variables in a research paper

Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable?

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

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What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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Independent and Dependent Variables

This guide discusses how to identify independent and dependent variables effectively and incorporate their description within the body of a research paper.

A variable can be anything you might aim to measure in your study, whether in the form of numerical data or reflecting complex phenomena such as feelings or reactions. Dependent variables change due to the other factors measured, especially if a study employs an experimental or semi-experimental design. Independent variables are stable: they are both presumed causes and conditions in the environment or milieu being manipulated.

Identifying Independent and Dependent Variables

Even though the definitions of the terms independent and dependent variables may appear to be clear, in the process of analyzing data resulting from actual research, identifying the variables properly might be challenging. Here is a simple rule that you can apply at all times: the independent variable is what a researcher changes, whereas the dependent variable is affected by these changes. To illustrate the difference, a number of examples are provided below.

• The purpose of Study 1 is to measure the impact of different plant fertilizers on how many fruits apple trees bear. Independent variable : plant fertilizers (chosen by researchers) Dependent variable : fruits that the trees bear (affected by choice of fertilizers)
• The purpose of Study 2 is to find an association between living in close vicinity to hydraulic fracturing sites and respiratory diseases. Independent variable: proximity to hydraulic fracturing sites (a presumed cause and a condition of the environment) Dependent variable: the percentage/ likelihood of suffering from respiratory diseases

Confusion is possible in identifying independent and dependent variables in the social sciences. When considering psychological phenomena and human behavior, it can be difficult to distinguish between cause and effect. For example, the purpose of Study 3 is to establish how tactics for coping with stress are linked to the level of stress-resilience in college students. Even though it is feasible to speculate that these variables are interdependent, the following factors should be taken into account in order to clearly define which variable is dependent and which is interdependent.

• The dependent variable is usually the objective of the research. In the study under examination, the levels of stress resilience are being investigated.
• The independent variable precedes the dependent variable. The chosen stress-related coping techniques help to build resilience; thus, they occur earlier.

Writing Style and Structure

Usually, the variables are first described in the introduction of a research paper and then in the method section. No strict guidelines for approaching the subject exist; however, academic writing demands that the researcher make clear and concise statements. It is only reasonable not to leave readers guessing which of the variables is dependent and which is independent. The description should reflect the literature review, where both types of variables are identified in the context of the previous research. For instance, in the case of Study 3, a researcher would have to provide an explanation as to the meaning of stress resilience and coping tactics.

In properly organizing a research paper, it is essential to outline and operationalize the appropriate independent and dependent variables. Moreover, the paper should differentiate clearly between independent and dependent variables. Finding the dependent variable is typically the objective of a study, whereas independent variables reflect influencing factors that can be manipulated. Distinguishing between the two types of variables in social sciences may be somewhat challenging as it can be easy to confuse cause with effect. Academic format calls for the author to mention the variables in the introduction and then provide a detailed description in the method section.

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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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Types of Variables in Research | Definitions & Examples

Published on 19 September 2022 by Rebecca Bevans . Revised on 28 November 2022.

In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts.
• Categorical data represents groupings.

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variable can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables.

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is colour-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms ‘dependent’ and ‘independent’ don’t apply, because you are not trying to establish a cause-and-effect relationship.

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases, you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e., the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variable are listed below.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

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Educational Research Basics by Del Siegle

Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).

OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .

If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .

If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).

QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative.  QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.

QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables

A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).

Variables have different purposes or roles…

Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)

While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.

Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.

The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.

Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.

The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).

Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.

Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).

With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.

If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language:   Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).

Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.

Here are some examples similar to your homework:

Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read

High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable:  Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.

We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

Research Questions and Hypotheses

The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement  that a relationship does not exist or a difference does not exist and we have the null hypothesis.

Format for sample research questions and accompanying hypotheses:

Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis:  There is no relationship between height and weight. Alternative Hypothesis:   There is a relationship between height and weight.

When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better.  Most researchers use nondirectional hypotheses.

We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).

Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis:   Boys do not like reading more than girls. Alternative Hypothesis:   Boys do like reading more than girls.

Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis:   There is no difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis:   There is a difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading differ.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Types of variables and commonly used statistical designs.

Jacob Shreffler ; Martin R. Huecker .

Affiliations

Last Update: March 6, 2023 .

• Definition/Introduction

Suitable statistical design represents a critical factor in permitting inferences from any research or scientific study. [1]  Numerous statistical designs are implementable due to the advancement of software available for extensive data analysis. [1]  Healthcare providers must possess some statistical knowledge to interpret new studies and provide up-to-date patient care. We present an overview of the types of variables and commonly used designs to facilitate this understanding. [2]

• Issues of Concern

Individuals who attempt to conduct research and choose an inappropriate design could select a faulty test and make flawed conclusions. This decision could lead to work being rejected for publication or (worse) lead to erroneous clinical decision-making, resulting in unsafe practice. [1]  By understanding the types of variables and choosing tests that are appropriate to the data, individuals can draw appropriate conclusions and promote their work for an application. [3]

To determine which statistical design is appropriate for the data and research plan, one must first examine the scales of each measurement. [4]  Multiple types of variables determine the appropriate design.

Ordinal data (also sometimes referred to as discrete) provide ranks and thus levels of degree between the measurement. [5]  Likert items can serve as ordinal variables, but the Likert scale, the result of adding all the times, can be treated as a continuous variable. [6]  For example, on a 20-item scale with each item ranging from 1 to 5, the item itself can be an ordinal variable, whereas if you add up all items, it could result in a range from 20 to 100. A general guideline for determining if a variable is ordinal vs. continuous: if the variable has more than ten options, it can be treated as a continuous variable. [7]  The following examples are ordinal variables:

• Likert items
• Cancer stages
• Residency Year

Nominal, Categorical, Dichotomous, Binary

Other types of variables have interchangeable terms. Nominal and categorical variables describe samples in groups based on counts that fall within each category, have no quantitative relationships, and cannot be ranked. [8]  Examples of these variables include:

• Service (i.e., emergency, internal medicine, psychiatry, etc.)
• Mode of Arrival (ambulance, helicopter, car)

A dichotomous or a binary variable is in the same family as nominal/categorical, but this type has only two options. Binary logistic regression, which will be discussed below, has two options for the outcome of interest/analysis. Often used as (yes/no), examples of dichotomous or binary variables would be:

• Alive (yes vs. no)
• Insurance (yes vs. no)
• Readmitted (yes vs. no)

With this overview of the types of variables provided, we will present commonly used statistical designs for different scales of measurement. Importantly, before deciding on a statistical test, individuals should perform exploratory data analysis to ensure there are no issues with the data and consider type I, type II errors, and power analysis. Furthermore, investigators should ensure appropriate statistical assumptions. [9] [10]  For example, parametric tests, including some discussed below (t-tests, analysis of variance (ANOVA), correlation, and regression), require the data to have a normal distribution and that the variances within each group are similar. [6] [11]  After eliminating any issues based on exploratory data analysis and reducing the likelihood of committing type I and type II errors, a statistical test can be chosen. Below is a brief introduction to each of the commonly used statistical designs with examples of each type. An example of one research focus, with each type of statistical design discussed, can be found in Table 1 to provide more examples of commonly used statistical designs. 

Commonly Used Statistical Designs

Independent Samples T-test

An independent samples t-test allows a comparison of two groups of subjects on one (continuous) variable. Examples in biomedical research include comparing results of treatment vs. control group and comparing differences based on gender (male vs. female).

Example: Does adherence to the ketogenic diet (yes/no; two groups) have a differential effect on total sleep time (minutes; continuous)?

Paired T-test

A paired t-test analyzes one sample population, measuring the same variable on two different occasions; this is often useful for intervention and educational research.

Example :  Does participating in a research curriculum (one group with intervention) improve resident performance on a test to measure research competence (continuous)?

One-Way Analysis of Variance (ANOVA)

Analysis of variance (ANOVA), as an extension of the t-test, determines differences amongst more than two groups, or independent variables based on a dependent variable. [11]  ANOVA is preferable to conducting multiple t-tests as it reduces the likelihood of committing a type I error.

Example: Are there differences in length of stay in the hospital (continuous) based on the mode of arrival (car, ambulance, helicopter, three groups)?

Repeated Measures ANOVA

Another procedure commonly used if the data for individuals are recurrent (repeatedly measured) is a repeated-measures ANOVA. [1]  In these studies, multiple measurements of the dependent variable are collected from the study participants. [11]  A within-subjects repeated measures ANOVA determines effects based on the treatment variable alone, whereas mixed ANOVAs allow both between-group effects and within-subjects to be considered.

Within-Subjects Example: How does ketamine effect mean arterial pressure (continuous variable) over time (repeated measurement)?

Mixed Example: Does mean arterial pressure (continuous) differ between males and females (two groups; mixed) on ketamine throughout a surgical procedure (over time; repeated measurement)?  

Nonparametric Tests

Nonparametric tests, such as the Mann-Whitney U test (two groups; nonparametric t-test), Kruskal Wallis test (multiple groups; nonparametric ANOVA), Spearman’s rho (nonparametric correlation coefficient) can be used when data are ordinal or lack normality. [3] [5]  Not requiring normality means that these tests allow skewed data to be analyzed; they require the meeting of fewer assumptions. [11]

Example: Is there a relationship between insurance status (two groups) and cancer stage (ordinal)?  

A Chi-square test determines the effect of relationships between categorical variables, which determines frequencies and proportions into which these variables fall. [11]  Similar to other tests discussed, variants and extensions of the chi-square test (e.g., Fisher’s exact test, McNemar’s test) may be suitable depending on the variables. [8]

Example: Is there a relationship between individuals with methamphetamine in their system (yes vs. no; dichotomous) and gender (male or female; dichotomous)?

Correlation

Correlations (used interchangeably with ‘associations’) signal patterns in data between variables. [1]  A positive association occurs if values in one variable increase as values in another also increase. A negative association occurs if variables in one decrease while others increase. A correlation coefficient, expressed as r,  describes the strength of the relationship: a value of 0 means no relationship, and the relationship strengthens as r approaches 1 (positive relationship) or -1 (negative association). [5]

Example: Is there a relationship between age (continuous) and satisfaction with life survey scores (continuous)?

Linear Regression

Regression allows researchers to determine the degrees of relationships between a dependent variable and independent variables and results in an equation for prediction. [11]  A large number of variables are usable in regression methods.

Example: Which admission to the hospital metrics (multiple continuous) best predict the total length of stay (minutes; continuous)?

Binary Logistic Regression

This type of regression, which aims to predict an outcome, is appropriate when the dependent variable or outcome of interest is binary or dichotomous (yes/no; cured/not cured). [12]

Example: Which panel results (multiple of continuous, ordinal, categorical, dichotomous) best predict whether or not an individual will have a positive blood culture (dichotomous/binary)?

The table provides more examples of commonly used statistical designs by providing an example of one research focus and discussing each type of statistical design (see Table. Types of Variables and Statistical Designs).

• Clinical Significance

Though numerous other statistical designs and extensions of methods covered in this article exist, the above information provides a starting point for healthcare providers to become acquainted with variables and commonly used designs. Researchers should study types of variables before determining statistical tests to obtain relevant measures and valid study results. [6]  There is a recommendation to consult a statistician to ensure appropriate usage of the statistical design based on the variables and that the assumptions are upheld. [1]  With the variety of statistical software available, investigators must a priori understand the type of statistical tests when designing a study. [13]  All providers must interpret and scrutinize journal publications to make evidence-based clinical decisions, and this becomes enhanced by a limited but sound understanding of variables and commonly used study designs. [14]

• Nursing, Allied Health, and Interprofessional Team Interventions

All interprofessional healthcare team members need to be familiar with study design and the variables used in studies to accurately evaluate new data and studies as they are published and apply the latest data to patient care and drive optimal outcomes.

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Types of Variables and Statistical Designs.  Contributed by M Huecker, MD, and J Shreffler, PhD

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Types of Variables and Commonly Used Statistical Designs. [Updated 2023 Mar 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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15 Independent and Dependent Variable Examples

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

An independent variable (IV) is what is manipulated in a scientific experiment to determine its effect on the dependent variable (DV).

By varying the level of the independent variable and observing associated changes in the dependent variable, a researcher can conclude whether the independent variable affects the dependent variable or not.

This can provide very valuable information when studying just about any subject.

Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.

The term causation is vitally important. Scientists want to know what causes changes in the dependent variable. The only way to do that is to manipulate the independent variable and observe any changes in the dependent variable.

Definition of Independent and Dependent Variables

The independent variable and dependent variable are used in a very specific type of scientific study called the experiment .

Although there are many variations of the experiment, generally speaking, it involves either the presence or absence of the independent variable and the observation of what happens to the dependent variable.

The research participants are randomly assigned to either receive the independent variable (called the treatment condition), or not receive the independent variable (called the control condition).

Other variations of an experiment might include having multiple levels of the independent variable.

If the independent variable affects the dependent variable, then it should be possible to observe changes in the dependent variable based on the presence or absence of the independent variable.  

Of course, there are a lot of issues to consider when conducting an experiment, but these are the basic principles.

These concepts should not be confused with predictor and outcome variables .

Examples of Independent and Dependent Variables

1. gatorade and improved athletic performance.

A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.

If they can back up that claim with hard scientific data, that would be great for sales.

So, the researcher goes to a nearby university and randomly selects both male and female athletes from several sports: track and field, volleyball, basketball, and football. Each athlete will run on a treadmill for one hour while their heart rate is tracked.

All of the athletes are given the exact same amount of liquid to consume 30-minutes before and during their run. Half are given Gatorade, and the other half are given water, but no one knows what they are given because both liquids have been colored.

In this example, the independent variable is Gatorade, and the dependent variable is heart rate.  

2. Chemotherapy and Cancer

A hospital is investigating the effectiveness of a new type of chemotherapy on cancer. The researchers identified 120 patients with relatively similar types of cancerous tumors in both size and stage of progression.

The patients are randomly assigned to one of three groups: one group receives no chemotherapy, one group receives a low dose of chemotherapy, and one group receives a high dose of chemotherapy.

Each group receives chemotherapy treatment three times a week for two months, except for the no-treatment group. At the end of two months, the doctors measure the size of each patient’s tumor.

In this study, despite the ethical issues (remember this is just a hypothetical example), the independent variable is chemotherapy, and the dependent variable is tumor size.

3. Interior Design Color and Eating Rate

A well-known fast-food corporation wants to know if the color of the interior of their restaurants will affect how fast people eat. Of course, they would prefer that consumers enter and exit quickly to increase sales volume and profit.

So, they rent space in a large shopping mall and create three different simulated restaurant interiors of different colors. One room is painted mostly white with red trim and seats; one room is painted mostly white with blue trim and seats; and one room is painted mostly white with off-white trim and seats.

Next, they randomly select shoppers on Saturdays and Sundays to eat for free in one of the three rooms. Each shopper is given a box of the same food and drink items and sent to one of the rooms. The researchers record how much time elapses from the moment they enter the room to the moment they leave.

The independent variable is the color of the room, and the dependent variable is the amount of time spent in the room eating.

4. Hair Color and Attraction

A large multinational cosmetics company wants to know if the color of a woman’s hair affects the level of perceived attractiveness in males. So, they use Photoshop to manipulate the same image of a female by altering the color of her hair: blonde, brunette, red, and brown.

Next, they randomly select university males to enter their testing facilities. Each participant sits in front of a computer screen and responds to questions on a survey. At the end of the survey, the screen shows one of the photos of the female.

At the same time, software on the computer that utilizes the computer’s camera is measuring each male’s pupil dilation. The researchers believe that larger dilation indicates greater perceived attractiveness.

The independent variable is hair color, and the dependent variable is pupil dilation.

5. Mozart and Math

After many claims that listening to Mozart will make you smarter, a group of education specialists decides to put it to the test. So, first, they go to a nearby school in a middle-class neighborhood.

During the first three months of the academic year, they randomly select some 5th-grade classrooms to listen to Mozart during their lessons and exams. Other 5 th grade classrooms will not listen to any music during their lessons and exams.

The researchers then compare the scores of the exams between the two groups of classrooms.

Although there are a lot of obvious limitations to this hypothetical, it is the first step.

The independent variable is Mozart, and the dependent variable is exam scores.

6. Essential Oils and Sleep

A company that specializes in essential oils wants to examine the effects of lavender on sleep quality. They hire a sleep research lab to conduct the study. The researchers at the lab have their usual test volunteers sleep in individual rooms every night for one week.

The conditions of each room are all exactly the same, except that half of the rooms have lavender released into the rooms and half do not. While the study participants are sleeping, their heart rates and amount of time spent in deep sleep are recorded with high-tech equipment.

At the end of the study, the researchers compare the total amount of time spent in deep sleep of the lavender-room participants with the no lavender-room participants.

The independent variable in this sleep study is lavender, and the dependent variable is the total amount of time spent in deep sleep.

7. Teaching Style and Learning

A group of teachers is interested in which teaching method will work best for developing critical thinking skills.

So, they train a group of teachers in three different teaching styles : teacher-centered, where the teacher tells the students all about critical thinking; student-centered, where the students practice critical thinking and receive teacher feedback; and AI-assisted teaching, where the teacher uses a special software program to teach critical thinking.

At the end of three months, all the students take the same test that assesses critical thinking skills. The teachers then compare the scores of each of the three groups of students.

The independent variable is the teaching method, and the dependent variable is performance on the critical thinking test.

8. Concrete Mix and Bridge Strength

A chemicals company has developed three different versions of their concrete mix. Each version contains a different blend of specially developed chemicals. The company wants to know which version is the strongest.

So, they create three bridge molds that are identical in every way. They fill each mold with one of the different concrete mixtures. Next, they test the strength of each bridge by placing progressively more weight on its center until the bridge collapses.

In this study, the independent variable is the concrete mixture, and the dependent variable is the amount of weight at collapse.

9. Recipe and Consumer Preferences

People in the pizza business know that the crust is key. Many companies, large and small, will keep their recipe a top secret. Before rolling out a new type of crust, the company decides to conduct some research on consumer preferences.

The company has prepared three versions of their crust that vary in crunchiness, they are: a little crunchy, very crunchy, and super crunchy. They already have a pool of consumers that fit their customer profile and they often use them for testing.

Each participant sits in a booth and takes a bite of one version of the crust. They then indicate how much they liked it by pressing one of 5 buttons: didn’t like at all, liked, somewhat liked, liked very much, loved it.

The independent variable is the level of crust crunchiness, and the dependent variable is how much it was liked.

10. Protein Supplements and Muscle Mass

A large food company is considering entering the health and nutrition sector. Their R&D food scientists have developed a protein supplement that is designed to help build muscle mass for people that work out regularly.

The company approaches several gyms near its headquarters. They enlist the cooperation of over 120 gym rats that work out 5 days a week. Their muscle mass is measured, and only those with a lower level are selected for the study, leaving a total of 80 study participants.

They randomly assign half of the participants to take the recommended dosage of their supplement every day for three months after each workout. The other half takes the same amount of something that looks the same but actually does nothing to the body.

At the end of three months, the muscle mass of all participants is measured.

The independent variable is the supplement, and the dependent variable is muscle mass.  

11. Air Bags and Skull Fractures

In the early days of airbags , automobile companies conducted a great deal of testing. At first, many people in the industry didn’t think airbags would be effective at all. Fortunately, there was a way to test this theory objectively.

In a representative example: Several crash cars were outfitted with an airbag, and an equal number were not. All crash cars were of the same make, year, and model. Then the crash experts rammed each car into a crash wall at the same speed. Sensors on the crash dummy skulls allowed for a scientific analysis of how much damage a human skull would incur.

The amount of skull damage of dummies in cars with airbags was then compared with those without airbags.

The independent variable was the airbag and the dependent variable was the amount of skull damage.

12. Vitamins and Health

Some people take vitamins every day. A group of health scientists decides to conduct a study to determine if taking vitamins improves health.

They randomly select 1,000 people that are relatively similar in terms of their physical health. The key word here is “similar.”

Because the scientists have an unlimited budget (and because this is a hypothetical example, all of the participants have the same meals delivered to their homes (breakfast, lunch, and dinner), every day for one year.

In addition, the scientists randomly assign half of the participants to take a set of vitamins, supplied by the researchers every day for 1 year. The other half do not take the vitamins.

At the end of one year, the health of all participants is assessed, using blood pressure and cholesterol level as the key measurements.

In this highly unrealistic study, the independent variable is vitamins, and the dependent variable is health, as measured by blood pressure and cholesterol levels.

13. Meditation and Stress

Does practicing meditation reduce stress? If you have ever wondered if this is true or not, then you are in luck because there is a way to know one way or the other.

All we have to do is find 90 people that are similar in age, stress levels, diet and exercise, and as many other factors as we can think of.

Next, we randomly assign each person to either practice meditation every day, three days a week, or not at all. After three months, we measure the stress levels of each person and compare the groups.

How should we measure stress? Well, there are a lot of ways. We could measure blood pressure, or the amount of the stress hormone cortisol in their blood, or by using a paper and pencil measure such as a questionnaire that asks them how much stress they feel.

In this study, the independent variable is meditation and the dependent variable is the amount of stress (however it is measured).

14. Video Games and Aggression

When video games started to become increasingly graphic, it was a huge concern in many countries in the world. Educators, social scientists, and parents were shocked at how graphic games were becoming.

Since then, there have been hundreds of studies conducted by psychologists and other researchers. A lot of those studies used an experimental design that involved males of various ages randomly assigned to play a graphic or non-graphic video game.

Afterward, their level of aggression was measured via a wide range of methods, including direct observations of their behavior, their actions when given the opportunity to be aggressive, or a variety of other measures.

So many studies have used so many different ways of measuring aggression.

In these experimental studies, the independent variable was graphic video games, and the dependent variable was observed level of aggression.

15. Vehicle Exhaust and Cognitive Performance

Car pollution is a concern for a lot of reasons. In addition to being bad for the environment, car exhaust may cause damage to the brain and impair cognitive performance.

One way to examine this possibility would be to conduct an animal study. The research would look something like this: laboratory rats would be raised in three different rooms that varied in the degree of car exhaust circulating in the room: no exhaust, little exhaust, or a lot of exhaust.

After a certain period of time, perhaps several months, the effects on cognitive performance could be measured.

One common way of assessing cognitive performance in laboratory rats is by measuring the amount of time it takes to run a maze successfully. It would also be possible to examine the physical effects of car exhaust on the brain by conducting an autopsy.

In this animal study, the independent variable would be car exhaust and the dependent variable would be amount of time to run a maze.

Read Next: Extraneous Variables Examples

The experiment is an incredibly valuable way to answer scientific questions regarding the cause and effect of certain variables. By manipulating the level of an independent variable and observing corresponding changes in a dependent variable, scientists can gain an understanding of many phenomena.

For example, scientists can learn if graphic video games make people more aggressive, if mediation reduces stress, if Gatorade improves athletic performance, and even if certain medical treatments can cure cancer.

The determination of causality is the key benefit of manipulating the independent variable and them observing changes in the dependent variable. Other research methodologies can reveal factors that are related to the dependent variable or associated with the dependent variable, but only when the independent variable is controlled by the researcher can causality be determined.

Ferguson, C. J. (2010). Blazing Angels or Resident Evil? Can graphic video games be a force for good? Review of General Psychology, 14 (2), 68-81. https://doi.org/10.1037/a0018941

Flannelly, L. T., Flannelly, K. J., & Jankowski, K. R. (2014). Independent, dependent, and other variables in healthcare and chaplaincy research. Journal of Health Care Chaplaincy , 20 (4), 161–170. https://doi.org/10.1080/08854726.2014.959374

Manocha, R., Black, D., Sarris, J., & Stough, C.(2011). A randomized, controlled trial of meditation for work stress, anxiety and depressed mood in full-time workers. Evidence-Based Complementary and Alternative Medicine , vol. 2011, Article ID 960583. https://doi.org/10.1155/2011/960583

Rumrill, P. D., Jr. (2004). Non-manipulation quantitative designs. Work (Reading, Mass.) , 22 (3), 255–260.

Taylor, J. M., & Rowe, B. J. (2012). The “Mozart Effect” and the mathematical connection, Journal of College Reading and Learning, 42 (2), 51-66.  https://doi.org/10.1080/10790195.2012.10850354

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Home » Dependent Variable – Definition, Types and Example

Dependent Variable – Definition, Types and Example

Table of Contents

Dependent Variable

Definition:

Dependent variable is a variable in a study or experiment that is being measured or observed and is affected by the independent variable. In other words, it is the variable that researchers are interested in understanding, predicting, or explaining based on the changes made to the independent variable.

Types of Dependent Variables

Types of Dependent Variables are as follows:

• Continuous dependent variable : A continuous variable is a variable that can take on any value within a certain range. Examples include height, weight, and temperature.
• Discrete dependent variable: A discrete variable is a variable that can only take on certain values within a certain range. Examples include the number of children in a family, the number of pets someone has, and the number of cars owned by a household.
• Categorical dependent variable: A categorical variable is a variable that can take on values that belong to specific categories or groups. Examples include gender, race, and marital status.
• Dichotomous dependent variable: A dichotomous variable is a categorical variable that can take on only two values. Examples include whether someone is a smoker or non-smoker, or whether someone has a certain medical condition or not.
• Ordinal dependent variable: An ordinal variable is a categorical variable that has a specific order or ranking to its categories. Examples include education level (e.g., high school diploma, college degree, graduate degree), or socioeconomic status (e.g., low, middle, high).
• Interval dependent variable: An interval variable is a continuous variable that has a specific measurement scale with equal intervals between the values. Examples include temperature measured in degrees Celsius or Fahrenheit.
• Ratio dependent variable : A ratio variable is a continuous variable that has a true zero point and equal intervals between the values. Examples include height, weight, and income.
• Count dependent variable: A count variable is a discrete variable that represents the number of times an event occurs within a specific time period. Examples include the number of times a customer visits a store, or the number of times a student misses a class.
• Time-to-event dependent variable: A time-to-event variable is a type of continuous variable that measures the time it takes for an event to occur. Examples include the time until a customer makes a purchase, or the time until a patient recovers from an illness.
• Latent dependent variable: A latent variable is a variable that cannot be directly observed or measured, but is inferred from other observable variables. Examples include intelligence, personality traits, and motivation.
• Binary dependent variable: A binary variable is a dichotomous variable with only two possible outcomes, usually represented by 0 or 1. Examples include whether a customer will make a purchase or not, or whether a patient will respond to a treatment or not.
• Multinomial dependent variable: A multinomial variable is a categorical variable with more than two possible outcomes. Examples include political affiliation, type of employment, or type of transportation used to commute.
• Longitudinal dependent variable : A longitudinal variable is a type of continuous variable that measures change over time. Examples include academic performance, income, or health status.

Examples of Dependent Variable

Here are some examples of dependent variables in different fields:

• In physics : The velocity of an object is a dependent variable as it changes in response to the force applied to it.
• In psychology : The level of happiness or satisfaction of a person can be a dependent variable as it may change in response to different factors such as the level of stress or social support.
• I n medicine: The effectiveness of a new drug can be a dependent variable as it may be measured in relation to the symptoms of a disease.
• In education : The grades of a student can be a dependent variable as they may be influenced by factors such as teaching methods or amount of studying.
• In economics : The demand for a product can be a dependent variable as it may change in response to factors such as the price or availability of the product.
• In biology : The growth rate of a plant can be a dependent variable as it may change in response to factors such as sunlight, water, or soil nutrients.
• In sociology: The level of social support for an individual can be a dependent variable as it may change in response to factors such as the availability of community resources or the strength of social networks.
• In marketing : The sales of a product can be a dependent variable as they may change in response to factors such as advertising, pricing, or consumer trends.
• In environmental science : The biodiversity of an ecosystem can be a dependent variable as it may change in response to factors such as climate change, pollution, or habitat destruction.
• I n political science : The outcome of an election can be a dependent variable as it may change in response to factors such as campaign strategies, political advertising, or voter turnout.
• I n criminology : The likelihood of a person committing a crime can be a dependent variable as it may change in response to factors such as poverty, education, or socialization.
• In engineering : The efficiency of a machine can be a dependent variable as it may change in response to factors such as the materials used, the design of the machine, or the operating conditions.
• In linguistics: The speed and accuracy of language processing can be a dependent variable as they may change in response to factors such as linguistic complexity, language experience, or cognitive ability.
• In history : The outcome of a historical event, such as a battle or a revolution, can be a dependent variable as it may change in response to factors such as leadership, strategy, or external forces.
• In sports science : The performance of an athlete can be a dependent variable as it may change in response to factors such as training methods, nutrition, or psychological factors.

Applications of Dependent Variable

• Experimental studies: In experimental studies, the dependent variable is used to test the effect of one or more independent variables on the outcome variable. For example, in a study on the effect of a new drug on blood pressure, the dependent variable is the blood pressure.
• Observational studies : In observational studies, the dependent variable is used to explore the relationship between two or more variables. For example, in a study on the relationship between physical activity and depression, the dependent variable is the level of depression.
• Psychology : In psychology, dependent variables are used to measure the response or behavior of individuals in response to different experimental or natural conditions.
• Predictive modeling : In predictive modeling, the dependent variable is used to predict the outcome of a future event or situation. For example, in financial modeling, the dependent variable can be used to predict the future value of a stock or currency.
• Regression analysis : In regression analysis, the dependent variable is used to predict the value of one or more independent variables based on their relationship with the dependent variable. For example, in a study on the relationship between income and education, the dependent variable is income.
• Machine learning : In machine learning, the dependent variable is used to train the model to predict the value of the dependent variable based on the values of one or more independent variables. For example, in image recognition, the dependent variable can be used to identify the object in an image.
• Quality control : In quality control, the dependent variable is used to monitor the performance of a product or process. For example, in a manufacturing process, the dependent variable can be used to measure the quality of the product and identify any defects.
• Marketing research : In marketing research, the dependent variable is used to understand consumer behavior and preferences. For example, in a study on the effectiveness of a new advertising campaign, the dependent variable can be used to measure consumer response to the ad.
• Social sciences research : In social sciences research, the dependent variable is used to study human behavior and attitudes. For example, in a study on the impact of social media on mental health, the dependent variable can be used to measure the level of anxiety or depression.
• Epidemiological studies: In epidemiological studies, the dependent variable is used to investigate the prevalence and incidence of diseases or health conditions. For example, in a study on the risk factors for heart disease, the dependent variable can be used to measure the occurrence of heart disease.
• Environmental studies : In environmental studies, the dependent variable is used to assess the impact of environmental factors on ecosystems and natural resources. For example, in a study on the effect of pollution on aquatic life, the dependent variable can be used to measure the health and survival of aquatic organisms.
• Educational research: In educational research, the dependent variable is used to study the effectiveness of different teaching methods and instructional strategies. For example, in a study on the impact of a new teaching program on student achievement, the dependent variable can be used to measure student performance.

Purpose of Dependent Variable

The purpose of the dependent variable is to help researchers understand the relationship between the independent variable and the outcome they are studying. By measuring the changes in the dependent variable, researchers can determine the effects of different variables on the outcome of interest.

When to use Dependent Variable

Following are some situations When to use Dependent Variable:

• When conducting scientific research or experiments, the dependent variable is the factor that is being measured or observed to determine its relationship with other factors or variables.
• In statistical analysis, the dependent variable is the outcome or response variable that is being predicted or explained by one or more independent variables.
• When formulating hypotheses, the dependent variable is the variable that is being predicted or explained by the independent variable(s).
• When writing a research paper or report, it is important to clearly define the dependent variable(s) in order to provide a clear understanding of the research question and methods used to answer it.
• In social sciences, such as psychology or sociology, the dependent variable may refer to behaviors, attitudes, or other measurable aspects of individuals or groups.
• In natural sciences, such as biology or physics, the dependent variable may refer to physical properties or characteristics, such as temperature, speed, or mass.
• The dependent variable is often contrasted with the independent variable, which is the variable that is being manipulated or changed in order to observe its effects on the dependent variable.

Characteristics of Dependent Variable

Some Characteristics of Dependent Variable are as follows:

• The dependent variable is the outcome or response variable in the study.
• Its value depends on the values of one or more independent variables.
• The dependent variable is typically measured or observed, rather than manipulated by the researcher.
• It can be continuous (e.g., height, weight) or categorical (e.g., yes/no, red/green/blue).
• The dependent variable should be relevant to the research question and meaningful to the study participants.
• It should have a clear and consistent definition and be measured or observed consistently across all participants in the study.
• The dependent variable should be valid and reliable, meaning that it measures what it is intended to measure and produces consistent results over time.

Advantages of Dependent Variable

Some Advantages of Dependent Variable are as follows:

• Allows for the testing of hypotheses: By measuring the dependent variable in response to changes in the independent variable, researchers can test hypotheses and draw conclusions about cause-and-effect relationships.
• Provides insight into the relationship between variables: The dependent variable can provide insight into how one variable is related to another, allowing researchers to identify patterns and make predictions about future outcomes.
• Enables the evaluation of interventions : By measuring changes in the dependent variable over time, researchers can evaluate the effectiveness of interventions and determine whether they have a meaningful impact on the outcome being studied.
• Enables the comparison of groups: The dependent variable can be used to compare groups of participants or populations, helping researchers to identify differences or similarities and draw conclusions about underlying factors that may be contributing to those differences.
• Enables the calculation of statistical measures: By measuring the dependent variable, researchers can calculate statistical measures such as means, variances, and standard deviations, which are used to make statistical inferences about the population being studied.

Disadvantages of Dependent Variable

• Limited in scope: The dependent variable is limited to the specific outcome being studied, which may not capture the full complexity of the system or phenomenon being investigated.
• Vulnerable to confounding variables: Confounding variables, or factors that are not controlled for in the study, can influence the dependent variable and obscure the relationship between the independent and dependent variables.
• Prone to measurement error: The dependent variable may be subject to measurement error due to issues with data collection methods or measurement instruments, which can lead to inaccurate or unreliable results.
• Limited to observable variables : The dependent variable is typically limited to variables that can be measured or observed, which may not capture underlying or latent variables that may be important for understanding the phenomenon being studied.
• Ethical concerns: In some cases, measuring the dependent variable may raise ethical concerns, such as in studies of sensitive topics or vulnerable populations.
• Limited to specific time periods : The dependent variable is typically measured at specific time points or over specific time periods, which may not capture changes or fluctuations in the outcome over longer periods of time.

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What Are Variables in a Research Paper?

What Components Are Necessary for an Experiment to Be Valid?

Research papers will mention a variety of different variables, and, at first, these technical terms might seem difficult and confusing. But with a little practice, identifying these variables becomes second nature. Because they are sometimes not explicitly labeled in the research writeup, it is useful to have a real research paper on hand as you learn these terms, so you can get some hands-on practice at identifying them.

Independent Variable

The independent variable, also known as the IV, is the variable that the researchers are manipulating in an experiment or quasi-experiment. It is also the label given to the “criterion” variable in certain types of regression analysis. For example, if a researcher has two groups of people watch either a happy film or a sad film before giving an IQ test, the IV is the mood of the participants.

Dependent Variable

The dependent variable, or DV, is the one that is being measured by the researcher; it is the outcome variable. There is often confusion between the IV and the DV among new science students, but a good way to distinguish them is to remember that the outcome of measuring the DV is hypothesized to depend on the manipulation of the IV. In the above example, IQ was hypothesized to depend on the mood of the participants.

A covariate is a variable that the researchers include in an analysis to determine whether the IV is able to influence the DV over and above any effect the covariate might have. The classic example is when researchers take a baseline measurement, perform some manipulation, then take the measurement again. When they analyze this data, they will enter the baseline scores as a covariate, which will help cancel out any initial differences between the participants.

Extraneous Variables

An extraneous variable is a little different from the rest because it is not directly measured, or often even wanted, by the researchers. It is a variable that has an impact on the results of the experiment that the researchers didn't anticipate. For example, the heat of the room might be different between two groups of IQ tests, and the hot room might annoy people and affect their scores. Scientists try to control these variables, which means keeping them constant between groups.

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Warren Davies has been writing since 2007, focusing on bespoke projects for online clients such as PsyT and The Institute of Coaching. This has been alongside work in research, web design and blogging. A Linux user and gamer, warren trains in martial arts as a hobby. He has a Bachelor of Science and Master of Science in psychology, and further qualifications in statistics and business studies.

What are Examples of Variables in Research?

Table of contents, introduction.

In writing your thesis, one of the first terms that you encounter is the word variable. Failure to understand the meaning and the usefulness of variables in your study will prevent you from doing excellent research. What are variables, and how do you use variables in your research?

You may find it challenging to understand just what variables are in research, especially those that deal with quantitative data analysis. This initial difficulty about variables becomes much more confusing when you encounter the phrases “dependent variable” and “independent variable” as you go deeper in studying this vital concept of research, as well as statistics.

Therefore, it is a must that you should be able to grasp thoroughly the meaning of variables and ways on how to measure them. Yes, the variables should be measurable so that you will use your data for statistical analysis.

Definition of Variable

Variables are those simplified portions of the complex phenomena that you intend to study. The word variable is derived from the root word “vary,” meaning, changing in amount, volume, number, form, nature, or type. These variables should be measurable, i.e., they can be counted or subjected to a scale.

Examples of Variables in Research: 6 Phenomena

Phenomenon 1: climate change, phenomenon 2: crime and violence in the streets, phenomenon 3: poor performance of students in college entrance exams, phenomenon 4: fish kill, phenomenon 5: poor crop growth, phenomenon 6:  how content goes viral.

Notice in the above variable examples that all the factors listed under the phenomena can be counted or measured using an ordinal, ratio, or interval scale, except for the last one. The factors that influence how content goes viral are essentially subjective.

The expected values derived from these variables will be in terms of numbers, amount, category, or type. Quantified variables allow statistical analysis . Variable descriptions, correlations, or differences are then determined.

Difference Between Independent and Dependent Variables

Independent variables.

For example, in the second phenomenon, i.e., crime and violence in the streets, the independent variables are the number of law enforcers. If there are more law enforcers, it is expected that it will reduce the following:

Dependent Variables

For example, in the first phenomenon on climate change, temperature as the independent variable influences sea level rise, the dependent variable. Increased temperature will cause the expansion of water in the sea. Thus, sea-level rise on a global scale will occur.

Finding the relationship between variables

Finding the relationship between variables requires a thorough  review of the literature . Through a review of the relevant and reliable literature, you will find out which variables influence the other variable. You do not just guess relationships between variables. The entire process is the essence of research.

At this point, I believe that the concept of the variable is now clear to you. Share this information with your peers, who may have difficulty in understanding what the variables are in research.

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Method and methodology: the difference, when to stop searching the literature: three tips, regression analysis: 5 steps and 4 applications, about the author, patrick regoniel, 128 comments.

Your question is unclear to me Biyaminu. What do you mean? If you want to cite this, see the citation box after the article.

I salute your work, before I was have no enough knowledge about variable I think I was claimed from my lecturers, but the real meaning I was in the mid night. thanks

thanks for the explanation a bout variables. keep on posting information a bout reseach on my email.

You can see in the last part of the above article an explanation about dependent and independent variables.

I am requested to write 50 variables in my research as per my topic which is about street vending. I am really clueless.

Dear Alhaji, just be clear about what you want to do. Your research question must be clearly stated before you build your conceptual framework.

Can you please give me what are the possible variables in terms of installation of street lights along barangay roads of calauan, laguna: an assessment?

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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 analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is 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.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Research Article

The impact of digital technology on entrepreneurship——Evidence from China General Social Survey

Roles Conceptualization, Data curation, Formal analysis

* E-mail: [email protected] (KW); [email protected] (YQ)

Affiliation School of Economics and Management, Luoyang Institute of Science and Technology, Henan Universities and Colleges New Pattern Think Tank Industrial Innovation and Regional High Quality Development Research Institute, Luoyang, China

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Affiliation School of Foreign Languages, Luoyang Institute of Science and Technology, Luoyang, China

Roles Formal analysis, Writing – review & editing

• Kedong Wu, 
• Mengchun Zhu, 

• Published: September 24, 2024
• https://doi.org/10.1371/journal.pone.0310188
• Reader Comments

In light of the rapid development of digital technology, it is imperative to study the impact of digital technology on the labour force’s entrepreneurial choices with the utmost urgency. This paper first constructs a theoretical mechanism for how digital technology affects individual entrepreneurship. It then empirically examines data from the China General Social Survey (CGSS) to test the theory. The results show that digital technology significantly increases individual entrepreneurial choices. Furthermore, the conclusions of the study are robust even when the estimation method and variable measurement are changed. Finally, the study finds that digital technology has the greatest impact on entrepreneurship among individuals with low education, the second-largest impact on those with medium education, and the third-largest impact on those with high education. Individuals with higher education levels have the second largest impact on the entrepreneurship of individuals with higher education levels, while the smallest impact is observed in this group. Digital technology development has a stronger role in promoting entrepreneurship of individuals with urban household registration than those with rural household registration. In terms of sub-region, digital technology has a larger role in individual entrepreneurship in the eastern and central regions, and has a less significant role in the western region. The findings of this study suggest that there is a need to implement measures to accelerate the pace of digital technology development, enhance the training of entrepreneurial skills and attitudes among highly educated individuals, and direct efforts towards enhancing digital technology development in rural and western China.

Citation: Wu K, Zhu M, Qu Y (2024) The impact of digital technology on entrepreneurship——Evidence from China General Social Survey. PLoS ONE 19(9): e0310188. https://doi.org/10.1371/journal.pone.0310188

Editor: Valentina Diana Rusu, Alexandru Ioan Cuza University: Universitatea Alexandru Ioan Cuza, ROMANIA

Received: December 18, 2023; Accepted: August 27, 2024; Published: September 24, 2024

Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study was supported by Research and Practice Project on Higher Education Teaching Reform in Henan Province (2024SJGLX0187), The National Natural Science Foundation of Guangxi Province (2023GXNSFBA026063) and Henan Province Soft Science Research Programme Project (242400410060). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent years, the development process of the digital economy in countries around the world has been accelerating. This has been accompanied by an acceleration in the updating and iteration of digital technology as a support for the development of the digital economy. Furthermore, digital technology has gradually penetrated into all areas of society, which has had a profound impact on the development of the national economy and the pattern of the labour market.

A number of scholars have studied the impact of digital technologies such as the Internet and artificial intelligence on the skill structure of the labour force. These include Berman, Falk, Bresnahan, Levy, Relich and Moore. Berman’s study found that the 1980s saw the emergence of new features in the structure of the US labour force as a result of technological advances [ 1 ]. The employment market is experiencing a notable surge in demand for skilled labour. Falk examines the relationship between information technology and labour force structure, concluding that an increase in information technology capital investment leads to an uptick in the demand for high-skilled labour, and that an increase in information inputs at the firm level will have a catalytic effect on high-skilled employment [ 2 ]. Bresnahan’s study indicates that digital technology has a significant skill bias, and the development of digital technology has resulted in a labour market that is biased towards skilled labour [ 3 ]. The studies of Levy, Relich, and Moore corroborate the conclusions of the aforementioned scholars and demonstrate that as the level of digital technology application increases, the labour market becomes biased towards a more highly educated, skilled, and qualified workforce [ 4 – 6 ].

The depth of research into the impact of digital technology on the industrial structure of the labour force has also attracted the attention of scholars.

Ding Lin and Wang Huijuan conducted an empirical examination of the relationship between Internet technology and employment, utilizing input-output data from several countries [ 7 ]. Their findings indicated that the advancement of Internet technology has a facilitating effect on overall employment, with a particularly pronounced impact on the employment of the labour force in the tertiary industry. Wang Wen conducted an empirical study on panel data for 30 provinces in China [ 8 ]. The empirical results indicate that industrial intelligence has the effect of reducing the share of employment in the manufacturing industry and increasing the share of employment in the service industry, with a particular increase in the share of employment in the productive service industry and high-end service industry. Guo Dongjie and colleagues demonstrated that the advancement of the digital economy is conducive to an increase in the share of employment in the tertiary industry and a decrease in the share of employment in the primary and secondary industries [ 9 ].

Concurrently, research on the impact of digital technology on labour force employment choice has begun to emerge. The existing literature on this topic primarily concerns the impact of digital technologies, such as the Internet, on the labour force’s employment choices. Overall, the use of the Internet for job searching can significantly reduce search costs and increase the likelihood of job seekers obtaining a job [ 10 ]. Furthermore, employers can release job information through online platforms to expand the scope of information transmission, thus reducing the time of job vacancies [ 11 ]. Additionally, job seekers can utilize Internet information resources to obtain relevant job information in a timely manner, which allows them to have more employment options and employment opportunities [ 12 ]. Furthermore, a number of scholars have examined the influence of the Internet on farm household entrepreneurship and family entrepreneurship [ 13 , 14 ]. The study revealed that the utilization of the Internet fosters the growth of farm household and family entrepreneurship.

The current academic research on digital technology and labour force employment can be broadly categorized into three main areas: firstly, the relationship between digital technology and labour force employment skill structure; secondly, the relationship between digital technology and labour force employment industry structure; and thirdly, the relationship between digital technology and labour force employment choice. In comparison to the aforementioned two areas, research on digital technology and labour force employment choice is relatively scarce, with considerable scope for further investigation. The current findings primarily examine the influence of the Internet on labour force employment choice, with a paucity of studies that directly assess the impact of digital technology on labour force entrepreneurship. In the current era of the digital economy, the term "digital technology" encompasses not only the Internet but also a multitude of other content and a richer set of connotations. The mechanism by which digital technology affects individual labour force entrepreneurship remains unclear. The effect of digital technology on labour force entrepreneurship is still largely unknown. Furthermore, the impact of the development of digital technology on labour force entrepreneurship decision-making requires further investigation.

The potential contributions of this paper are as follows: Firstly, unlike previous studies, in that it this paper examines the phenomenon of labour force entrepreneurial choice from the perspective of digital technology development. This broadens the research perspective on the factors influencing labour force entrepreneurial choice. Secondly, this paper constructs a theoretical mechanism for the impact of digital technology on labour force entrepreneurship. This enriches the theoretical research content in this field. Thirdly, the theory will be empirically tested using micro-data, which will provide micro-empirical evidence for the theory of digital technology and entrepreneurship. Fourthly, the findings of this study will be incorporated into the policy recommendations for accelerating the development of digital technology in China. These recommendations will provide meaningful references for governmental decision-making.

Theoretical analysis and research hypotheses

Mechanisms by which digital technologies influence entrepreneurial decision-making.

The development of digital technology can affect potential entrepreneurs’ entrepreneurial decisions through four paths: increasing entrepreneurial opportunities, improving the availability of information resources, expanding the scope of the market, and reducing the cost of entrepreneurship. Firstly, the impact of digital technology on entrepreneurial opportunities. Stevenson and Gumpert proposed that technology, market, government regulation and social values are the four external environmental factors affecting entrepreneurial opportunities [ 15 ]. Saemundsson and Dahlstrand based their classification of entrepreneurial opportunities on the two factors of technological knowledge and market knowledge [ 16 ], which they divided into four categories: existing technology—existing market, existing technology—new market, new technology—existing market and new technology—new market.—Expansion into new markets. The development of digital technology represents an important external environmental factor that will disrupt the established equilibrium, creating new entrepreneurial opportunities, production processes, markets, and ways of organizing. This will result in the emergence of two distinct types of entrepreneurial opportunities: the exploitation of existing markets through the application of new technology and the creation of new markets through the introduction of new technology. On one hand, the application of digital technologies to existing markets enables the introduction of new features to existing products and the enhancement of their performance. On another hand, significant innovations may be generated by the utilization of digital technology expertise to address novel requirements in both life and work contexts. Indeed, a considerable number of entrepreneurial opportunities can be generated in traditional industries or markets by leveraging digital technologies to meet consumer needs. Examples of this phenomenon include the application and popularization of e-commerce, online education, online healthcare and remote collaborative research and development.

Secondly, the impact of digital technology on access to information resources. Entrepreneurs require a variety of resources, including capital and information. The latter category encompasses a range of topics, including economic, policy, growth potential, market, technology, and other relevant areas. Shane and Venkataraman posit that information is instrumental in the utilization and development of entrepreneurial opportunities [ 17 ]. Consequently, digital technology can facilitate entrepreneurship by affecting access to information resources. On the one hand, the application of digital technology can assist potential entrepreneurs in more effectively identifying entrepreneurial information. Potential entrepreneurs who utilize digital technology are more likely to obtain a plethora of pertinent, readily accessible, punctual and efficacious information through digital technology, which enables them to identify and grasp potential entrepreneurial opportunities. On the other hand, digital technology can assist entrepreneurs in acquiring information regarding alterations in the entrepreneurial environment and in maintaining awareness of pertinent business-related counsel. This can facilitate timely, precise, and efficacious adjustments to entrepreneurial practices, thereby reducing the risk of the entrepreneurial process.

Thirdly, the impact of digital technologies on the scope of markets. The accelerated evolution of digital technologies has facilitated the efficient matching of producers and consumers, thereby reducing the costs associated with market research. The pervasive adoption of digital technologies has diminished the significance of geographical boundaries, enabling producers to connect with potential consumers at a greater distance than was previously feasible. This has facilitated entrepreneurs to reach a larger customer base at a reduced cost.

Indeed, digital technology, as a universal technology, has profoundly affected consumers’ food, clothing, housing and transportation, directly or indirectly impacting all industries. The pervasive adoption of digital technology has considerably broadened the market potential for aspiring entrepreneurs, enabling them to expand their market size at a significantly reduced cost. The expansion of the market due to the popularization and widespread use of digital technologies can increase the profitability of entrepreneurship. Furthermore, the expansion of the market can also increase the survival rate of a business, thereby reducing the risk of entrepreneurship.

Fourthly, the impact of digital technologies on the cost of entrepreneurship. The popularization and application of digital technology affect the cost of entrepreneurship in three principal ways. Firstly, the dissemination and utilization of digital technology can diminish the financial outlay required to commence a business. The advancement of digital technology facilitates the dissemination of information, thereby reducing the cost of acquiring it. The marginal cost of information provided by digital technology is minimal, and by utilizing digital technology, entrepreneurs can obtain all aspects of information they require for their own development at a reduced cost. Secondly, the extensive application of digital technology can reduce the variable cost of business participation in the market. The development of digital technology alters the structure of the market, reducing information asymmetry, thereby enhancing market efficiency. The dissemination of digital technology enables the expeditious completion of business transactions, thereby enhancing the efficiency of product activities. Finally, the utilization of digital technology can result in a reduction of the transaction costs associated with business operations. The advent of digital technology has brought with it the capacity for high-speed and easy data transmission, which has made it easier for entrepreneurs to exchange information with upstream and downstream firms. This has resulted in a notable reduction in transaction costs. The abundance, accessibility and transparency of information not only reduce search costs, but also supervision and enforcement costs.

In conclusion, we propose the following hypothesis:

• Hypothesis 1: All other factors being equal, the development of digital technology can increase entrepreneurs’ options available to entrepreneurs.

Heterogeneity in the impact of digital technology development on entrepreneurial decision-making

Firstly, digital technology may have a heterogeneous impact on the entrepreneurial choices of individuals with different levels of education. It is evident that educational attainment is a pivotal factor in understanding the influence of digital technology on entrepreneurial decision-making. Individuals with different levels of education demonstrate significant heterogeneity in their entrepreneurial motivations and opportunities when confronted with the advent of digitization. Those with higher levels of education are likely to possess more profound knowledge and professional skills, which affords them a significant advantage in the digital era. In the job market, they are more likely to find employment that aligns with their professional backgrounds and is compensated with relatively desirable salaries and benefits. Consequently, as digital technology continues to evolve, individuals with advanced academic qualifications may be more inclined to pursue secure career paths rather than embarking on entrepreneurial endeavors, despite the numerous advantages and opportunities that entrepreneurship offers. However, the situation is quite different for individuals with low levels of education. Those with lower levels of education are less competitive in the traditional job market and often face greater pressure to find employment and greater uncertainty regarding their future prospects. The advent of digital technology, particularly the emergence of mobile Internet and social media, has created a plethora of novel entrepreneurial opportunities for those with limited education. The advent of digital technology has facilitated the realization of entrepreneurial aspirations among individuals with low levels of education. These platforms have lowered the threshold for entrepreneurship, enabling individuals with low education levels to realize their self-worth through innovative business or service models. Those with a medium level of education occupy a position somewhere between the aforementioned extremes. While they may possess certain knowledge and skills, they also face certain employment pressures. Consequently, the advent of digital technology presents both opportunities and challenges for this demographic. Those with medium levels of education may choose between stable employment and entrepreneurship, according to their own circumstances and the prevailing market environment. In conclusion, the impact of digital technology on entrepreneurial choices is closely related to the level of education. The digital era presents individuals with different levels of education with distinct entrepreneurial opportunities and challenges. This reflects the universality and inclusiveness of digital technology. It is reasonable to posit that the development of digital technology has the most significant impact on the entrepreneurial choices of individuals with low levels of education, followed by those with medium levels of education, while the impact on individuals with higher levels of education is relatively minor.

Secondly, the impact of digital technology on entrepreneurial choices may vary depending on the domicile of the individual. In order to gain a more nuanced understanding of the impact of digital technologies on entrepreneurial choices, it is essential to consider the role of urban-rural household differences. These differences may lead to heterogeneous impacts across different household groups. Individuals with urban household registration are more likely to be situated in a more developed and diversified economic environment. In such an environment, individuals are more likely to be exposed to new technologies and new thinking, and are more likely to find resources and partners that are compatible with their entrepreneurial ideas. Conversely, towns and cities offer a larger market and stronger spending power, providing entrepreneurs with greater market opportunities. Furthermore, the infrastructure and public services in towns and cities are more comprehensive, and entrepreneurs can utilize more convenient channels and resources to support their business operations. These factors play a pivotal role in fostering entrepreneurial activities. Conversely, individuals with rural household registration may encounter greater challenges and constraints. Firstly, the relatively low level of economic development and limited market capacity in rural areas constrain the market opportunities for entrepreneurs. Secondly, the infrastructure and public services in rural areas are relatively underdeveloped, which makes it challenging for entrepreneurs to access the necessary resources. Furthermore, the dissemination of information in rural areas is relatively limited, which may impede entrepreneurs’ ability to obtain the most recent market intelligence and industry developments in a timely manner. This, in turn, increases the risk and uncertainty associated with entrepreneurship. Therefore, it is reasonable to assume that the development of digital technology has a greater contribution to the entrepreneurial choices of urban domiciled individuals. Nevertheless, this does not imply that individuals with rural household registration are unable to benefit from the advancement of digital technology. As technology becomes more prevalent and infrastructure improves, it is anticipated that entrepreneurs in rural areas will also be afforded greater opportunities and support.

Thirdly, the impact of digital technology on individual entrepreneurial choices may vary across different regions. In China, the impact of the development of digital technology on individual entrepreneurial choices is also significantly affected by geographical differences. Given China’s vast territory and the uneven economic and social development across its regions, there are significant differences in the level of development and the pace of digital technology adoption in the eastern, central and western regions. In the eastern and central regions, which have been at the vanguard of China’s economic expansion, the development of their digital economies has been particularly noteworthy. The digital technology sector in these two regions has a long history of development and has become deeply embedded in every aspect of society and the economy. Both large Internet companies and small stores at the end of the street are actively embracing digitization and seeking more efficient and convenient ways to operate. In such an environment, individual entrepreneurs are able to access the latest digital technologies and business models at an earlier stage and utilize them to enhance their competitiveness. Concurrently, the well-developed infrastructure and public services in these regions provide significant convenience for entrepreneurs. However, a different picture emerges when we turn our attention to the western region. Due to a number of historical and geographical factors, the development of digital technology in the western region has been relatively slow. Despite the state’s recent increase in investment in the western region, there persists a disparity between the western region and the eastern and central regions in terms of digital infrastructure construction, technology application and digital literacy. This discrepancy is also evident in the realm of individual entrepreneurship. In the western region, while digital technology offers entrepreneurs certain opportunities and conveniences, these are considerably less prevalent than in the eastern and central regions. Consequently, the development of digital technologies has had a relatively limited impact on individual entrepreneurial choices in the western region.

In conclusion, we put forth the following hypotheses:

• Hypothesis 2a: There is a degree of heterogeneity in the choice of digital technology to influence entrepreneurship, all other input factors being equal.
• Hypothesis 2b: Holding other input factors constant, there are differences between urban and rural areas in the choice of digital technologies to influence entrepreneurship.
• Hypothesis 2c: It is assumed that there is regional heterogeneity in the choice of digital technology to influence entrepreneurship, provided that other input factors are equal.

Research design

Data sources and processing.

The data presented in this paper has been derived from the following sources: The data used in this study was drawn from the CGSS 2013, 2015, 2017, 2018, and 2021 surveys, as well as the China Statistical Yearbook from previous years. The Chinese General Social Survey (CGSS) represents China’s earliest national, comprehensive, and continuous academic survey programme, implemented by the China Survey and Data Center of Renmin University of China. In accordance with international standards, more than 10,000 households are surveyed on each occasion in all provinces, municipalities and autonomous regions of mainland China. The most recent data from this survey is currently updated to 2021.

In this paper, we first merge the CGSS data for the five periods mentioned above. Second, we remove samples with missing key variables. Finally, it should be noted that the current regional matching code published by CGSS can only match provincial data. Therefore, we match micro data with provincial macro data to obtain the dataset used in this paper.

Econometric modelling

There, i represents different provinces, and t represents different years, entrepreneur denotes the dummy variable for whether a labor force individual chooses to start a business or not, index denotes the level of digital technology development in the region where a labor force individual is located; X denotes the control variable for a labor force individual; in addition, Pro is used to denote the fixed effects of province, Year is used to denote the fixed effects of year, ε is used to denote the random error term.

Variable selection

(1) the explained variable..

Entrepreneur, is a dummy variable that measures whether or not an individual is engaged in entrepreneurial activity. If the individual is currently engaged in entrepreneurial activity, the variable entrepreneur is assigned a value of 1; otherwise, it is assigned a value of 0. The information on the current work status of the individual surveyed in the CGSS is used to determine whether or not the respondent is engaged in entrepreneurial activity. If the respondent indicates that they are a "boss" or "partner" during the interview, this is considered to be entrepreneurial activity. Additionally, "self-employed" and "freelance" are also considered to be entrepreneurial activities, as these are also existing activities. Furthermore, we consider self-employment and freelancing to be entrepreneurial activities, a common approach in existing literature.

(2) The core explanatory variables.

Digital technology development level measurement index (index). At present, there is no unified standard for the index system to measure the level of digital technology development. In accordance with the methodologies employed in existing literature [ 18 , 19 ], and in consideration of the availability of data, three dimensions are employed to capture the development of digital technology in each province in China: the construction of digital technology infrastructure, the scale of the digital economy, and the degree of digital technology mobile application. The length of long-distance fibre-optic cable lines is employed to assess the construction of digital technology infrastructure. The volume of express delivery business, software industry revenue, and total telecommunications business are utilized to gauge the scale of digital economy development. Finally, the number of end-of-year cell phone subscribers and the capacity of cell phone exchanges are employed to reflect the degree of mobile application of digital technology.

Firstly, the aforementioned six indicators were standardized utilizing the method of standardization of extreme deviation. To this end, it is necessary to ascertain the maximum value (Xmax) and minimum value (Xmin) of a specific indicator and calculate the extreme deviation. The ratio R is calculated as the difference between the maximum value (Xmax) and the minimum value (Xmin), and then the minimum value (Xmin) is subtracted from each observed value (X) of the variable in question. This value is then divided by the extreme deviation (R). Subsequently, the weight of each indicator is determined utilizing the entropy weighting method. Finally, the level of digital technology development is calculated based on the indicators and weights that have been standardized.

(3) Control variables.

In this paper, control variables for individual characteristics and regional characteristics were selected.

Individual characteristics: The first variable is the gender of the respondent, which is assigned a value of 1 for males and 0 for females. The second variable is the age of the respondent, which is calculated as the square of the respondent’s age (age2). The third variable is whether the respondent has an urban household registration (huji), which is assigned a value of 1 for having an urban household registration and vice versa. Finally, the respondent’s membership of the Chinese Communist Party (CCP) was considered. This was categorized as either 1 for CCP membership or 0 for non-CCP membership. The marital status of the respondent is also taken into account. This is assigned a value of 1 for those who are married and 0 for those who are divorced. If the respondent is a member of the CPC (dangyuan), CPC members are assigned a value of 1, and non-CPC members are assigned a value of 0. The marital status of the respondent is determined by two indicators: whether or not the respondent has a spouse (spouse) and whether or not the respondent is divorced (divorce). If the respondent is married, the spouse is assigned a value of 1, and vice versa. The salary of the respondent is proxied by the logarithmic form of the respondent’s annual income.

Regional characteristics: The first indicator, population density (pop), is expressed as the ratio of the total population to the administrative area at the end of the year in the respondent’s province. The second indicator, employment rate (job), is expressed as the employment rate of urban units. The third indicator, financial development (loan), is measured as the average of the ratio of the total amount of loans from financial institutions to GDP. The fourth indicator is the openness level (tra), which is expressed as the ratio of the total amount of imports and exports in the respondent’s province to GDP. The fifth indicator is the urbanization rate (urban), which is expressed as a percentage. The sixth indicator is the regional economic development level (gdprio), which is measured as the real growth rate of regional GDP. This is expressed as the share of total imports and exports to GDP of the respondent’s province.

The descriptive statistics for the variables in this paper are presented in Table 1 .

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https://doi.org/10.1371/journal.pone.0310188.t001

Analysis of measurement results

Analysis of baseline regression results.

Given that the explanatory variables in this paper are dummy variables, the Probit model is employed to assess the impact of digital technology development on individual entrepreneurship, while also accounting for province and year effects. In the robustness test section, the fixed effects model and Logit model are also employed for testing purposes. In order to examine the robustness of the model, the estimation is carried out by adding control variables step by step, and the regression results are shown in Table 2 . It can be seen that in the estimation results (1)-(3), the marginal effects of the level of digital technology development on labour force entrepreneurship are all positive at the 1% significance level. This indicates that the development of digital technology significantly increases individual entrepreneurial choices, thereby confirming Hypothesis 1.

https://doi.org/10.1371/journal.pone.0310188.t002

Heterogeneity discussion

(1) the existence of diverse levels of educational attainment..

The sample was divided into three groups according to the education level of individuals: low (junior high school and below), middle (senior high school and junior college) and high (university college and above). The estimation results are shown in Table 3 . The principal conclusions of this paper remain valid, and the improvement in the level of digital technology has increased the range of options available to individuals wishing to start their own business. However, the impact of digital technology on the entrepreneurship of individuals with different levels of education varies to some extent. The effect on individuals with low levels of education is the most pronounced, the effect on individuals with medium levels of education is the second most pronounced, and the effect on entrepreneurship of individuals with high levels of education is the least pronounced. The continuous development of digital technology has the effect of making it easier for highly educated individuals to find satisfactory jobs. Consequently, the level of digital technology development has a smaller effect on highly educated individuals relative to those with medium and low levels of education. Consequently, hypothesis 2a is validated.

https://doi.org/10.1371/journal.pone.0310188.t003

(2) Urban-rural heterogeneity.

The sample is divided into two groups, one comprising individuals residing in urban areas and the other in rural areas. Regression analysis was conducted on each group separately, and the results are presented in columns (1) and (2) of Table 4 . The main conclusion of this paper remains valid. The development of digital technology increases the choice of individual entrepreneurship, but the impact of the development of digital technology on individual entrepreneurship of different domiciles shows some variability. This is evidenced by the greater positive promotion of entrepreneurship of individuals of urban domiciles, which confirms Hypothesis 2b. It is also notable that the coefficient of the impact of digital technology on entrepreneurship of individuals in towns and cities is significant at the 1% level, whereas the coefficient of the impact of digital technology on rural individual entrepreneurship is not significant. One potential explanation for this phenomenon is that individuals with urban household registration tend to have greater access to opportunities, larger markets, more convenient channels, and so forth, compared to rural individuals. Consequently, the development of digital technology has a more pronounced impact on the entrepreneurial choices of individuals with urban household registration.

https://doi.org/10.1371/journal.pone.0310188.t004

(3) Heterogeneity in the east, central and western regions.

Further, based on the region where individuals are located, the sample is divided into three groups: East, Central and West. The estimation results are shown in columns (3)-(5) of Table 4 . The main conclusion of this paper remains valid, namely that digital technology increases the choice of individual entrepreneurship. However, the impact of digital technology development on individual entrepreneurship in different regions shows a certain degree of variability. The effect on individual entrepreneurship in the central region is the largest and significant at the 5% level, that on the eastern region is the second largest and significant at the 1% level, and that on the western region is insignificant. This confirms Hypothesis 2c.

Mechanical testing

The theoretical analysis in the previous section states that the development of digital technology can affect potential entrepreneurs’ entrepreneurial decisions through four paths: increasing entrepreneurial opportunities, improving the availability of information resources, expanding the scope of the market, and reducing the cost of entrepreneurship. This section uses a mediated effects model to test the theoretical mechanisms proposed in the previous section.

First, the entrepreneurial opportunity mechanism is tested. Considering that if the regional economy develops faster, then the entrepreneurial opportunities of the labour force may also be more, the economic development speed of the province where the labour force is located is used as a proxy variable for entrepreneurial opportunities and estimated based on the mediation effect model, and the results of the estimation are shown in columns (1)-(2) of Table 5 . It can be seen that digital technology has a significant positive effect on entrepreneurial opportunities and that digital technology can increase the labour force’s options to choose entrepreneurship by generating entrepreneurial opportunities.

https://doi.org/10.1371/journal.pone.0310188.t005

Secondly, the mechanism of information resource acquisition is tested. Considering that the higher degree of factor market development has a greater role in promoting the level of information technology development, the easier it is to access information resources, the degree of factor market development in the province where the labour force is located is used as a proxy variable for the ease or difficulty of access to information resources and estimated based on the mediated effects model, and the estimated results are shown in columns (3)-(4) of Table 5 . It can be seen that digital technology has a significant positive impact on access to information resources, and digital technology can increase the labour force’s choice of entrepreneurship by enhancing access to information resources.

Thirdly, the market scope mechanism is tested. Considering that intermediary organisations arise and develop with the expansion of market scope, and that a higher degree of intermediary organisation development implies a greater market scope, the degree of intermediary organisation and legal development in the province where the labour force is located is used as a proxy variable for market scope and estimated based on the mediated effects model, and the results of the estimation are shown in columns (5)-(6) of Table 5 . It can be seen that digital technology has a significant positive effect on market scope and that digital technology can increase the labour force’s options to choose entrepreneurship by expanding the market scope.

Finally, the entrepreneurial cost mechanism is tested. Considering that the government-market relationship is closely related to the institutional transaction costs of enterprise production and business activities, and that a good government-market relationship can reduce the transaction costs and financing constraints of enterprises, the government-market relationship is used as a proxy variable for entrepreneurial costs and estimated based on the mediated effects model, and the estimated results are shown in columns (7)-(8) of Table 5 . It can be seen that digital technology has a significant positive impact on government-market relations, and that digital technology can increase the labour force’s choice to choose entrepreneurship by improving government-market relations and thus reducing entrepreneurial costs.

It should be noted that the three indicators used in this part of the discussion, namely the degree of development of factor markets, the degree of development of intermediary organisations and laws, and the relationship between the government and the market, are derived from the sub-indices in the China Marketisation Index compiled by Fan Gang.

Robustness testing

(1) the instrumental variables approach..

The regression results presented in Table 2 provide empirical support for the research hypothesis presented in the previous section. However, the estimation results are subject to endogeneity problems. To address the aforementioned issues, a re-estimation is conducted utilizing the instrumental variable method. The instrumental variable method requires the identification of exogenous variables that are related to digital technology and can only indirectly affect individual labour force entrepreneurship by influencing digital technology as instrumental variables of digital technology. In this paper, we utilize the distance from each provincial capital city to Hangzhou (in logarithmic form) as the instrumental variable for digital technology. The rationale is that digital finance, exemplified by Alipay, originated in Hangzhou, which has led to Hangzhou’s digital technology development being at the vanguard of the Chinese market. Based on the spatial spillover effect of digital technology, it is reasonable to assume that the closer the geographic proximity to Hangzhou, the higher the level of development of digital technology. There is currently no evidence to suggest that the distances of provincial capitals to Hangzhou can be used through channels other than those affecting the development of digital technology to affect labour force entrepreneurship. Furthermore, given that the distance from provincial capital cities to Hangzhou remains constant over time, the current study’s findings are employed to construct an instrumental variable that varies with region and time (index_iv1). This variable is derived from the mean value of digital technology development in other provinces in China. The first two columns of Table 6 present the results of the 2SLS estimation using instrumental variables. Among these, column (1) of Table 6 demonstrates the estimation of the first stage. It can be seen that the larger the value of the constructed instrumental variable, the lower the level of digital economic development of the region. The estimated coefficient is significant at the 1% level with an R2 of 0.6576, indicating that the instrumental variable has a strong explanatory power for the endogenous variables. Column (2) of Table 6 presents the estimation of the second stage. It can be observed that the coefficient of digital technology is significantly positive at the 1% level. This shows that the estimated results of the instrumental variables support the main hypothesis of this paper.

https://doi.org/10.1371/journal.pone.0310188.t006

Furthermore, given that Beijing, Guizhou, and Ulanqab are China’s national big data centres, regions that are closer to the big data centres are likely to have more favourable conditions for the development of digital technology. Therefore, this paper selects the average distance from the provincial capital to these three regions as the instrumental variable for estimation. Furthermore, given that the average distance remains constant over time, the findings of the current study are employed to construct an instrumental variable that varies with region and time (index_iv2). This variable is derived from the mean value of digital technology development in other provinces in China. The results of the regression are shown in the third and fourth columns of Table 6 . Among these, column (3) of Table 6 demonstrates the estimation of the first stage. It can be seen that the larger the value of the constructed instrumental variable, the lower the level of digital economic development of the region. The estimated coefficient is significant at the 1% level with an R2 of 0.5978, indicating that the instrumental variable has a strong explanatory power for the endogenous variables. Column (4) of Table 6 presents the estimation of the second stage. It can be observed that the coefficient of digital technology is significantly positive at the 1% level. This again shows that the estimated results of the instrumental variables support the main hypothesis of this paper.

(2) Changing the estimation method and the measurement of the explanatory variables.

In order to further test the robustness of the conclusions, this paper employs two alternative estimation methods for the model: the fixed effects model and the Logit model. The estimation results are presented in Table 7 . Secondly, the measurement method of the explanatory variables is altered, with the measures of individual entrepreneurship utilizing "self-employment" (entrepreneur1) and "being his own boss" (entrepreneur2), respectively. The regression results are displayed in Table 8 . Secondly, the explanatory variables were changed. The variables "self-employment" (entrepreneur1) and "own boss" (entrepreneur2) were used to measure individual entrepreneurship. The regression results are shown in Table 8 . It can be seen that the conclusions of this paper maintain a good robustness.

https://doi.org/10.1371/journal.pone.0310188.t007

https://doi.org/10.1371/journal.pone.0310188.t008

(3) Estimation using sub-indicators of the core explanatory variables.

In order to test the effect of sub-indicators of the level of digital technology development on individual entrepreneurship, the sub-indicators of the digital technology development index are brought into the model for estimation here respectively. Table 9 reports the regression results of the indicators of the volume of courier business (KD). The income of the software industry (SOFT), the total amount of telecommunication services (DX), the number of end-of-year subscribers of mobile telephones (YD), the capacity of mobile telephone exchanges (RL), and the long-distance fibre optic cable line length indicator (GL) were regressed. It can be observed that, with the exception of the long-distance fibre optic cable line length indicator, all the other indicators exert a significant positive influence on individual entrepreneurial decision-making. This supports the main conclusions of this paper to a certain extent.

https://doi.org/10.1371/journal.pone.0310188.t009

Research findings and policy recommendations

Conclusions of the study and discussion.

This paper initially elucidates the mechanism by which the development of digital technology affects individual entrepreneurship theoretically and proposes research hypotheses. Secondly, it employs data from five periods of the China General Social Survey (CGSS) to test an econometric model and reaches the following conclusions: firstly, digital technology significantly increases individual entrepreneurial choices. Furthermore, the findings of the study remain robust after transforming estimation methods and changing the way variables are measured. Secondly, digital technology has the greatest impact on entrepreneurship among individuals with lower levels of education (i.e. those who have completed junior high school or below). It also has the second greatest impact on individuals with intermediate levels of education (i.e. those who have completed senior high school or secondary school). In contrast, it has the smallest impact on entrepreneurship among individuals with higher levels of education (i.e. those who have completed university college or above). Thirdly, the development of digital technology has a more pronounced effect on the promotion of entrepreneurship among individuals with urban household registration than among those with rural household registration. Fourth, regionally, the impact of digital technology on individual entrepreneurship is more pronounced in the East and Central regions.

In summary, this paper examines the phenomenon of labour entrepreneurship from the perspective of digital technology development. It broadens the scope of research on the factors influencing labour force entrepreneurship choices, enriches the theoretical content of digital technology and labour force entrepreneurship, and provides micro-empirical evidence for the theory of digital technology and entrepreneurship. The findings of the study will provide valuable references for government decision-making.

The findings of this paper are consistent with those of other current literatures. For example, studies have found that the Internet has a facilitating effect on entrepreneurial behaviour [ 10 ], that the construction of digital villages is conducive to entrepreneurship among rural residents [ 13 ], and that the use of robots and the development of digital finance are also conducive to entrepreneurship [ 20 ]. However, unlike these studies, which have all looked at one aspect of digital technology, the digital technology that is the subject of this paper is a larger category that expands and deepens these studies.

Policy recommendations

The findings of this paper provide suggestions for promoting "mass entrepreneurship" in China from a digital technology perspective, with obvious policy implications. Firstly, it is recommended that the level of digital technology development be accelerated. It is recommended that a plan for the development of digital technology in China be formulated and implemented. This should include the strengthening of strategic guidance and policy support for the development of digital technology, as well as the improvement of laws and regulations on digital technology market access, operation, management, innovation, security, and so forth. This will create a favourable market environment, thereby providing institutional safeguards for the development of digital technology. Furthermore, the construction of digital China should be viewed as an opportunity to strengthen the construction of digital technology infrastructure, with the basic network system being given priority for the promotion of the development of digital technology. The following is a summary of the measures taken by the government to promote the development of digital technology.

Secondly, the cultivation of entrepreneurial practice and entrepreneurial spirit among highly educated individuals should be strengthened. The findings of this study indicate that digital technology has the least impact on the employment decision-making of the labour force with higher education levels. In other words, the higher the education level, the weaker the promotional effect of digital technology on individual entrepreneurship. Consequently, the entrepreneurial enthusiasm of college graduates can be stimulated by strengthening the innovation and entrepreneurial practice of college students, cultivating their entrepreneurial spirit as well as entrepreneurial thinking, and encouraging college students with entrepreneurial thinking and entrepreneurial ability to actively participate in the entrepreneurial army.

Thirdly, in order to achieve a balance, we focus on enhancing the development of digital technology in rural and western China. Empirical evidence indicates that digital technology has a stronger impact on the entrepreneurial decision-making of the labour force in the eastern and central regions and in towns and cities, while its impact on the entrepreneurial choices of the labour force in the western region and rural areas is relatively weaker. This suggests that the current low level of digital technology development in China’s western and rural regions may be hindering the entrepreneurial activities of individual laborers. Consequently, it is imperative to prioritize the balanced development of digital technology in China, accelerate the advancement of digital technology in the western region, and gradually narrow the disparity between urban and rural areas in terms of digitization, in accordance with the Chinese government’s "mass entrepreneurship" policy.

It is important to note that the research presented in this paper is based on data from China. However, the effects of digital technology on entrepreneurship can be reasonably generalised to other countries and regions. Consequently, the recommendations for countermeasures presented in this paper can also serve as a reference for policy formulation in countries other than China.

Insufficient research and prospects

This paper examines the impact of digital technology development on labour entrepreneurship. However, the CGSS data only publishes codes that match provincial-level data, which limits the scope of the paper. This means that the paper is unable to find suitable exogenous shocks related to digital technology development to deal with the endogenous problems of this paper. This is a limitation of this paper.

Given these limitations, future research could consider utilizing data from other studies to determine the causal relationship between digital technology development and entrepreneurship with exogenous shocks to digital technology development. Furthermore, given that data elements are an important foundation for digital technology development, the relationship between data element development and entrepreneurship represents a promising direction for future research.

Supporting information

https://doi.org/10.1371/journal.pone.0310188.s001

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• 6. Moore P V, Woodcock J. Augmented Exploitation: Artificial Intelligence, Automation and Work. Pluto Press, 2021.

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The Reports of Death were created at field hospitals or by battlefield commanders and were later used by Sextons to complete Internment Records. The records were created or collected by the Assistant Manager for Public Education to inform the public of the activities of the Manhattan District and later the Atomic Energy Commission (AEC). The records also reflect the effort of the AEC to promote the peaceful uses of atomic energy. These rolls were created because the Cherokee citizenship of many ex-slaves of the Cherokee in Indian Territory was disputed by the Cherokee tribe. The establishment of their status was important in determining their right to live on Cherokee land and to share in certain annuity and other payment, including a special $75,000 award voted by Congress on October 19, 1888. A series of investigations was conducted to compile the rolls of the Cherokee Freedmen. These investigations were conducted by John W. Wallace, 1889-90; Leo E. Bennett, 1891-92; Marcus D. Shelby, 1893; James G. Dickson, 1895-96; and William Clifton, William Thompson, and Robert H. Kern, 1896-97. These records were created to provide a reference source for agency staff who wish to determine the name of the institution or the "chief of party" conducting a specific magnetics study. The minute books were created to record the court's activities on a daily basis, including both criminal and civil litigation. Previous Element Next Element Table of Contents Lifecycle Data Requirements Guide 490 IMAGES How to write variables in research paper Types of Research Variable in Research with Example Types of Research Variable in Research with Example How To Identify The Variables In A Research Study Types Of Variables In Research Ppt Types of variables in scientific research VIDEO Practical Research 2 Quarter 1 Module 3: Kinds of Variables and Their Uses Variables in Psychological Research MEDIATOR & MODERATOR VARIABLE| RESEARCH APTITUDE| Rachana mam| REDISCOVER EDUCATION #ugcnet Types of Variables Controlling of Variables-Research Methods-Psychology- Standardization,Participant & Situational How to Explain Variables COMMENTS Variables in Research Types of Variables in Research. Types of Variables in Research are as follows: Independent Variable. This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type ... Types of Variables in Research & Statistics Example (salt tolerance experiment) Independent variables (aka treatment variables) Variables you manipulate in order to affect the outcome of an experiment. The amount of salt added to each plant's water. Dependent variables (aka response variables) Variables that represent the outcome of the experiment. Independent & Dependent Variables (With Examples) While the independent variable is the " cause ", the dependent variable is the " effect " - or rather, the affected variable. In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable. Keeping with the previous example, let's look at some dependent variables ... Independent vs. Dependent Variables Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper. A dependent variable from one study can be the independent variable in another study, so it's important to pay attention to research design. Here are some tips for identifying each variable type. How to Easily Identify Independent and Dependent Variables in Research The independent variable precedes the dependent variable. The chosen stress-related coping techniques help to build resilience; thus, they occur earlier. Writing Style and Structure. Usually, the variables are first described in the introduction of a research paper and then in the method section. Variables in Research: Breaking Down the ... The Role of Variables in Research. In scientific research, variables serve several key functions: Define Relationships: Variables allow researchers to investigate the relationships between different factors and characteristics, providing insights into the underlying mechanisms that drive phenomena and outcomes. Establish Comparisons: By manipulating and comparing variables, scientists can ... Organizing Your Social Sciences Research Paper Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent. The variables should be outlined in the introduction of your paper and explained in more detail in the methods section. There are no ... Types of Variables in Research Compare your paper to billions of pages and articles with Scribbr's Turnitin-powered plagiarism checker. Run a free check. ... Types of Variables in Research | Definitions & Examples. Published on 19 September 2022 by Rebecca Bevans. Revised on 28 November 2022. In statistical research, a variable is defined as an attribute of an object of ... Variables Categorical variables are groups…such as gender or type of degree sought. Quantitative variables are numbers that have a range…like weight in pounds or baskets made during a ball game. When we analyze data we do turn the categorical variables into numbers but only for identification purposes…e.g. 1 = male and 2 = female. Types of Variables, Descriptive Statistics, and Sample Size Abstract. This short "snippet" covers three important aspects related to statistics - the concept of variables, the importance, and practical aspects related to descriptive statistics and issues related to sampling - types of sampling and sample size estimation. Keywords: Biostatistics, descriptive statistics, sample size, variables. Variables in Research Variables in Research. The definition of a variable in the context of a research study is some feature with the potential to change, typically one that may influence or reflect a relationship or ... A Student's Guide to the Classification and Operationalization of So, it is usual for research protocols to include many independent variables and many dependent variables in the generation of many hypotheses, as shown in Table 1. Pairing each variable in the "independent variable" column with each variable in the "dependent variable" column would result in the generation of these hypotheses. Importance of Variables in Stating the Research Objectives So, it is usual for research protocols to include many independent variables and many dependent variables in the generation of many hypotheses, as shown in Table 1. Pairing each variable in the "independent variable" column with each variable in the "dependent variable" column would result in the generation of these hypotheses. Research Variables: Types, Uses and Definition of Terms The purpose of research is to describe and explain variance in the world, that is, variance that. occurs naturally in the world or chang e that we create due to manipulation. Variables are ... Types of Variables and Commonly Used Statistical Designs Suitable statistical design represents a critical factor in permitting inferences from any research or scientific study.[1] Numerous statistical designs are implementable due to the advancement of software available for extensive data analysis.[1] Healthcare providers must possess some statistical knowledge to interpret new studies and provide up-to-date patient care. We present an overview of ... 15 Independent and Dependent Variable Examples Examples of Independent and Dependent Variables. 1. Gatorade and Improved Athletic Performance. A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve. Dependent Variable When writing a research paper or report, it is important to clearly define the dependent variable(s) in order to provide a clear understanding of the research question and methods used to answer it. In social sciences, such as psychology or sociology, the dependent variable may refer to behaviors, attitudes, or other measurable aspects of ... (PDF) Identifying Variables All research projects are based around variables. A variable is the characteristic or attribute of an individual, group, educational system, or the environment that is of interest in a research study. Types of Variables Research; Moderator Variable: The moderator variable affects the cause-and-effect relationship between the independent and dependent variables. As a result, the influence of the independent variable is in the presence of the moderator variable. Gender; Race; Class; Suppose you want to conduct a study, educational awareness of a specific area. What Are Variables in a Research Paper? Independent Variable. The independent variable, also known as the IV, is the variable that the researchers are manipulating in an experiment or quasi-experiment. It is also the label given to the "criterion" variable in certain types of regression analysis. For example, if a researcher has two groups of people watch either a happy film or a ... Examples of Variables in Research: 6 Noteworthy Phenomena Introduction. Definition of Variable. Examples of Variables in Research: 6 Phenomena. Phenomenon 1: Climate change. Phenomenon 2: Crime and violence in the streets. Phenomenon 3: Poor performance of students in college entrance exams. Phenomenon 4: Fish kill. Phenomenon 5: Poor crop growth. Phenomenon 6: How Content Goes Viral. Research Methods Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question: The impact of digital technology on entrepreneurship——Evidence from In this paper, control variables for individual characteristics and regional characteristics were selected. Individual characteristics: The first variable is the gender of the respondent, which is assigned a value of 1 for males and 0 for females. ... Insufficient research and prospects. This paper examines the impact of digital technology ... Function and Use Mandatory Repeatable Data Type Authority Level Available A/V Only Public Element No* No Variable Character Length (2000) None Series No Yes *Function and Use is mandatory for new descriptions of organizational records, but not for descriptions of personal papers or legacy descriptions. Definition: The description of why the archival materials were created. This element differs from Scope and ... essay homework paper phd review speech study thesis