R. Potts, and T. Brewer. 2020. Governing community-based natural resource management in Australia: International implications. Land 9: 234. https://doi.org/10.3390/land9070234 .

de Moor, J., and M. Wahlström. 2019. Narrating political opportunities: Explaining strategic adaptation in the climate movement. Theory and Society 48: 419–451. https://doi.org/10.1007/s11186-019-09347-3 .

DeCaro, D., and M. Stokes. 2008. Social-psychological principles of community-based conservation and conservancy motivation: Attaining goals within an autonomy-supportive environment. Conservation Biology 22: 1443–1451.

Djenontin, I.N.S., and A.M. Meadow. 2018. The art of co-production of knowledge in environmental sciences and management: Lessons from international practice. Environmental Management 61: 885–903. https://doi.org/10.1007/s00267-018-1028-3 .

Duncan, L.E. 2018. The psychology of collective action. In The Oxford handbook of personality and social psychology , ed. K. Deaux and M. Snyder. Oxford: Oxford University Press.

Edwards, M., F. Wood, M. Davies, and A. Edwards. 2015. ‘Distributed health literacy’: Longitudinal qualitative analysis of the roles of health literacy mediators and social networks of people living with a long-term health condition. Health Expectations 18: 1180–1193. https://doi.org/10.1111/hex.12093 .

Emerson, K., T. Nabatchi, and S. Balogh. 2012. An integrative framework for collaborative governance. Journal of Public Administration Research and Theory 22: 1–29.

Engeström, Y. 2001. Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work 14: 133–156. https://doi.org/10.1080/13639080020028747 .

Ensor, J., and B. Harvey. 2015. Social learning and climate change adaptation: Evidence for international development practice. Wires Climate Change 6: 509–522. https://doi.org/10.1002/wcc.348 .

Fanta, V., M. Šálek, and P. Sklenicka. 2019. How long do floods throughout the millennium remain in the collective memory? Nature Communications 10: 1105. https://doi.org/10.1038/s41467-019-09102-3 .

Feinstein, N.W. 2018. Collective science literacy: A key to community science capacity [Conference session]. American Association for the Advancement of Science Annual Meeting, Austin, TX, USA https://d32ogoqmya1dw8.cloudfront.net/files/earthconnections/collective_science_literacy_key.pdf .

Feola, G. 2015. Societal transformation in response to global environmental change: A review of emerging concepts. Ambio 44: 376–390. https://doi.org/10.2139/ssrn.2689741 .

Fernandez-Gimenez, M.E., H.L. Ballard, and V.E. Sturtevant. 2008. Adaptive management and social learning in collaborative and community-based monitoring: A study of five community-based forestry organizations in the western USA. Ecology and Society 13: 15.

Folke, C., T. Hahn, P. Olsson, and J. Norberg. 2005. Adaptive governance of social-ecological systems. Annual Review of Environment and Resources 30: 441–473. https://doi.org/10.1146/annurev.energy.30.050504.144511 .

Freedman, D.A., K.D. Bess, H.A. Tucker, D.L. Boyd, A.M. Tuchman, and K.A. Wallston. 2009. Public health literacy defined. American Journal of Preventive Medicine 36: 446–451. https://doi.org/10.1016/j.amepre.2009.02.001 .

Freeman, R.B., and W. Huang. 2015. Collaborating with people like me: Ethnic coauthorship within the United States. Journal of Labor Economics 33: S289–S318.

Gadgil, M., F. Berkes, and C. Folke. 1993. Indigenous knowledge for biodiversity conservation. Ambio 22: 151–156.

Galaz, V., B. Crona, H. Österblom, P. Olsson, and C. Folke. 2012. Polycentric systems and interacting planetary boundaries—Emerging governance of climate change–ocean acidification–marine biodiversity. Ecological Economics 81: 21–32. https://doi.org/10.1016/j.ecolecon.2011.11.012 .

Geiger, S.M., M. Geiger, and O. Wilhelm. 2019. Environment-specific vs general knowledge and their role in pro-environmental behavior. Frontiers in Psychology 10: 718. https://doi.org/10.3389/fpsyg.2019.00718 .

Gifford, R., C. Kormos, and A. McIntyre. 2011. Behavioral dimensions of climate change: Drivers, responses, barriers, and interventions. Wires Climate Change 2: 801–827. https://doi.org/10.1002/wcc.143 .

González, N., L.C. Moll, and C. Amanti. 2006. Funds of knowledge: Theorizing practices in households, communities, and classrooms . New York: Routledge.

Gordon, D.M. 2019. Measuring collective behavior: An ecological approach. Theory in Biosciences . https://doi.org/10.1007/s12064-019-00302-5 .

Gould, R.K., N.M. Ardoin, J.M. Thomsen, and N. Wyman Roth. 2019. Exploring connections between environmental learning and behavior through four everyday-life case studies. Environmental Education Research 25: 314–340.

Graham, S., A.L. Metcalf, N. Gill, R. Niemiec, C. Moreno, T. Bach, V. Ikutegbe, L. Hallstrom, et al. 2019. Opportunities for better use of collective action theory in research and governance for invasive species management. Conservation Biology 33: 275–287. https://doi.org/10.1111/cobi.13266 .

Granovetter, M. 1978. Threshold models of collective behavior. American Journal of Sociology 83: 1420–1443.

Groulx, M., M.C. Brisbois, C.J. Lemieux, A. Winegardner, and L. Fishback. 2017. A role for nature-based citizen science in promoting individual and collective climate change action? A systematic review of learning outcomes. Science Communication 39: 45–76. https://doi.org/10.1177/1075547016688324 .

Gutiérrez, K.D., and B. Rogoff. 2003. Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher 32: 19–25. https://doi.org/10.3102/0013189X032005019 .

Guzys, D., A. Kenny, V. Dickson-Swift, and G. Threlkeld. 2015. A critical review of population health literacy assessment. BMC Public Health 15: 1–7. https://doi.org/10.1186/s12889-015-1551-6 .

Halbwachs, M. 1992. On collective memory (L. A. Coser, Ed. & Trans.). University of Chicago Press. (Original works published 1941 and 1952).

Heikkila, T., S. Villamayor-Tomas, and D. Garrick. 2018. Bringing polycentric systems into focus for environmental governance. Environmental Policy and Governance 28: 207–211. https://doi.org/10.1002/eet.1809 .

Heimlich, J.E., and N.M. Ardoin. 2008. Understanding behavior to understand behavior change: A literature review. Environmental Education Research 14: 215–237. https://doi.org/10.1080/13504620802148881 .

Hill, R., F.J. Walsh, J. Davies, A. Sparrow, M. Mooney, R.M. Wise, and M. Tengö. 2020. Knowledge co-production for Indigenous adaptation pathways: Transform post-colonial articulation complexes to empower local decision-making. Global Environmental Change 65: 102161. https://doi.org/10.1016/j.gloenvcha.2020.102161 .

Hollweg, K.S., J. Taylor, R.W. Bybee, T.J. Marcinkowski, W.C. McBeth, and P. Zoido. 2011. Developing a framework for assessing environmental literacy: Executive summary . North American Association for Environmental Education. https://cdn.naaee.org/sites/default/files/envliteracyexesummary.pdf .

Hovardas, T. 2020. A social learning approach for stakeholder engagement in large carnivore conservation and management. Frontiers in Ecology and Evolution 8: 436. https://doi.org/10.3389/fevo.2020.525278 .

Jagers, S.C., N. Harring, Å. Löfgren, M. Sjöstedt, F. Alpizar, B. Brülde, D. Langlet, A. Nilsson, et al. 2020. On the preconditions for large-scale collective action. Ambio 49: 1282–1296. https://doi.org/10.1007/s13280-019-01284-w .

Jordan, A., D. Huitema, H. van Asselt, and J. Forster. 2018. Governing climate change: Polycentricity in action? Cambridge: Cambridge University Press.

Jörg, T. 2011. New thinking in complexity for the social sciences and humanities: A generative, transdisciplinary approach . New York: Springer Science & Business Media.

Jost, J.T., J. Becker, D. Osborne, and V. Badaan. 2017. Missing in (collective) action: Ideology, system justification, and the motivational antecedents of two types of protest behavior. Current Directions in Psychological Science 26: 99–108. https://doi.org/10.1177/0963721417690633 .

Jull, J., A. Giles, and I.D. Graham. 2017. Community-based participatory research and integrated knowledge translation: Advancing the co-creation of knowledge. Implementation Science 12: 150. https://doi.org/10.1186/s13012-017-0696-3 .

Kahan, D.M., H. Jenkins-Smith, and D. Braman. 2011. Cultural cognition of scientific consensus. Journal of Risk Research 14: 147–174. https://doi.org/10.1080/13669877.2010.511246 .

Kania, J., and M. Kramer. 2011. Collective impact. Stanford Social Innovation Review 9: 36–41.

Karachiwalla, R., and F. Pinkow. 2021. Understanding crowdsourcing projects: A review on the key design elements of a crowdsourcing initiative. Creativity and Innovation Management 30: 563–584. https://doi.org/10.1111/caim.12454 .

Kellert, S.R., J.N. Mehta, S.A. Ebbin, and L.L. Lichtenfeld. 2000. Community natural resource management: Promise, rhetoric, and reality. Society & Natural Resources 13: 705–715.

Klein, J.T. 1990. Interdisciplinarity: History, theory, and practice . Detroit: Wayne State University Press.

Knapp, C.N., R.S. Reid, M.E. Fernández-Giménez, J.A. Klein, and K.A. Galvin. 2019. Placing transdisciplinarity in context: A review of approaches to connect scholars, society and action. Sustainability 11: 4899. https://doi.org/10.3390/su11184899 .

Koliou, M., J.W. van de Lindt, T.P. McAllister, B.R. Ellingwood, M. Dillard, and H. Cutler. 2020. State of the research in community resilience: Progress and challenges. Sustainable and Resilient Infrastructure 5: 131–151. https://doi.org/10.1080/23789689.2017.1418547 .

Kopnina, H. 2016. Of big hegemonies and little tigers: Ecocentrism and environmental justice. The Journal of Environmental Education 47: 139–150. https://doi.org/10.1080/00958964.2015.1048502 .

Krasny, M.E., M. Mukute, O. Aguilar, M.P. Masilela, and L. Olvitt. 2017. Community environmental education. In Urban environmental education review , ed. A. Russ and M.E. Krasny, 124–132. Ithaca: Cornell University Press.

Chapter   Google Scholar  

Lave, J. 1991. Situating learning in communities of practice.

Lave, J., and E. Wenger. 1991. Situated learning: Legitimate peripheral participation . Cambridge: Cambridge University Press.

Lee, S., and W.-M. Roth. 2003. Science and the “good citizen”: Community-based scientific literacy. Science, Technology, & Human Values 28: 403–424. https://doi.org/10.1177/0162243903028003003 .

Lévy, P., and R. Bononno. 1997. Collective intelligence: Mankind’s emerging world in cyberspace . New York: Perseus Books.

Lloyd, A. 2005. No man (or woman) is an island: Information literacy, affordances and communities of practice. The Australian Library Journal 54: 230–237. https://doi.org/10.1080/00049670.2005.10721760 .

Lopez-Gunn, E. 2003. The role of collective action in water governance: A comparative study of groundwater user associations in La Mancha aquifers in Spain. Water International 28: 367–378. https://doi.org/10.1080/02508060308691711 .

Lu, J.G., A.C. Hafenbrack, P.W. Eastwick, D.J. Wang, W.W. Maddux, and A.D. Galinsky. 2017. “Going out” of the box: Close intercultural friendships and romantic relationships spark creativity, workplace innovation, and entrepreneurship. Journal of Applied Psychology 102: 1091–1108. https://doi.org/10.1037/apl0000212 .

Lubeck, A., A. Metcalf, C. Beckman, L. Yung, and J. Angle. 2019. Collective factors drive individual invasive species control behaviors: Evidence from private lands in Montana, USA. Ecology and Society . https://doi.org/10.5751/ES-10897-240232 .

Mackay, C.M.L., M.T. Schmitt, A.E. Lutz, and J. Mendel. 2021. Recent developments in the social identity approach to the psychology of climate change. Current Opinion in Psychology 42: 95–101. https://doi.org/10.1016/j.copsyc.2021.04.009 .

Magis, K. 2010. Community resilience: An indicator of social sustainability. Society & Natural Resources 23: 401–416. https://doi.org/10.1080/08941920903305674 .

Manfredo, M.J., T.L. Teel, and A.M. Dietsch. 2016. Implications of human value shift and persistence for biodiversity conservation. Conservation Biology 30: 287–296. https://doi.org/10.1111/cobi.12619 .

Marshall, G.R., M.J. Coleman, B.M. Sindel, I.J. Reeve, and P.J. Berney. 2016. Collective action in invasive species control, and prospects for community-based governance: The case of serrated tussock ( Nassella trichotoma ) in New South Wales, Australia. Land Use Policy 56: 100–111. https://doi.org/10.1016/j.landusepol.2016.04.028 .

Mårtensson, L., and G. Hensing. 2012. Health literacy: A heterogeneous phenomenon: A literature review. Scandinavian Journal of Caring Sciences 26: 151–160. https://doi.org/10.1111/j.1471-6712.2011.00900.x .

Martin, C., and C. Steinkuehler. 2010. Collective information literacy in massively multiplayer online games. E-Learning and Digital Media 7: 355–365. https://doi.org/10.2304/elea.2010.7.4.355 .

Masson, T., and I. Fritsche. 2021. We need climate change mitigation and climate change mitigation needs the ‘We’: A state-of-the-art review of social identity effects motivating climate change action. Current Opinion in Behavioral Sciences 42: 89–96. https://doi.org/10.1016/j.cobeha.2021.04.006 .

Massung, E., D. Coyle, K.F. Cater, M. Jay, and C. Preist. 2013. Using crowdsourcing to support pro-environmental community activism. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . https://doi.org/10.1145/2470654.2470708 .

McAdam, D. 2017. Social movement theory and the prospects for climate change activism in the United States. Annual Review of Political Science 20: 189–208. https://doi.org/10.1146/annurev-polisci-052615-025801 .

McAdam, D., and H. Boudet. 2012. Putting social movements in their place: Explaining opposition to energy projects in the United States, 2000–2005 . Cambridge University Press.

McKenzie-Mohr, D. 2011. Fostering sustainable behavior: An introduction to community-based social marketing (3rd edn.). New Society Publishers.

McKinley, D.C., A.J. Miller-Rushing, H.L. Ballard, R. Bonney, H. Brown, S.C. Cook-Patton, D.M. Evans, R.A. French, et al. 2017. Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation 208: 15–28.

Miller, D.L. 2014. Introduction to collective behavior and collective action (3rd ed.). Waveland Press.

Mills, J., D. Gibbon, J. Ingram, M. Reed, C. Short, and J. Dwyer. 2011. Organising collective action for effective environmental management and social learning in Wales. The Journal of Agricultural Education and Extension 17: 69–83. https://doi.org/10.1080/1389224X.2011.536356 .

Mistry, J., and A. Berardi. 2016. Bridging Indigenous and scientific knowledge. Science 352: 1274–1275. https://doi.org/10.1126/science.aaf1160 .

Mochizuki, Y., and A. Bryan. 2015. Climate change education in the context of education for sustainable development: Rationale and principles. Journal of Education for Sustainable Development 9: 4–26. https://doi.org/10.1177/0973408215569109 .

Monroe, M.C. 2003. Two avenues for encouraging conservation behaviors. Human Ecology Review 10: 113–125.

Nasir, N.S., M.M. de Royston, B. Barron, P. Bell, R. Pea, R. Stevens, and S. Goldman. 2020. Learning pathways: How learning is culturally organized. In Handbook of the cultural foundations of learning , ed. N.S. Nasir, C.D. Lee, R. Pea, and M.M. de Royston, 195–211. Routledge.

National Academies of Sciences, Engineering, and Medicine. 2016. Science literacy: Concepts, contexts, and consequences . https://doi.org/10.17226/23595

National Research Council. 2015. Collective behavior: From cells to societies: Interdisciplinary research team summaries . National Academies Press. https://doi.org/10.17226/21737

Niemiec, R.M., N.M. Ardoin, C.B. Wharton, and G.P. Asner G.P. 2016. Motivating residents to combat invasive species on private lands: Social norms and community reciprocity. Ecology and Society , 21. https://doi.org/10.5751/ES-08362-210230

Niemiec, R.M., S. McCaffrey, and M.S. Jones. 2020. Clarifying the degree and type of public good collective action problem posed by natural resource management challenges. Ecology and Society 25: 30. https://doi.org/10.5751/ES-11483-250130 .

Norström, A.V., C. Cvitanovic, M.F. Löf, S. West, C. Wyborn, P. Balvanera, A.T. Bednarek, E.M. Bennett, et al. 2020. Principles for knowledge co-production in sustainability research. Nature Sustainability 3: 182–190. https://doi.org/10.1038/s41893-019-0448-2 .

Olick, J.K. 1999. Collective memory: The two cultures. Sociological Theory 17: 333–348. https://doi.org/10.1111/0735-2751.00083 .

Ostrom, E. 1990. Governing the commons: The evolution of institutions for collective action . Cambridge University Press.

Ostrom, E. 2000. Collective action and the evolution of social norms. Journal of Economic Perspectives 14: 137–158. https://doi.org/10.1257/jep.14.3.137 .

Ostrom, E. 2009. A general framework for analyzing sustainability of social-ecological systems. Science 325: 419–422. https://doi.org/10.1126/science.1172133 .

Ostrom, E. 2010. Polycentric systems for coping with collective action and global environmental change. Global Environmental Change 20: 550–557. https://doi.org/10.1016/j.gloenvcha.2010.07.004 .

Ostrom, E. 2012. Nested externalities and polycentric institutions: Must we wait for global solutions to climate change before taking actions at other scales? Economic Theory 49: 353–369. https://doi.org/10.1007/s00199-010-0558-6 .

Ostrom, E., and T.K. Ahn. 2009. The meaning of social capital and its link to collective action. In Handbook of social capital: The troika of sociology, political science and economics , ed. G.T. Svendsen and G.L.H. Svendsen, 17–35. Edward Elgar Publishing.

Papen, U. 2009. Literacy, learning and health: A social practices view of health literacy. Literacy and Numeracy Studies . https://doi.org/10.5130/lns.v0i0.1275 .

Park, R.E. 1927. Human nature and collective behavior. American Journal of Sociology 32: 733–741.

Paul, A.M. 2021. The extended mind: The power of thinking outside the brain . Boston: Mariner Books.

Pawilen, G.T. 2021. Integrating Indigenous knowledge in the Philippine elementary science curriculum: Integrating Indigenous knowledge. International Journal of Curriculum and Instruction 13: 1148–1160.

Prager, K. 2015. Agri-environmental collaboratives for landscape management in Europe. Current Opinion in Environmental Sustainability 12: 59–66. https://doi.org/10.1016/j.cosust.2014.10.009 .

Pretty, J., and H. Ward. 2001. Social capital and the environment. World Development 29: 209–227. https://doi.org/10.1016/S0305-750X(00)00098-X .

Putnam, R.D. 2020. Bowling alone: Revised and updated: The collapse and revival of American community . Anniversary. New York: Simon & Schuster.

Raymond, L. 2006. Cooperation without trust: Overcoming collective action barriers to endangered species protection. Policy Studies Journal 34: 37–57. https://doi.org/10.1111/j.1541-0072.2006.00144.x .

Reed, M.S., A.C. Evely, G. Cundill, I. Fazey, J. Glass, A. Laing, J. Newig, B. Parrish, et al. 2010. What is social learning? Ecology and Society 15: 12.

Reicher, S., R. Spears, and S.A. Haslam. 2010. The social identity approach in social psychology. In The SAGE handbook of identities (pp. 45–62). SAGE. https://doi.org/10.4135/9781446200889

Reid, A. 2019. Blank, blind, bald and bright spots in environmental education research. Environmental Education Research 25: 157–171. https://doi.org/10.1080/13504622.2019.1615735 .

Rogoff, B. 2003. The cultural nature of human development (Reprint edition) . Oxford: Oxford University Press.

Roth, C.E. 1992. Environmental literacy: Its roots, evolution and directions in the 1990s . http://eric.ed.gov/?id=ED348235

Roth, W.-M. 2003. Scientific literacy as an emergent feature of collective human praxis. Journal of Curriculum Studies 35: 9–23. https://doi.org/10.1080/00220270210134600 .

Roth, W.-M., and A.C. Barton. 2004. Rethinking scientific literacy . London: Psychology Press.

Roth, W.-M., and S. Lee. 2002. Scientific literacy as collective praxis. Public Understanding of Science 11: 33–56. https://doi.org/10.1088/0963-6625/11/1/302 .

Roth, W.-M., and S. Lee. 2004. Science education as/for participation in the community. Science Education 88: 263–291.

Roth, W.-M., and Y.-J. Lee. 2007. “Vygotsky’s neglected legacy”: Cultural-historical activity theory. Review of Educational Research 77: 186–232.

Sadoff, C.W., and D. Grey. 2005. Cooperation on international rivers: A continuum for securing and sharing benefits. Water International 30: 420–427.

Samerski, S. 2019. Health literacy as a social practice: Social and empirical dimensions of knowledge on health and healthcare. Social Science & Medicine 226: 1–8. https://doi.org/10.1016/j.socscimed.2019.02.024 .

Sawyer, R.K. 2014. The future of learning: Grounding educational innovation in the learning sciences. In The Cambridge handbook of the learning sciences , ed. R.K. Sawyer, 726–746. Cambridge: Cambridge University Press.

Saxe, J.G. n.d.. The blind man and the elephant . All Poetry. Retrieved October 6, 2020, from https://allpoetry.com/The-Blind-Man-And-The-Elephant .

Scheepers, D., and N. Ellemers. 2019. Social identity theory. In Social psychology in action: Evidence-based interventions from theory to practice , ed. K. Sassenberg and M.L.W. Vliek, 129–143. New York: Springer International Publishing.

Schipper, E.L.F., N.K. Dubash, and Y. Mulugetta. 2021. Climate change research and the search for solutions: Rethinking interdisciplinarity. Climatic Change 168: 18. https://doi.org/10.1007/s10584-021-03237-3 .

Schoerning, E. 2018. A no-conflict approach to informal science education increases community science literacy and engagement. Journal of Science Communication, Doi 10: 17030205.

Schultz, P.W. 2014. Strategies for promoting proenvironmental behavior: Lots of tools but few instructions. European Psychologist 19: 107–117. https://doi.org/10.1027/1016-9040/a000163 .

Sharifi, A. 2016. A critical review of selected tools for assessing community resilience. Ecological Indicators 69: 629–647. https://doi.org/10.1016/j.ecolind.2016.05.023 .

Sherrieb, K., F.H. Norris, and S. Galea. 2010. Measuring capacities for community resilience. Social Indicators Research 99: 227–247. https://doi.org/10.1007/s11205-010-9576-9 .

Singh, R.K., A. Singh, K.K. Zander, S. Mathew, and A. Kumar. 2021. Measuring successful processes of knowledge co-production for managing climate change and associated environmental stressors: Adaptation policies and practices to support Indian farmers. Journal of Environmental Management 282: 111679. https://doi.org/10.1016/j.jenvman.2020.111679 .

Sloman, S., and P. Fernbach. 2017. The knowledge illusion: Why we never think alone . New York: Riverhead Books.

Smelser, N.J. 2011. Theory of collective behavior . Quid Pro Books. (Original work published 1962).

Sørensen, K., S. Van den Broucke, J. Fullam, G. Doyle, J. Pelikan, Z. Slonska, H. Brand, and (HLS-EU) Consortium Health Literacy Project European. 2012. Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health 12: 80. https://doi.org/10.1186/1471-2458-12-80 .

Spitzer, W., and J. Fraser. 2020. Advancing community science literacy. Journal of Museum Education 45: 5–15. https://doi.org/10.1080/10598650.2020.1720403 .

Stables, A., and K. Bishop. 2001. Weak and strong conceptions of environmental literacy: Implications for environmental education. Environmental Education Research 7: 89. https://doi.org/10.1080/13504620125643 .

Stern, M.J., R.B. Powell, and N.M. Ardoin. 2008. What difference does it make? Assessing outcomes from participation in a residential environmental education program. The Journal of Environmental Education 39: 31–43. https://doi.org/10.3200/JOEE.39.4.31-43 .

Stets, J.E., and P.J. Burke. 2000. Identity theory and social identity theory. Social Psychology Quarterly 63: 224–237. https://doi.org/10.2307/2695870 .

Sturmer, S., and B. Simon. 2004. Collective action: Towards a dual-pathway model. European Review of Social Psychology 15: 59–99. https://doi.org/10.1080/10463280340000117 .

Sullivan, A., A. York, D. White, S. Hall, and S. Yabiku. 2017. De jure versus de facto institutions: Trust, information, and collective efforts to manage the invasive mile-a-minute weed (Mikania micrantha). International Journal of the Commons 11: 171–199. https://doi.org/10.18352/ijc.676 .

Sunstein, C.R. 2008. Infotopia: How many minds produce knowledge . Oxford: Oxford University Press.

Surowiecki, J. 2005. The wisdom of crowds . New York: Anchor.

Swim, J.K., S. Clayton, and G.S. Howard. 2011. Human behavioral contributions to climate change: Psychological and contextual drivers. American Psychologist 66: 251–264.

Thaker, J., P. Howe, A. Leiserowitz, and E. Maibach. 2019. Perceived collective efficacy and trust in government influence public engagement with climate change-related water conservation policies. Environmental Communication 13: 681–699. https://doi.org/10.1080/17524032.2018.1438302 .

Tudge, J.R.H., and P.A. Winterhoff. 1993. Vygotsky, Piaget, and Bandura: Perspectives on the relations between the social world and cognitive development. Human Development 36: 61–81. https://doi.org/10.1159/000277297 .

Turner, R.H., and L.M. Killian. 1987. Collective behavior , 3rd ed. Englewood Cliffs: Prentice Hall.

Turner, R.H., N.J. Smelser, and L.M. Killian. 2020. Collective behaviour. In Encyclopedia Britannica . Encyclopedia Britannica, Inc. https://www.britannica.com/science/collective-behaviour .

van der Linden, S. 2014. Towards a new model for communicating climate change. In Understanding and governing sustainable tourism mobility , ed. S. Cohen, J. Higham, P. Peeters, and S. Gössling, 263–295. Milton Park: Routledge.

van Zomeren, M., T. Postmes, and R. Spears. 2008. Toward an integrative social identity model of collective action: A quantitative research synthesis of three socio-psychological perspectives. Psychological Bulletin 134: 504–535. https://doi.org/10.1037/0033-2909.134.4.504 .

Vygotsky, L.S. 1980. Mind in society: The development of higher psychological processes . Cambridge: Harvard University Press.

Waldron, F., B. Ruane, R. Oberman, and S. Morris. 2019. Geographical process or global injustice? Contrasting educational perspectives on climate change. Environmental Education Research 25: 895–911. https://doi.org/10.1080/13504622.2016.1255876 .

Wals, A.E.J., M. Brody, J. Dillon, and R.B. Stevenson. 2014. Convergence between science and environmental education. Science 344: 583–584.

Wenger, E.C., and W.M. Snyder. 2000. Communities of practice: The organizational frontier. Harvard Business Review 78: 139–146.

Weschsler, D. 1971. Concept of collective intelligence. American Psychologist 26: 904–907. https://doi.org/10.1037/h0032223 .

Wheaton, M., A. Kannan, and N.M. Ardoin. 2018. Environmental literacy: Setting the stage (Environmental Literacy Brief, Vol. 1). Social Ecology Lab, Stanford University. https://ed.stanford.edu/sites/default/files/news/images/stanfordsocialecologylab-brief-1.pdf .

Wojcik, D.J., N.M. Ardoin, and R.K. Gould. 2021. Using social network analysis to explore and expand our understanding of a robust environmental learning landscape. Environmental Education Research 27: 1263–1283.

Wood, W., and D. Rünger. 2016. Psychology of habit. Annual Review of Psychology 67: 289–314. https://doi.org/10.1146/annurev-psych-122414-033417 .

Woolley, A.W., C.F. Chabris, A. Pentland, N. Hashmi, and T.W. Malone. 2010. Evidence for a collective intelligence factor in the performance of human groups. Science 330: 686–688. https://doi.org/10.1126/science.1193147 .

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Acknowledgements

We are grateful to Maria DiGiano, Anna Lee, and Becca Shareff for their feedback and contributions to early drafts of this paper. We appreciate the research and writing assistance supporting this paper provided by various members of the Stanford Social Ecology Lab, especially: Brennecke Gale, Pari Ghorbani, Regina Kong, Naomi Ray, and Austin Stack.

This work was supported by a grant from the Pisces Foundation.

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Ardoin, N.M., Bowers, A.W. & Wheaton, M. Leveraging collective action and environmental literacy to address complex sustainability challenges. Ambio 52 , 30–44 (2023). https://doi.org/10.1007/s13280-022-01764-6

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DOI : https://doi.org/10.1007/s13280-022-01764-6

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Student airplane accessibility project named finalist for international award

• Lindsey Byars

20 May 2024

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Susan Sale and Mikayla Kaczmar, recent Virginia Tech industrial design graduates from the College of Architecture, Arts, and Design , will travel to Hamburg, Germany, this week to represent their team from the Calhoun Honors Discovery Program and the United States in the university division of Hamburg Aviation’s Crystal Cabin Awards .

The award recognizes exceptional aircraft cabin products and concepts that elevate the passenger’s experience. For passengers who use a wheelchair, Virginia Tech’s project could be life changing.

Chairs in the Air is a collaboration between Virginia Tech’s student team, Boeing, All Wheels Up , and Collins Aerospace. The team’s Wheelchair Space and Securement System concept would allow passengers with limited mobility to sit securely in their own wheelchairs during flight, reducing the risk of injury and protecting their chairs from damage during stowing.

“Our design is pretty simple,” said Kaczmar. “It’s a retrofittable pallet design you put right into an aircraft that involves three economy class chairs. All three chairs fold up, you can wheel the chair into a built-in securement system that locks the wheelchair into place, and the outside chair can fold back down to allow for a passenger or mobility helper to sit.”

“The general concept of the design is that a wheelchair user can remain in their personal wheelchair for the duration of their flight — from booking to boarding to beyond,” said Sale.

Keeping a wheelchair with its passenger protects both the person and the essential device. Wheelchairs are costly to fix, and if damage is extensive or the stowed chair is sent to the wrong location, the person will not be mobile when they reach the destination.   

For travelers with neuromuscular diseases, being separated from their chairs for extended time can be life threatening. Sale recalls two wheelchair-on-airplane deaths when team members started their project. Existing airplane seats don’t include the harnesses and other safey gear that may be essential for passengers who use wheelchairs, so another traveler must be there to hold the passenger’s body up during take-off, turbulence, and landing.

“This is a really big issue for wheelchair users that they have to be separated from their chairs when they fly,” said Sale. “It’s dangerous and painful, not to mention undignified for them to be moved around the aircraft. It’s not how we should be treating people.”

Kaczmar and Sale pioneered this project for the past two years, conducting research with industry partners and listening to stories from many wheelchair users. However, the idea for a team to address airplane accessibility came from Juliana Iacono during her first year at Virginia Tech.

Iacono’s family, who are avid travelers, have been flying with her wheelchair-dependent brother for many years. As he got older, navigating airplanes became more difficult. Based on these experiences, Iacono developed a passon for accessible air travel. She presented the problem with aircraft accessibility to a cohort of students to see if others would be interested in forming a team. Shortly after, Chairs in the Air came to fruition.

“I gave them a lot of insight in meetings that influenced design ideas, helping them also think about the person who is traveling with the person in a chair and what the full experience is on the plane from beginning to end,” said Iacono.

Now in her third year as an industrial design major and a member of the Chairs in the Air team, Iacono is thrilled to see the project come full circle.

“As product designers, it’s rewarding to be able to help create products and make changes that hopefully make the world a better place and make people’s lives easier,” said Iacono.

Ellen Braaten , assistant professor emerita in the School of Architecture , has worked closely with team members for the past two years, using her electric wheelchair to test out prototypes team members created and to help them understand how difficult air travel is in a chair. For Braaten, airlines implementing this system would mean she could fly for the first time in decades.

“I am a polio survivor from 1947. I walked on crutches for 50 years, flew all over the world, and when I had to get into a wheelchair, that was no longer a possibility,” said Braaten, who added that air travel with a wheelchair became a demeaning process that involved being carried in on a gurney and dropped into a seat.

“These kids were so involved and so understanding,” said Braaten. “We could talk about the issues that I’m confronted with, and in the long run, it’s going to be good for everyone by allowing people to have the freedom that they need.”

Braaten is proud of the team’s accomplishments and adds that their recognition as a finalist in the Crystal Cabin Award makes her feel hope that one day she’ll be able to fly again, allowing her to visit her daughter more often.

On May 28, the winners will be announced in Hamburg, Germany. Supported by funding from the Virginia Tech Honors College and the School of Design, Sale and Kaczmar will be in attendance to present their project. They hope industry experts attending the event will be inspired to help make air travel more accessible.

The U.S. Department of Transportation announced a new rule in 2023 requiring airlines to make lavatories on new single-aisle aircraft large enough to permit a passenger with a disability and attendant. Both Sale and Kaczmar see potential for the legislature to continue working toward better accessibility.

“On the issue of improving accessibility on aircraft there’s still work to be done for it to be a legislative requirement, so I think it’s good for people to keep this in mind in the future,” said Sale. “This is an issue that they can influence with their vote.”

Zeke Barlow

540-231-5417

• Accessibility
• College of Architecture, Arts, and Design
• Emeritus and Emerita Faculty
• Honors College
• Industrial Design
• School of Architecture

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