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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:
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…
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:
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:
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.
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:
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.
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:
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!
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.
Let’s jump into it…
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.
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.
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:
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.
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:
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!
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:
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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Methodology
Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.
In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.
Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.
Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.
What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.
An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
Independent variables are also called:
These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.
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There are two main types of independent variables.
In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.
You can apply just two levels in order to find out if an independent variable has an effect at all.
You can also apply multiple levels to find out how the independent variable affects the dependent variable.
You have three independent variable levels, and each group gets a different level of treatment.
You randomly assign your patients to one of the three groups:
A true experiment requires you to randomly assign different levels of an independent variable to your participants.
Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.
Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.
It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .
Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.
Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.
A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.
In statistics , dependent variables are also called:
The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.
Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.
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.
Use this list of questions to check whether you’re dealing with an independent variable:
Check whether you’re dealing with a dependent variable:
Independent and dependent variables are generally used in experimental and quasi-experimental research.
Here are some examples of research questions and corresponding independent and dependent variables.
Research question | Independent variable | Dependent variable(s) |
---|---|---|
Do tomatoes grow fastest under fluorescent, incandescent, or natural light? | ||
What is the effect of intermittent fasting on blood sugar levels? | ||
Is medical marijuana effective for pain reduction in people with chronic pain? | ||
To what extent does remote working increase job satisfaction? |
For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .
The type of test is determined by:
You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.
In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).
The type of visualization you use depends on the variable types in your research questions:
To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.
You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.
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.
Research bias
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.
In statistics, dependent variables are also called:
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!
Yes, but including more than one of either type requires multiple research questions .
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .
To ensure the internal validity of an experiment , you should only change one independent variable at a time.
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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.
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:
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;
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.
Educational resources and simple solutions for your research journey
A variable is an important element of research. It is a characteristic, number, or quantity of any category that can be measured or counted and whose value may change with time or other parameters.
Variables are defined in different ways in different fields. For instance, in mathematics, a variable is an alphabetic character that expresses a numerical value. In algebra, a variable represents an unknown entity, mostly denoted by a, b, c, x, y, z, etc. In statistics, variables represent real-world conditions or factors. Despite the differences in definitions, in all fields, variables represent the entity that changes and help us understand how one factor may or may not influence another factor.
Variables in research and statistics are of different types—independent, dependent, quantitative (discrete or continuous), qualitative (nominal/categorical, ordinal), intervening, moderating, extraneous, confounding, control, and composite. In this article we compare the first two types— independent vs dependent variables .
Table of Contents
Researchers conduct experiments to understand the cause-and-effect relationships between various entities. In such experiments, the entities whose values change are called variables. These variables describe the relationships among various factors and help in drawing conclusions in experiments. They help in understanding how some factors influence others. Some examples of variables include age, gender, race, income, weight, etc.
As mentioned earlier, different types of variables are used in research. Of these, we will compare the most common types— independent vs dependent variables . The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let’s explain this with an independent and dependent variable example : In a study to analyze the effect of antibiotic use on microbial resistance, antibiotic use is the independent variable and microbial resistance is the dependent variable because antibiotic use affects microbial resistance.( 1)
Here is a list of the important characteristics of independent variables .( 2,3)
Independent variables in research are of the following two types:( 4)
Quantitative independent variables differ in amounts or scales. They are numeric and answer questions like “how many” or “how often.”
Here are a few quantitative independent variables examples :
Qualitative independent variables are non-numerical variables.
A few qualitative independent variables examples are listed below:
A quantitative variable is represented by actual amounts and a qualitative variable by categories or groups.
Here are a few characteristics of dependent variables: ( 3)
Here are a few dependent variable examples :
Dependent variables are of two types:( 5)
These variables can take on any value within a given range and are measured on a continuous scale, for example, weight, height, temperature, time, distance, etc.
These variables are divided into distinct categories. They are not measured on a continuous scale so only a limited number of values are possible, for example, gender, race, etc.
The following table compares independent vs dependent variables .
How to identify | Manipulated or controlled | Observed or measured |
Purpose | Cause or predictor variable | Outcome or response variable |
Relationship | Independent of other variables | Influenced by the independent variable |
Control | Manipulated or assigned by researcher | Measured or observed during experiments |
Listed below are a few examples of research questions from various disciplines and their corresponding independent and dependent variables.( 6)
Genetics | What is the relationship between genetics and susceptibility to diseases? | genetic factors | susceptibility to diseases |
History | How do historical events influence national identity? | historical events | national identity |
Political science | What is the effect of political campaign advertisements on voter behavior? | political campaign advertisements | voter behavior |
Sociology | How does social media influence cultural awareness? | social media exposure | cultural awareness |
Economics | What is the impact of economic policies on unemployment rates? | economic policies | unemployment rates |
Literature | How does literary criticism affect book sales? | literary criticism | book sales |
Geology | How do a region’s geological features influence the magnitude of earthquakes? | geological features | earthquake magnitudes |
Environment | How do changes in climate affect wildlife migration patterns? | climate changes | wildlife migration patterns |
Gender studies | What is the effect of gender bias in the workplace on job satisfaction? | gender bias | job satisfaction |
Film studies | What is the relationship between cinematographic techniques and viewer engagement? | cinematographic techniques | viewer engagement |
Archaeology | How does archaeological tourism affect local communities? | archaeological techniques | local community development |
Experiments usually have at least two variables—independent and dependent. The independent variable is the entity that is being tested and the dependent variable is the result. Classifying independent and dependent variables as discrete and continuous can help in determining the type of analysis that is appropriate in any given research experiment, as shown in the table below. ( 7)
Chi-Square | t-test | ||
Logistic regression | ANOVA | ||
Phi | Regression | ||
Cramer’s V | Point-biserial correlation | ||
Logistic regression | Regression | ||
Point-biserial correlation | Correlation |
Here are some more research questions and their corresponding independent and dependent variables. ( 6)
What is the impact of online learning platforms on academic performance? | type of learning | academic performance |
What is the association between exercise frequency and mental health? | exercise frequency | mental health |
How does smartphone use affect productivity? | smartphone use | productivity levels |
Does family structure influence adolescent behavior? | family structure | adolescent behavior |
What is the impact of nonverbal communication on job interviews? | nonverbal communication | job interviews |
In addition to all the characteristics of independent and dependent variables listed previously, here are few simple steps to identify the variable types in a research question.( 8)
Let’s try out these steps with an example.
A researcher wants to conduct a study to see if his new weight loss medication performs better than two bestseller alternatives. He wants to randomly select 20 subjects from Richmond, Virginia, aged 20 to 30 years and weighing above 60 pounds. Each subject will be randomly assigned to three treatment groups.
To identify the independent and dependent variables, we convert this paragraph into a question, as follows: Does the new medication perform better than the alternatives? Here, the medications are the independent variable and their performances or effect on the individuals are the dependent variable.
Data visualization is the graphical representation of information by using charts, graphs, and maps. Visualizations help in making data more understandable by making it easier to compare elements, identify trends and relationships (among variables), among other functions.
Bar graphs, pie charts, and scatter plots are the best methods to graphically represent variables. While pie charts and bar graphs are suitable for depicting categorical data, scatter plots are appropriate for quantitative data. The independent variable is usually placed on the X-axis and the dependent variable on the Y-axis.
Figure 1 is a scatter plot that depicts the relationship between the number of household members and their monthly grocery expenses. 9 The number of household members is the independent variable and the expenses the dependent variable. The graph shows that as the number of members increases the expenditure also increases.
Let’s summarize the key takeaways about independent vs dependent variables from this article:
The following table lists the different types of variables used in research.( 10)
Categorical | Measures a construct that has different categories | gender, race, religious affiliation, political affiliation |
Quantitative | Measures constructs that vary by degree of the amount | weight, height, age, intelligence scores |
Independent (IV) | Measures constructs considered to be the cause | Higher education (IV) leads to higher income (DV) |
Dependent (DV) | Measures constructs that are considered the effect | Exercise (IV) will reduce anxiety levels (DV) |
Intervening or mediating (MV) | Measures constructs that intervene or stand in between the cause and effect | Incarcerated individuals are more likely to have psychiatric disorder (MV), which leads to disability in social roles |
Confounding (CV) | “Rival explanations” that explain the cause-and-effect relationship | Age (CV) explains the relationship between increased shoe size and increase in intelligence in children |
Control variable | Extraneous variables whose influence can be controlled or eliminated | Demographic data such as gender, socioeconomic status, age |
2. Why is it important to differentiate between independent vs dependent variables ?
Differentiating between independent vs dependent variables is important to ensure the correct application in your own research and also the correct understanding of other studies. An incorrectly framed research question can lead to confusion and inaccurate results. An easy way to differentiate is to identify the cause and effect.
3. How are independent and dependent variables used in non-experimental research?
So far in this article we talked about variables in relation to experimental research, wherein variables are manipulated or measured to test a hypothesis, that is, to observe the effect on dependent variables. Let’s examine non-experimental research and how variable are used. 11 In non-experimental research, variables are not manipulated but are observed in their natural state. Researchers do not have control over the variables and cannot manipulate them based on their research requirements. For example, a study examining the relationship between income and education level would not manipulate either variable. Instead, the researcher would observe and measure the levels of each variable in the sample population. The level of control researchers have is the major difference between experimental and non-experimental research. Another difference is the causal relationship between the variables. In non-experimental research, it is not possible to establish a causal relationship because other variables may be influencing the outcome.
4. Are there any advantages and disadvantages of using independent vs dependent variables ?
Here are a few advantages and disadvantages of both independent and dependent variables.( 12)
Advantages:
Disadvantages:
We hope this article has provided you with an insight into the use and importance of independent vs dependent variables , which can help you effectively use variables in your next research study.
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The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.
The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.
The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”
Here are several examples of independent and dependent variables in experiments:
If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).
This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.
Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis
Independent and dependent variables in research, can qualitative data have independent and dependent variables.
Experiments rely on capturing the relationship between independent and dependent variables to understand causal patterns. Researchers can observe what happens when they change a condition in their experiment or if there is any effect at all.
It's important to understand the difference between the independent variable and dependent variable. We'll look at the notion of independent and dependent variables in this article. If you are conducting experimental research, defining the variables in your study is essential for realizing rigorous research .
In experimental research, a variable refers to the phenomenon, person, or thing that is being measured and observed by the researcher. A researcher conducts a study to see how one variable affects another and make assertions about the relationship between different variables.
A typical research question in an experimental study addresses a hypothesized relationship between the independent variable manipulated by the researcher and the dependent variable that is the outcome of interest presumably influenced by the researcher's manipulation.
Take a simple experiment on plants as an example. Suppose you have a control group of plants on one side of a garden and an experimental group of plants on the other side. All things such as sunlight, water, and fertilizer being equal, both plants should be expected to grow at the same rate.
Now imagine that the plants in the experimental group are given a new plant fertilizer under the assumption that they will grow faster. Then you will need to measure the difference in growth between the two groups in your study.
In this case, the independent variable is the type of fertilizer used on your plants while the dependent variable is the rate of growth among your plants. If there is a significant difference in growth between the two groups, then your study provides support to suggest that the fertilizer causes higher rates of plant growth.
The independent variable is the element in your study that you intentionally change, which is why it can also be referred to as the manipulated variable.
You manipulate this variable to see how it might affect the other variables you observe, all other factors being equal. This means that you can observe the cause and effect relationships between one independent variable and one or multiple dependent variables.
Independent variables are directly manipulated by the researcher, while dependent variables are not. They are "dependent" because they are affected by the independent variable in the experiment. Researchers can thus study how manipulating the independent variable leads to changes in the main outcome of interest being measured as the dependent variable.
Note that while you can have multiple dependent variables, it is challenging to establish research rigor for multiple independent variables. If you are making so many changes in an experiment, how do you know which change is responsible for the outcome produced by the study? Studying more than one independent variable would require running an experiment for each independent variable to isolate its effects on the dependent variable.
This being said, it is certainly possible to employ a study design that involves multiple independent and dependent variables, as is the case with what is called a factorial experiment. For example, a psychological study examining the effects of sleep and stress levels on work productivity and social interaction would have two independent variables and two dependent variables, respectively.
Such a study would be complex and require careful planning to establish the necessary research rigor , however. If possible, consider narrowing your research to the examination of one independent variable to make it more manageable and easier to understand.
Let's consider an experiment in the social studies. Suppose you want to determine the effectiveness of a new textbook compared to current textbooks in a particular school.
The new textbook is supposed to be better, but how can you prove it? Besides all the selling points that the textbook publisher makes, how do you know if the new textbook is any good? A rigorous study examining the effects of the textbook on classroom outcomes is in order.
The textbook given to students makes up the independent variable in your experimental study. The shift from the existing textbooks to the new one represents the manipulation of the independent variable in this study.
In any experiment, the dependent variable is observed to measure how it is affected by changes to the independent variable. Outcomes such as test scores and other performance metrics can make up the data for the dependent variable.
Now that we are changing the textbook in the experiment above, we should examine if there are any effects.
To do this, we will need two classrooms of students. As best as possible, the two sets of students should be of similar proficiency (or at least of similar backgrounds) and placed within similar conditions for teaching and learning (e.g., physical space, lesson planning).
The control group in our study will be one set of students using the existing textbook. By examining their performance, we can establish a baseline. The performance of the experimental group, which is the set of students using the new textbook, can then be compared with the baseline performance.
As a result, the change in the test scores make up the data for our dependent variable. We cannot directly affect how well students perform on the test, but we can conclude from our experiment whether the use of the new textbook might impact students' performance.
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We can typically think of an independent variable as something a researcher can directly change. In the above example, we can change the textbook used by the teacher in class. If we're talking about plants, we can change the fertilizer.
Conversely, the dependent variable is something that we do not directly influence or manipulate. Strictly speaking, we cannot directly manipulate a student's performance on a test or the rate of growth of a plant, not without other factors such as new teaching methods or new fertilizer, respectively.
Understanding the distinction between a dependent variable and an independent variable is key to experimental research. Ultimately, the distinction can be reduced to which element in a study has been directly influenced by the researcher.
Given the potential complexities encountered in research, there is essential terminology for other variables in any experimental study. You might employ this terminology or encounter them while reading other research.
A control variable is any factor that the researcher tries to keep constant as the independent variable changes. In the plant experiment described earlier in this article, the sunlight and water are each a controlled variable while the type of fertilizer used is the manipulated variable across control and experimental groups.
To ensure research rigor, the researcher needs to keep these control variables constant to dispel any concerns that differences in growth rate were being driven by sunlight or water, as opposed to the fertilizer being used.
Extraneous variables refer to any unwanted influence on the dependent variable that may confound the analysis of the study. For example, if bugs or animals ate the plants in your fertilizer study, this was greatly impact the rates of plant growth. This is why it would be important to control the environment and protect it from such threats.
Finally, independent variables can go by different names such as subject variables or predictor variables. Dependent variables can also be referred to as the responding variable or outcome variable. Whatever the language, they all serve the same role of influencing the dependent variable in an experiment.
The use of the word " variables " is typically associated with quantitative and confirmatory research. Naturalistic qualitative research typically does not employ experimental designs or establish causality. Qualitative research often draws on observations , interviews , focus groups , and other forms of data collection that are allow researchers to study the naturally occurring "messiness" of the social world, rather than controlling all variables to isolate a cause-and-effect relationship.
In limited circumstances, the idea of experimental variables can apply to participant observations in ethnography , where the researcher should be mindful of their influence on the environment they are observing.
However, the experimental paradigm is best left to quantitative studies and confirmatory research questions. Qualitative researchers in the social sciences are oftentimes more interested in observing and describing socially-constructed phenomena rather than testing hypotheses .
Nonetheless, the notion of independent and dependent variables does hold important lessons for qualitative researchers. Even if they don't employ variables in their study design, qualitative researchers often observe how one thing affects another. A theoretical or conceptual framework can then suggest potential cause-and-effect relationships in their study.
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Statistics By Jim
Making statistics intuitive
By Jim Frost 15 Comments
In this post, learn the definitions of independent and dependent variables, how to identify each type, how they differ between different types of studies, and see examples of them in use.
Independent variables (IVs) are the ones that you include in the model to explain or predict changes in the dependent variable. The name helps you understand their role in statistical analysis. These variables are independent . In this context, independent indicates that they stand alone and other variables in the model do not influence them. The researchers are not seeking to understand what causes the independent variables to change.
Independent variables are also known as predictors, factors , treatment variables, explanatory variables, input variables, x-variables, and right-hand variables—because they appear on the right side of the equals sign in a regression equation. In notation, statisticians commonly denote them using Xs. On graphs, analysts place independent variables on the horizontal, or X, axis.
In machine learning, independent variables are known as features.
For example, in a plant growth study, the independent variables might be soil moisture (continuous) and type of fertilizer (categorical).
Statistical models will estimate effect sizes for the independent variables.
Relate post : Effect Sizes in Statistics
The nature of independent variables changes based on the type of experiment or study:
Controlled experiments : Researchers systematically control and set the values of the independent variables. In randomized experiments, relationships between independent and dependent variables tend to be causal. The independent variables cause changes in the dependent variable.
Observational studies : Researchers do not set the values of the explanatory variables but instead observe them in their natural environment. When the independent and dependent variables are correlated, those relationships might not be causal.
When you include one independent variable in a regression model, you are performing simple regression. For more than one independent variable, it is multiple regression. Despite the different names, it’s really the same analysis with the same interpretations and assumptions.
Determining which IVs to include in a statistical model is known as model specification. That process involves in-depth research and many subject-area, theoretical, and statistical considerations. At its most basic level, you’ll want to include the predictors you are specifically assessing in your study and confounding variables that will bias your results if you don’t add them—particularly for observational studies.
For more information about choosing independent variables, read my post about Specifying the Correct Regression Model .
Related posts : Randomized Experiments , Observational Studies , Covariates , and Confounding Variables
The dependent variable (DV) is what you want to use the model to explain or predict. The values of this variable depend on other variables. It is the outcome that you’re studying. It’s also known as the response variable, outcome variable, and left-hand variable. Statisticians commonly denote them using a Y. Traditionally, graphs place dependent variables on the vertical, or Y, axis.
For example, in the plant growth study example, a measure of plant growth is the dependent variable. That is the outcome of the experiment, and we want to determine what affects it.
If you’re reading a study’s write-up, how do you distinguish independent variables from dependent variables? Here are some tips!
How statisticians discuss independent variables changes depending on the field of study and type of experiment.
In randomized experiments, look for the following descriptions to identify the independent variables:
In observational studies, independent variables are a bit different. While the researchers likely want to establish causation, that’s harder to do with this type of study, so they often won’t use the word “cause.” They also don’t set the values of the predictors. Some independent variables are the experiment’s focus, while others help keep the experimental results valid.
Here’s how to recognize independent variables in observational studies:
Regardless of the study type, if you see an estimated effect size, it is an independent variable.
Dependent variables are the outcome. The IVs explain the variability or causes changes in the DV. Focus on the “depends” aspect. The value of the dependent variable depends on the IVs. If Y depends on X, then Y is the dependent variable. This aspect applies to both randomized experiments and observational studies.
In an observational study about the effects of smoking, the researchers observe the subjects’ smoking status (smoker/non-smoker) and their lung cancer rates. It’s an observational study because they cannot randomly assign subjects to either the smoking or non-smoking group. In this study, the researchers want to know whether lung cancer rates depend on smoking status. Therefore, the lung cancer rate is the dependent variable.
In a randomized COVID-19 vaccine experiment , the researchers randomly assign subjects to the treatment or control group. They want to determine whether COVID-19 infection rates depend on vaccination status. Hence, the infection rate is the DV.
Note that a variable can be an independent variable in one study but a dependent variable in another. It depends on the context.
For example, one study might assess how the amount of exercise (IV) affects health (DV). However, another study might study the factors (IVs) that influence how much someone exercises (DV). The amount of exercise is an independent variable in one study but a dependent variable in the other!
Regression analysis and ANOVA mathematically describe the relationships between each independent variable and the dependent variable. Typically, you want to determine how changes in one or more predictors associate with changes in the dependent variable. These analyses estimate an effect size for each independent variable.
Suppose researchers study the relationship between wattage, several types of filaments, and the output from a light bulb. In this study, light output is the dependent variable because it depends on the other two variables. Wattage (continuous) and filament type (categorical) are the independent variables.
After performing the regression analysis, the researchers will understand the nature of the relationship between these variables. How much does the light output increase on average for each additional watt? Does the mean light output differ by filament types? They will also learn whether these effects are statistically significant.
Related post : When to Use Regression Analysis
As I mentioned earlier, graphs traditionally display the independent variables on the horizontal X-axis and the dependent variable on the vertical Y-axis. The type of graph depends on the nature of the variables. Here are a couple of examples.
Suppose you experiment to determine whether various teaching methods affect learning outcomes. Teaching method is a categorical predictor that defines the experimental groups. To display this type of data, you can use a boxplot, as shown below.
The groups are along the horizontal axis, while the dependent variable, learning outcomes, is on the vertical. From the graph, method 4 has the best results. A one-way ANOVA will tell you whether these results are statistically significant. Learn more about interpreting boxplots .
Now, imagine that you are studying people’s height and weight. Specifically, do height increases cause weight to increase? Consequently, height is the independent variable on the horizontal axis, and weight is the dependent variable on the vertical axis. You can use a scatterplot to display this type of data.
It appears that as height increases, weight tends to increase. Regression analysis will tell you if these results are statistically significant. Learn more about interpreting scatterplots .
April 2, 2024 at 2:05 am
Hi again Jim
Thanks so much for taking an interest in New Zealand’s Equity Index.
Rather than me trying to explain what our Ministry of Education has done, here is a link to a fairly short paper. Scroll down to page 4 of this (if you have the inclination) – https://fyi.org.nz/request/21253/response/80708/attach/4/1301098%20Response%20and%20Appendix.pdf
The Equity Index is used to allocate only 4% of total school funding. The most advantaged 5% of schools get no “equity funding” and the other 95% get a share of the equity funding pool based on their index score. We are talking a maximum of around $1,000NZD per child per year for the most disadvantaged schools. The average amount is around $200-$300 per child per year.
My concern is that I thought the dependent variable is the thing you want to explain or predict using one or more independent variables. Choosing the form of dependent variable that gets a good fit seems to be answering the question “what can we predict well?” rather than “how do we best predict the factor of interest?” The factor is educational achievement and I think this should have been decided upon using theory rather than experimentation with the data.
As it turns out, the Ministry has chosen a measure of educational achievement that puts a heavy weight on achieving an “excellence” rating on a qualification and a much lower weight on simply gaining a qualification. My reading is that they have taken what our universities do when looking at which students to admit.
It doesn’t seem likely to me that a heavy weighting on excellent achievement is appropriate for targeting extra funding to schools with a lot of under-achieving students.
However, my stats knowledge isn’t extensive and it’s definitely rusty, so your thoughts are most helpful.
Regards Kathy Spencer
April 1, 2024 at 4:08 pm
Hi Jim, Great website, thank you.
I have been looking at New Zealand’s Equity Index which is used to allocate a small amount of extra funding to schools attended by children from disadvantaged backgrounds. The Index uses 37 socioeconomic measures relating to a child’s and their parents’ backgrounds that are found to be associated with educational achievement.
I was a bit surprised to read how they had decided on the dependent variable to be used as the measure of educational achievement, or dependent variable. Part of the process was as follows- “Each measure was tested to see the degree to which it could be predicted by the socioeconomic factors selected for the Equity Index.”
Any comment?
Many thanks Kathy Spencer
April 1, 2024 at 9:20 pm
That’s a very complex study and I don’t know much about it. So, that limits what I can say about it. But I’ll give you a few thoughts that come to mind.
This method is common in educational and social research, particularly when the goal is to understand or mitigate the impact of socioeconomic disparities on educational outcomes.
There are the usual concerns about not confusing correlation with causation. However, because this program seems to quantify barriers and then provide extra funding based on the index, I don’t think that’s a problem. They’re not attempting to adjust the socioeconomic measures so no worries about whether they’re directly causal or not.
I might have a small concern about cherry picking the model that happens to maximize the R-squared. Chasing the R-squared rather than having theory drive model selecting is often problematic. Chasing the best fit increases the likelihood that the model fits this specific dataset best by random chance rather than being truly the best. If so, it won’t perform as well outside the dataset used to fit the model. Hopefully, they validated the predicted ability of the model using other data.
However, I’m not sure if the extra funding is determined by the model? I don’t know if the index value is calculated separately outside the candidate models and then fed into the various models. Or does the choice of model affect how the index value is calculated? If it’s the former, then the funding doesn’t depend on a potentially cherry picked model. If the latter, it does.
So, I’m not really clear on the purpose of the model. I’m guessing they just want to validate their Equity Index. And maximizing the R-squared doesn’t really say it’s the best Index but it does at least show that it likely has some merit. I’d be curious how the took the 37 measures and combined them to one index. So, I have more questions than answers. I don’t mean that in a critical sense. Just that I know almost nothing about this program.
I’m curious, what was the outcome they picked? How high was the R-squared? And what were your concerns?
February 6, 2024 at 6:57 pm
Excellent explanation, thank you.
February 5, 2024 at 5:04 pm
Thank you for this insightful blog. Is it valid to use a dependent variable delivered from the mean of independent variables in multiple regression if you want to evaluate the influence of each unique independent variable on the dependent variables?
February 5, 2024 at 11:11 pm
It’s difficult to answer your question because I’m not sure what you mean that the DV is “delivered from the mean of IVs.” If you mean that multiple IVs explain changes in the DV’s mean, yes, that’s the standard use for multiple regression.
If you mean something else, please explain in further detail. Thanks!
February 6, 2024 at 6:32 am
What I meant is; the DV values used as parameters for multiple regression is basically calculated as the average of the IVs. For instance:
From 3 IVs (X1, X2, X3), Y is delivered as :
Y = (Sum of all IVs) / (3)
Then the resulting Y is used as the DV along with the initial IVs to compute the multiple regression.
February 6, 2024 at 2:17 pm
There are a couple of reasons why you shouldn’t do that.
For starters, Y-hat (the predicted value of the regression equation) is the mean of the DV given specific values of the IV. However, that mean is calculated by using the regression coefficients and constant in the regression equation. You don’t calculate the DV mean as the sum of the IVs divided by the number of IVs. Perhaps given a very specific subject-area context, using this approach might seem to make sense but there are other problems.
A critical problem is that the Y is now calculated using the IVs. Instead, the DVs should be measured outcomes and not calculated from IVs. This violates regression assumptions and produces questionable results.
Additionally, it complicates the interpretation. Because the DV is calculated from the IV, you know the regression analysis will find a relationship between them. But you have no idea if that relationship exists in the real world. This complication occurs because your results are based on forcing the DV to equal a function of the IVs and do not reflect real-world outcomes.
In short, DVs should be real-world outcomes that you measure! And be sure to keep your IVs and DV independent. Let the regression analysis estimate the regression equation from your data that contains measured DVs. Don’t use a function to force the DV to equal some function of the IVs because that’s the opposite direction of how regression works!
I hope that helps!
September 6, 2022 at 7:43 pm
Thank you for sharing.
March 3, 2022 at 1:59 am
Excellent explanation.
February 13, 2022 at 12:31 pm
Thanks a lot for creating this excellent blog. This is my go-to resource for Statistics.
I had been pondering over a question for sometime, it would be great if you could shed some light on this.
In linear and non-linear regression, should the distribution of independent and dependent variables be unskewed? When is there a need to transform the data (say, Box-Cox transformation), and do we transform the independent variables as well?
October 28, 2021 at 12:55 pm
If I use a independent variable (X) and it displays a low p-value <.05, why is it if I introduce another independent variable to regression the coefficient and p-value of Y that I used in first regression changes to look insignificant? The second variable that I introduced has a low p-value in regression.
October 29, 2021 at 11:22 pm
Keep in mind that the significance of each IV is calculated after accounting for the variance of all the other variables in the model, assuming you’re using the standard adjusted sums of squares rather than sequential sums of squares. The sums of squares (SS) is a measure of how much dependent variable variability that each IV accounts for. In the illustration below, I’ll assume you’re using the standard of adjusted SS.
So, let’s say that originally you have X1 in the model along with some other IVs. Your model estimates the significance of X1 after assessing the variability that the other IVs account for and finds that X1 is significant. Now, you add X2 to the model in addition to X1 and the other IVs. Now, when assessing X1, the model accounts for the variability of the IVs including the newly added X2. And apparently X2 explains a good portion of the variability. X1 is no longer able to account for that variability, which causes it to not be statistically significant.
In other words, X2 explains some of the variability that X1 previously explained. Because X1 no longer explains it, it is no longer significant.
Additionally, the significance of IVs is more likely to change when you add or remove IVs that are correlated. Correlated IVs is known as multicollinearity. Multicollinearity can be a problem when you have too much. Given the change in significance, I’d check your model for multicollinearity just to be safe! Click the link to read a post that wrote about that!
September 6, 2021 at 8:35 am
nice explanation
August 25, 2021 at 3:09 am
it is excellent explanation
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Chittaranjan andrade.
1 Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.
Students without prior research experience may not know how to conceptualize and design a study. This article explains how an understanding of the classification and operationalization of variables is the key to the process. Variables describe aspects of the sample that is under study; they are so called because they vary in value from subject to subject in the sample. Variables may be independent or dependent. Independent variables influence the value of other variables; dependent variables are influenced in value by other variables. A hypothesis states an expected relationship between variables. A significant relationship between an independent and dependent variable does not prove cause and effect; the relationship may partly or wholly be explained by one or more confounding variables. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. These and other concepts are explained with the help of clinically relevant examples.
This article explains the following concepts: Independent variables, dependent variables, confounding variables, operationalization of variables, and construction of hypotheses.
In any body of research, the subject of study requires to be described and understood. For example, if we wish to study predictors of response to antidepressant drugs (ADs) in patients with major depressive disorder (MDD), we might select patient age, sex, age at onset of MDD, number of previous episodes of depression, duration of current depressive episode, presence of psychotic symptoms, past history of response to ADs, and other patient and illness characteristics as potential predictors. These characteristics or descriptors are called variables. Whether or not the patient responds to AD treatment is also a variable. A solid understanding of variables is the cornerstone in the conceptualization and preparation of a research protocol, and in the framing of study hypotheses. This subject is presented in two parts. This article, Part 1, explains what independent and dependent variables are, how an understanding of these is important in framing hypotheses, and what operationalization of a variable entails.
Variables are defined as characteristics of the sample that are examined, measured, described, and interpreted. Variables are so called because they vary in value from subject to subject in the study. As an example, if we wish to examine the relationship between age and height in a sample of children, age and height are the variables of interest; their values vary from child to child. In the earlier example, patients vary in age, sex, duration of current depressive episode, and response to ADs. Variables are classified as dependent and independent variables and are usually analyzed as categorical or continuous variables.
Independent variables are defined as those the values of which influence other variables. For example, age, sex, current smoking, LDL cholesterol level, and blood pressure are independent variables because their values (e.g., greater age, positive for current smoking, and higher LDL cholesterol level) influence the risk of myocardial infarction. Dependent variables are defined as those the values of which are influenced by other variables. For example, the risk of myocardial infarction is a dependent variable the value of which is influenced by variables such as age, sex, current smoking, LDL cholesterol level, and blood pressure. The risk is higher in older persons, in men, in current smokers, and so on.
There may be a cause–effect relationship between independent and dependent variables. For example, consider a clinical trial with treatment (iron supplement vs placebo) as the independent variable and hemoglobin level as the dependent variable. In children with anemia, an iron supplement will raise the hemoglobin level to a greater extent than will placebo; this is a cause–effect relationship because iron is necessary for the synthesis of hemoglobin. However, consider the variables teeth and weight . An alien from outer space who has no knowledge of human physiology may study human children below the age of 5 years and find that, as the number of teeth increases, weight increases. Should the alien conclude that there is a cause–effect relationship here, and that growing teeth causes weight gain? No, because a third variable, age, is a confounding variable 1 – 3 that is responsible for both increase in the number of teeth and increase in weight. In general, therefore, it is more proper to state that independent variables are associated with variations in the values of the dependent variables rather than state that independent variables cause variations in the values of the dependent variables. For causality to be asserted, other criteria must be fulfilled; this is out of the scope of the present article, and interested readers may refer to Schunemann et al. 4
As a side note, here, whether a particular variable is independent or dependent will depend on the question that is being asked. For example, in a study of factors influencing patient satisfaction with outpatient department (OPD) services, patient satisfaction is the dependent variable. But, in a study of factors influencing OPD attendance at a hospital, OPD attendance is the dependent variable, and patient satisfaction is merely one of many possible independent variables that can influence OPD attendance.
Students must have a clear idea about what they want to study in order to conceptualize and frame a research protocol. The first matters that they need to address are “What are my research questions?” and “What are my hypotheses?” Both questions can be answered only after choosing the dependent variables and then the independent variables for study.
In the case of a student who is interested in studying predictors of AD outcomes in patients with MDD, treatment response is the dependent variable and patient and clinical characteristics are possible independent variables. So, the selection of dependent and independent variables helps defines the objectives of the study:
Note that in a formal research protocol, the student will need to state all the independent variables and not merely list examples. The student may also choose to include additional independent variables, such as baseline biochemical, psychophysiological, and neuroradiological measures.
A hypothesis is a clear statement of what the researcher expects to find in the study. As an example, a researcher may hypothesize that longer duration of current depression is associated with poorer response to ADs. In this hypothesis, the duration of the current episode of depression is the independent variable and treatment response is the dependent variable. It should be obvious, now, that a hypothesis can also be defined as the statement of an expected relationship between an independent and a dependent variable . Or, expressed visually, (independent variable) (arrow) (dependent variable) = hypothesis.
It would be a waste of time and energy to do a study to examine only one question: whether duration of current depression predicts treatment response. 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. Table 2 shows how this is done for age. Sets of hypotheses can likewise be constructed for the remaining independent and dependent variables in Table 1 . Importantly, the student must select one of these hypotheses as the primary hypothesis; the remaining hypotheses, no matter how many they are, would be secondary hypotheses. It is necessary to have only one hypothesis as the primary hypothesis in order to calculate the sample size necessary for an adequately powered study and to reduce the risk of false positive findings in the analysis. 5 In rare situations, two hypotheses may be considered equally important and may be stated as coprimary hypotheses.
Independent Variables and Dependent Variables in a Study on Sociodemographic and Clinical Prediction of Response of Major Depressive Disorder to Antidepressant Drug Treatment
• Age • Sex • Age at onset of major depressive disorder • Number of past episodes of depression • Past history of response to antidepressant drugs • Duration of current depressive episode • Baseline severity of depression • Baseline suicidality • Baseline melancholia • Baseline psychotic symptoms • Baseline soft neurological signs • Severity of depression • Global severity of illness • Subjective well-being • Quality of life • Everyday functioning |
Combinations of Age with Dependent Variables in the Generation of Hypotheses
1. Older age is associated with less attenuation in the severity of depression. 2. Older age is associated with less attenuation in the global severity of illness. 3. Older age is associated with less improvement in subjective well-being. 4. Older age is associated with less improvement in quality of life. 5. Older age is associated with less improvement in everyday functioning. |
In Table 1 , suicidality is listed as an independent variable and severity of depression, as a dependent variable. These variables need to be operationalized; that is, stated in a way that explains how they will be measured. Table 3 presents three ways in which suicidality can be measured and four ways in which (reduction in) the severity of depression can be measured. Now, each way of measurement in the “independent variable” column can be paired with a way of measurement in the “dependent variable” column, making a total of 12 possible hypotheses. In like manner, the many variables listed in Table 1 can each be operationalized in several different ways, resulting in the generation of a very large number of hypotheses. As already stated, the student must select only one hypothesis as the primary hypothesis.
Possible Ways of Operationalization of Suicidality and Depression
Independent Variable: Suicidality | Dependent Variable: Severity of Depression |
• Item score on the HAM-D • Item score on the MADRS • Beck scale for Suicide ideation total score | • MADRS total score • HAM-D total score • HAM-D response rate • HAM-D remission rate |
HAM-D: Hamilton Depression Rating Scale, MADRS: Montgomery–Asberg Depression Rating Scale.
Much thought should be given to the operationalization of variables because variables that are carelessly operationalized will be poorly measured; the data collected will then be of poor quality, and the study will yield unreliable results. For example, socioeconomic status may be operationalized as lower, middle, or upper class, depending on the patient’s monthly income, on the total monthly income of the family, or using a validated socioeconomic status assessment scale that takes into consideration income, education, occupation, and place of residence. The student must choose the method that would best suit the needs of the study, and the method that has the greatest scientific acceptability. However, it is also permissible to operationalize the same variable in many different ways and to include all these different operationalizations in the study, as shown in Table 3 . This is because conceptualizing variables in different ways can help understand the subject of the study in different ways.
Operationalization of variables requires a consideration of the reliability and validity of the method of operationalization; discussions on reliability and validity are out of the scope of this article. Operationalization of variables also requires specification of the scale of measurement: nominal, ordinal, interval, or ratio; this is also out of the scope of the present article. Finally, operationalization of variables can also specify details of the measurement procedure. As an example, in a study on the use of metformin to reduce olanzapine-associated weight gain, we may state that we will obtain the weight of the patient but fail to explain how we will do it. Better would be to state that the same weighing scale will be used. Still better would be to state that we will use a weighing instrument that works on the principle of moving weights on a levered arm, and that the same instrument will be used for all patients. And best would be to add that we will weigh patients, dressed in standard hospital gowns, after they have voided their bladder but before they have eaten breakfast. When the way in which a variable will be measured is defined, measurement of that variable becomes more objective and uniform
The next article, Part 2, will address what categorical and continuous variables are, why continuous variables should not be converted into categorical variables and when this rule can be broken, and what confounding variables are.
Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author received no financial support for the research, authorship, and/or publication of this article.
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Editor-in-Chief for Simply Psychology
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Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.
In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.
Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.
In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.
It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.
For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).
In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.
By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.
For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.
In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.
In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.
An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).
In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.
For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).
For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.
In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).
For the following hypotheses, name the IV and the DV.
1. Lack of sleep significantly affects learning in 10-year-old boys.
IV……………………………………………………
DV…………………………………………………..
2. Social class has a significant effect on IQ scores.
DV……………………………………………….…
3. Stressful experiences significantly increase the likelihood of headaches.
4. Time of day has a significant effect on alertness.
To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.
Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).
For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.
Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.
In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.
The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.
If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.
Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .
For the following hypotheses, name the IV and the DV and operationalize both variables.
1. Women are more attracted to men without earrings than men with earrings.
I.V._____________________________________________________________
D.V. ____________________________________________________________
Operational definitions:
I.V. ____________________________________________________________
2. People learn more when they study in a quiet versus noisy place.
I.V. _________________________________________________________
D.V. ___________________________________________________________
3. People who exercise regularly sleep better at night.
Yes, it is possible to have more than one independent or dependent variable in a study.
In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.
Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.
Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.
Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.
Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.
Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.
Yes, both quantitative and qualitative data can have independent and dependent variables.
In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.
The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.
So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.
Yes, the same variable can be independent in one study and dependent in another.
The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.
However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.
The role of a variable as independent or dependent can vary depending on the research question and study design.
What Are Independent and Dependent Variables?
Both the independent variable and dependent variable are examined in an experiment using the scientific method , so it's important to know what they are and how to use them.
In a scientific experiment, you'll ultimately be changing or controlling the independent variable and measuring the effect on the dependent variable. This distinction is critical in evaluating and proving hypotheses.
Below you'll find more about these two types of variables, along with examples of each in sample science experiments, and an explanation of how to graph them to help visualize your data.
An independent variable is the condition that you change in an experiment. In other words, it is the variable you control. It is called independent because its value does not depend on and is not affected by the state of any other variable in the experiment. Sometimes you may hear this variable called the "controlled variable" because it is the one that is changed. Do not confuse it with a control variable , which is a variable that is purposely held constant so that it can't affect the outcome of the experiment.
The dependent variable is the condition that you measure in an experiment. You are assessing how it responds to a change in the independent variable, so you can think of it as depending on the independent variable. Sometimes the dependent variable is called the "responding variable."
If you are having a hard time identifying which variable is the independent variable and which is the dependent variable, remember the dependent variable is the one affected by a change in the independent variable. If you write out the variables in a sentence that shows cause and effect, the independent variable causes the effect on the dependent variable. If you have the variables in the wrong order, the sentence won't make sense.
Independent variable causes an effect on the dependent variable.
Example : How long you sleep (independent variable) affects your test score (dependent variable).
This makes sense, but:
Example : Your test score affects how long you sleep.
This doesn't really make sense (unless you can't sleep because you are worried you failed a test, but that would be a different experiment).
There is a standard method for graphing independent and dependent variables. The x-axis is the independent variable, while the y-axis is the dependent variable. You can use the DRY MIX acronym to help remember how to graph variables:
D = dependent variable R = responding variable Y = graph on the vertical or y-axis
M = manipulated variable I = independent variable X = graph on the horizontal or x-axis
Test your understanding with the scientific method quiz .
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Explore the essential roles of independent and dependent variables in research. This guide delves into their definitions, significance in experiments, and their critical relationship. Learn how these variables are the foundation of research design, influencing hypothesis testing, theory development, and statistical analysis, empowering researchers to understand and predict outcomes of research studies.
Introduction.
At the very base of scientific inquiry and research design , variables act as the fundamental steps, guiding the rhythm and direction of research. This is particularly true in human behavior research, where the quest to understand the complexities of human actions and reactions hinges on the meticulous manipulation and observation of these variables. At the heart of this endeavor lie two different types of variables, namely: independent and dependent variables, whose roles and interplay are critical in scientific discovery.
Understanding the distinction between independent and dependent variables is not merely an academic exercise; it is essential for anyone venturing into the field of research. This article aims to demystify these concepts, offering clarity on their definitions, roles, and the nuances of their relationship in the study of human behavior, and in science generally. We will cover hypothesis testing and theory development, illuminating how these variables serve as the cornerstone of experimental design and statistical analysis.
The significance of grasping the difference between independent and dependent variables extends beyond the confines of academia. It empowers researchers to design robust studies, enables critical evaluation of research findings, and fosters an appreciation for the complexity of human behavior research. As we delve into this exploration, our objective is clear: to equip readers with a deep understanding of these fundamental concepts, enhancing their ability to contribute to the ever-evolving field of human behavior research.
In the realm of human behavior research, independent variables are the keystones around which studies are designed and hypotheses are tested. Independent variables are the factors or conditions that researchers manipulate or observe to examine their effects on dependent variables, which typically reflect aspects of human behavior or psychological phenomena. Understanding the role of independent variables is crucial for designing robust research methodologies, ensuring the reliability and validity of findings.
Independent variables are those variables that are changed or controlled in a scientific experiment to test the effects on dependent variables. In studies focusing on human behavior, these can range from psychological interventions (e.g., cognitive-behavioral therapy), environmental adjustments (e.g., noise levels, lighting, smells, etc), to societal factors (e.g., social media use). For example, in an experiment investigating the impact of sleep on cognitive performance, the amount of sleep participants receive is the independent variable.
Selecting an independent variable requires careful consideration of the research question and the theoretical framework guiding the study. Researchers must ensure that their chosen variable can be effectively, and consistently manipulated or measured and is ethically and practically feasible, particularly when dealing with human subjects.
Manipulating an independent variable involves creating different conditions (e.g., treatment vs. control groups) to observe how changes in the variable affect outcomes. For instance, researchers studying the effect of educational interventions on learning outcomes might vary the type of instructional material (digital vs. traditional) to assess differences in student performance.
Manipulating independent variables in human behavior research presents unique challenges. Ethical considerations are paramount, as interventions must not harm participants. For example, studies involving vulnerable populations or sensitive topics require rigorous ethical oversight to ensure that the manipulation of independent variables does not result in adverse effects.
Practical limitations also come into play, such as controlling for extraneous variables that could influence the outcomes. In the aforementioned example of sleep and cognitive performance, factors like caffeine consumption or stress levels could confound the results. Researchers employ various methodological strategies, such as random assignment and controlled environments, to mitigate these influences.
The dependent variable in human behavior research acts as a mirror, reflecting the outcomes or effects resulting from variations in the independent variable. It is the aspect of human experience or behavior that researchers aim to understand, predict, or change through their studies. This section explores how dependent variables are measured, the significance of their accurate measurement, and the inherent challenges in capturing the complexities of human behavior.
Dependent variables are the responses or outcomes that researchers measure in an experiment, expecting them to vary as a direct result of changes in the independent variable. In the context of human behavior research, dependent variables could include measures of emotional well-being, cognitive performance, social interactions, or any other aspect of human behavior influenced by the experimental manipulation. For instance, in a study examining the effect of exercise on stress levels, stress level would be the dependent variable, measured through various psychological assessments or physiological markers.
Measuring dependent variables in human behavior research involves a diverse array of methodologies, ranging from self-reported questionnaires and interviews to physiological measurements and behavioral observations. The choice of measurement tool depends on the nature of the dependent variable and the objectives of the study.
The reliability and validity of the measurement of dependent variables are critical to the integrity of human behavior research.
Ensuring reliability and validity often involves the use of established measurement instruments with proven track records, pilot testing new instruments, and applying rigorous statistical analyses to evaluate measurement properties.
Measuring human behavior presents challenges due to its complexity and the influence of multiple, often interrelated, variables. Researchers must contend with issues such as participant bias, environmental influences, and the subjective nature of many psychological constructs. Additionally, the dynamic nature of human behavior means that it can change over time, necessitating careful consideration of when and how measurements are taken.
Understanding the relationship between independent and dependent variables is at the core of research in human behavior. This relationship is what researchers aim to elucidate, whether they seek to explain, predict, or influence human actions and psychological states. This section explores the nature of this relationship, the means by which it is analyzed, and common misconceptions that may arise.
The relationship between independent and dependent variables can manifest in various forms—direct, indirect, linear, nonlinear, and may be moderated or mediated by other variables. At its most basic, this relationship is often conceptualized as cause and effect: the independent variable (the cause) influences the dependent variable (the effect). For instance, increased physical activity (independent variable) may lead to decreased stress levels (dependent variable).
Statistical analyses play a pivotal role in examining the relationship between independent and dependent variables. Techniques vary depending on the nature of the variables and the research design, ranging from simple correlation and regression analyses for quantifying the strength and form of relationships, to complex multivariate analyses for exploring relationships among multiple variables simultaneously.
A fundamental consideration in human behavior research is the distinction between causality and correlation. Causality implies that changes in the independent variable cause changes in the dependent variable. Correlation, on the other hand, indicates that two variables are related but does not establish a cause-effect relationship. Confounding variables may influence both, creating the appearance of a direct relationship where none exists. Understanding this distinction is crucial for accurate interpretation of research findings.
The complexity of human behavior and the myriad factors that influence it often lead to challenges in interpreting the relationship between independent and dependent variables. Researchers must be wary of:
This exploration highlights the importance of understanding independent and dependent variables in human behavior research. Independent variables act as the initiating factors in experiments, influencing the observed behaviors, while dependent variables reflect the results of these influences, providing insights into human emotions and actions.
Ethical and practical challenges arise, especially in experiments involving human participants, necessitating careful consideration to respect participants’ well-being. The measurement of these variables is critical for testing theories and validating hypotheses, with their relationship offering potential insights into causality and correlation within human behavior.
Rigorous statistical analysis and cautious interpretation of findings are essential to avoid misconceptions. Overall, the study of these variables is fundamental to advancing human behavior research, guiding researchers towards deeper understanding and potential interventions to improve the human condition.
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Identifying dependent and indepent variables, structure and writing style.
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 .
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:
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;
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.
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 .
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:
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;
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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Chapter 4: Measurement and Units of Analysis
When one variable causes another variable, we have what researchers call independent and dependent variables. In the example where gender was found to be causally linked to cell phone addiction, gender would be the independent variable (IV) and cell phone addiction would be the dependent variable (DV). An independent variable is one that causes another. A dependent variable is one that is caused by the other. Dependent variables depend on independent variables. If you are struggling to figure out which is the dependent and which is the independent variable, there is a little trick, as follows:
Ask yourself the following question: Is X dependent upon Y. Now substitute words for X and Y. For example, is the level of success in an online class dependent upon time spent online? Success in an online class is the dependent variable, because it is dependent upon something. In this case, we are asking if the level of success in an online class is dependent upon the time spent online. Time spent online is the independent variable.
Table 4.2 provides you with an opportunity to practice identifying the dependent and the independent variable.
Practice Exercise: Practice choosing the dependent and independent variables. Identify the dependent and independent variables from the questions below.
1. Is success in an online class dependent upon gender? | ||
2. Is the prevalence of post-traumatic stress disorder in Canada dependent upon the level of funding for early intervention? | ||
3. Is the reporting of incidents of high school bullying dependent upon anti-bullying programs in high school? | ||
4. Is the survival rate of female heart attack victims correlated to hospital emergency room procedures? |
Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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.
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.
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.
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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Published on 4 May 2022 by Pritha Bhandari . Revised on 17 October 2022.
In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.
Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.
Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.
What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs dependent variables, independent and dependent variables in research, visualising independent and dependent variables, frequently asked questions about independent and dependent variables.
An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.
Independent variables are also called:
These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.
There are two main types of independent variables.
In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.
You can apply just two levels in order to find out if an independent variable has an effect at all.
You can also apply multiple levels to find out how the independent variable affects the dependent variable.
You have three independent variable levels, and each group gets a different level of treatment.
You randomly assign your patients to one of the three groups:
A true experiment requires you to randomly assign different levels of an independent variable to your participants.
Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.
Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.
It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment.
Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women, and other.
Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.
A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it ‘depends’ on your independent variable.
In statistics , dependent variables are also called:
The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.
Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.
Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic 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.
Use this list of questions to check whether you’re dealing with an independent variable:
Check whether you’re dealing with a dependent variable:
Independent and dependent variables are generally used in experimental and quasi-experimental research.
Here are some examples of research questions and corresponding independent and dependent variables.
Research question | Independent variable | Dependent variable(s) |
---|---|---|
Do tomatoes grow fastest under fluorescent, incandescent, or natural light? | ||
What is the effect of intermittent fasting on blood sugar levels? | ||
Is medical marijuana effective for pain reduction in people with chronic pain? | ||
To what extent does remote working increase job satisfaction? |
For experimental data, you analyse your results by generating descriptive statistics and visualising your findings. Then, you select an appropriate statistical test to test your hypothesis .
The type of test is determined by:
You’ll often use t tests or ANOVAs to analyse your data and answer your research questions.
In quantitative research , it’s good practice to use charts or graphs to visualise the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).
The type of visualisation you use depends on the variable types in your research questions:
To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.
You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.
A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.
In statistics, dependent variables are also called:
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .
Yes, but including more than one of either type requires multiple research questions .
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .
To ensure the internal validity of an experiment , you should only change one independent variable at a time.
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.
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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
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. Just because 2 = female does not mean that females are better than males who are only 1. With quantitative data having a higher number means you have more of something. So higher values have meaning. |
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
Home » Independent Variable – Definition, Types and Examples
Table of Contents
Definition:
Independent variable is a variable that is manipulated or changed by the researcher to observe its effect on the dependent variable. It is also known as the predictor variable or explanatory variable
The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome. The relationship between the independent variable and the dependent variable is often analyzed using statistical methods to determine the strength and direction of the relationship.
Types of Independent Variables are as follows:
These variables are categorical or nominal in nature and represent a group or category. Examples of categorical independent variables include gender, ethnicity, marital status, and educational level.
These variables are continuous in nature and can take any value on a continuous scale. Examples of continuous independent variables include age, height, weight, temperature, and blood pressure.
These variables are discrete in nature and can only take on specific values. Examples of discrete independent variables include the number of siblings, the number of children in a family, and the number of pets owned.
These variables are dichotomous or binary in nature, meaning they can take on only two values. Examples of binary independent variables include yes or no questions, such as whether a participant is a smoker or non-smoker.
These variables are manipulated or controlled by the researcher to observe their effect on the dependent variable. Examples of controlled independent variables include the type of treatment or therapy given, the dosage of a medication, or the amount of exposure to a stimulus.
Following analysis methods that can be used to examine the relationship between an independent variable and a dependent variable:
This method is used to determine the strength and direction of the relationship between two continuous variables. Correlation coefficients such as Pearson’s r or Spearman’s rho are used to quantify the strength and direction of the relationship.
This method is used to compare the means of two or more groups for a continuous dependent variable. ANOVA can be used to test the effect of a categorical independent variable on a continuous dependent variable.
This method is used to examine the relationship between a dependent variable and one or more independent variables. Linear regression is a common type of regression analysis that can be used to predict the value of the dependent variable based on the value of one or more independent variables.
This method is used to test the association between two categorical variables. It can be used to examine the relationship between a categorical independent variable and a categorical dependent variable.
This method is used to compare the means of two groups for a continuous dependent variable. It can be used to test the effect of a binary independent variable on a continuous dependent variable.
There are four commonly used Measuring Scales of Independent Variables:
Here are some examples of independent variables:
Independent Variable | ||
---|---|---|
The variable that is changed or manipulated in an experiment. | The variable that is measured or observed and is affected by the independent variable. | |
The independent variable is the cause and influences the dependent variable. | The dependent variable is the effect and is influenced by the independent variable. | |
Typically plotted on the x-axis of a graph. | Typically plotted on the y-axis of a graph. | |
Age, gender, treatment type, temperature, time. | Blood pressure, heart rate, test scores, reaction time, weight. | |
The researcher can control the independent variable to observe its effects on the dependent variable. | The researcher cannot control the dependent variable but can measure and observe its changes in response to the independent variable. | |
To determine the effect of the independent variable on the dependent variable. | To observe changes in the dependent variable and understand how it is affected by the independent variable. |
Applications of Independent Variable in different fields are as follows:
The purpose of an independent variable is to manipulate or control it in order to observe its effect on the dependent variable. In other words, the independent variable is the variable that is being tested or studied to see if it has an effect on the dependent variable.
The independent variable is often manipulated by the researcher in order to create different experimental conditions. By varying the independent variable, the researcher can observe how the dependent variable changes in response. For example, in a study of the effects of caffeine on memory, the independent variable would be the amount of caffeine consumed, while the dependent variable would be memory performance.
The main purpose of the independent variable is to determine causality. By manipulating the independent variable and observing its effect on the dependent variable, researchers can determine whether there is a causal relationship between the two variables. This is important for understanding how different variables affect each other and for making predictions about how changes in one variable will affect other variables.
Here are some situations when an independent variable may be used:
Here are some of the characteristics of independent variables:
Independent variables have several advantages, including:
Independent variables also have several disadvantages, including:
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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.
The variables in a study of a cause-and-effect relationship are called the independent and dependent variables. The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect.
Research. Project paper and Feasibility Paper: Week 2 As preparation for the final research paper. formulate a theory about the correlation between measurable independent variables (causes) and one measurable dependent variable (the effect). Be sure to have at least two independent variables for proposed research paper. The topic proposal should include the following four items which serve as . . .
In a study to determine whether how long a student sleeps affects test scores. the independent variable is the length of time spent sleeping while the dependent variable is the test score. You want to compare brands of paper towels. to see which holds the most liquid. The independent variable in your experiment would be the brand of paper towel.
The reaction of the behavior that the researcher tries to predict- is the dependent variable. Every experiment has at least one independent and one dependent variable. Everything about the experimental situation expects the independent variable is held constant. that is. it …
Independent and dependent variables are both mathematical and statistical tools that are utilised in research and experiments by statisticians and researchers. Both variables enable statistician measure results. maintain control and draw defined conclusions. Therefore during research. the variables are manipulated by the experimenters.
In an experiment. the independent variable is the variable that is varied or manipulated by the researcher. and the dependent variable is the response that is measured. An independent variable is the presumed cause. whereas the dependent variable is the presumed effect.
Research paper Academic writing Starting the research process . . . which comes between the independent and dependent variables. The hours of study impacts the number of practice problems. which in turn impacts the exam score. The more hours a student studies. the more practice problems they will complete; the more practice problems completed. the higher the student’s exam …
This Independent Variables Essay example is published for educational and informational purposes only. If you need a custom essay or research paper on this topic please use our writing services. EssayEmpire. com offers reliable custom essay writing services that can help you to receive high grades and impress your professors with the quality of . . . Face recognition algorithm using python / For . . .
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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.
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 ...
The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect. Its value depends on changes in the independent variable. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on math test scores.
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 ...
The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let's explain this with an independent and dependent ...
Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. ... The Practice of Social Research (12th ed.) Wadsworth Publishing. ISBN -495-59841-. di Francia, G. Toraldo (1981). The Investigation of the Physical World. Cambridge University Press.
Independent and Dependent Variables, Explained With Examples. Written by MasterClass. Last updated: Mar 21, 2022 • 4 min read. In experiments that test cause and effect, two types of variables come into play. One is an independent variable and the other is a dependent variable, and together they play an integral role in research design.
Independent and dependent variables in research. In experimental research, a variable refers to the phenomenon, person, or thing that is being measured and observed by the researcher. A researcher conducts a study to see how one variable affects another and make assertions about the relationship between different variables.
Independent variables and dependent variables are the two fundamental types of variables in statistical modeling and experimental designs. ... That process involves in-depth research and many subject-area, theoretical, and statistical considerations. At its most basic level, you'll want to include the predictors you are specifically assessing ...
Pairing each variable in the "independent variable" column with each variable in the "dependent variable" column would result in the generation of these hypotheses. Table 2 shows how this is done for age. Sets of hypotheses can likewise be constructed for the remaining independent and dependent variables in Table 1. Importantly, the ...
In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations. One is called the dependent variable, and the other is the independent variable. In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome.
Independent and Dependent Variable Examples. In a study to determine whether the amount of time a student sleeps affects test scores, the independent variable is the amount of time spent sleeping while the dependent variable is the test score. You want to compare brands of paper towels to see which holds the most liquid.
The relationship between independent and dependent variables can manifest in various forms—direct, indirect, linear, nonlinear, and may be moderated or mediated by other variables. At its most basic, this relationship is often conceptualized as cause and effect: the independent variable (the cause) influences the dependent variable (the effect).
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 ...
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 ...
4.5 Independent and Dependent Variables. When one variable causes another variable, we have what researchers call independent and dependent variables. In the example where gender was found to be causally linked to cell phone addiction, gender would be the independent variable (IV) and cell phone addiction would be the dependent variable (DV).
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 ...
Research Topic Independent Variable Dependent Variable; All Research Topics: Manipulated by the researcher. Measured by the researcher. All Research Topics: What is being changed.
The number of hours the student studies is the independent variable because nothing directly affects the number of study hours. The grade the student earns in the class is the dependent variable because how much time the student commits to preparing can affect the grade. Related: 23 Research Databases for Professional and Academic Use.
The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect. Its value depends on changes in the independent variable. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on maths test scores.
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.
Definition: Independent variable is a variable that is manipulated or changed by the researcher to observe its effect on the dependent variable. It is also known as the predictor variable or explanatory variable. The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome.
Be sure to have at least two independent variables for proposed research paper. The topic proposal should include the following four items which serve as . . . In a study to determine whether how long a student sleeps affects test scores. the independent variable is the length of time spent sleeping while the dependent variable is the test score.