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  • Control Variables | What Are They & Why Do They Matter?

Control Variables | What Are They & Why Do They Matter?

Published on March 1, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s objectives , but is controlled because it could influence the outcomes.

Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomization or statistical control (e.g., to account for participant characteristics like age in statistical tests). Control variables can help prevent research biases like omitted variable bias from affecting your results.

Control variables

Table of contents

Why do control variables matter, how do you control a variable, control variable vs. control group, other interesting articles, frequently asked questions about control variables.

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables . This helps you establish a correlational or causal relationship between your variables of interest and helps avoid research bias .

Aside from the independent and dependent variables , all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results and affect the reliability of your arguments.

Control variables in experiments

In an experiment , a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

  • Timing of meals
  • Caffeine intake
  • Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations ). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

To account for other factors that are likely to influence the results, you also measure these control variables:

  • Marital status

Prevent plagiarism. Run a free check.

There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational studies or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

It’s possible that the participants who found the study through Facebook use more screen time during the day, and this might influence how alert they are in your study.

Standardized procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables , you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

  • To control for diet, fresh and frozen meals are delivered to participants three times a day.
  • To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
  • To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other types of variables .

“Controlling for a variable” means modelling control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

A control variable isn’t the same as a control group . Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

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

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

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A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

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4.1: Overview of the Control Variable

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  • Yang Lydia Yang
  • Kansas State University

In the first chapter, we talked about many different threats to internal validity of a research design and one of the control techniques is to build the extraneous variable into our research design. In this chapter, we will extend between-subjects design by looking at different ways to add in an extraneous or a control variable. Why do we need to add control variables? And what criteria should we use when selecting control variables? The main reason we want to include control variables is that the control variables are having an effect on the dependent variable we are studying. Since control variables are not the independent variables in our research, they could potentially confound the results of the study if left unattended. In other words, they can impose threats to the internal validity of the research design. By taking some measures to include the control variables, we are minimizing their effect on the dependent variable, which gives us more confidence to claim it is the independent variable, not the control variable, that causes changes in the dependent variable.

Using the example from the previous chapter, let's say we are conducting an experiment on the effect of cell phone use (yes vs. no) on driving ability. The independent variable is cell phone use with two treatment conditions (yes or no) and the dependent variable is driving ability. A potential control variable would be driving experience as driving experience is most likely to have an impact on driving ability. In order to reduce the potential threat driving experience has on driving ability, we can add it into our study as a control variable. Although it is not the focus of the study, control variable IS a part of your study as we know it influences the outcome variable. By including driving experience into our study, we can minimizing its effect on our research design, and be more confident it is the cell phone use, not driving experience, that leads to changes in driving ability. Therefore adding control variables can increase the internal validity of the research design.

How do we select control variables? Any variables can be potential control variables as long as there is good theoretical or empirical evidence(s) to show they influence the outcome variables. The nature of the variable is not a concern. The control variable can be categorical or continuous. Using the same example above, to measure driving experience, we can ask participants to identify which following level of driving experience represents them the best - seasoned, intermediate, or inexperienced. Or we can ask participants to identify how many months they have driven. Or if you are concerned about the accuracy of participants' own estimation, you can ask participants the age they received their driving license and do the calculation yourself. Regardless how you measure it, as long as the control variable is solid, as in it indeed influences the outcome variable, it can be included in the research study.

Then how do we use different types of control variables? There are two major ways to use control variables. One is randomized block design, which uses control variables at the design stage when we actively set up the experiment. Randomized block design typically uses categorical control variables. The other one is analysis of covariance, which uses control variables at the data analysis stage when we analyze the statistical data. Analysis of covariance typically uses continuous variables. We will look at each of them closely in the following sections.

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  • Knowledge Base
  • Methodology
  • What Are Control Variables | Definition & Examples

What Are Control Variables? | Definition & Examples

Published on 4 May 2022 by Pritha Bhandari . Revised on 16 June 2023.

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s aims but is controlled because it could influence the outcomes.

Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomisation or statistical control (e.g., to account for participant characteristics like age in statistical tests).

Control variables

Table of contents

Why do control variables matter, how do you control a variable, control variable vs control group, frequently asked questions about control variables.

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables . This helps you establish a correlational or causal relationship between your variables of interest.

Aside from the independent and dependent variables , all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results.

Control variables in experiments

In an experiment , a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

  • Timing of meals
  • Caffeine intake
  • Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations ). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

To account for other factors that are likely to influence the results, you also measure these control variables:

  • Marital status

Prevent plagiarism, run a free check.

There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

It’s possible that the participants who found the study through Facebook have more screen time during the day, and this might influence how alert they are in your study.

Standardised procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables, you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

  • To control for diet, fresh and frozen meals are delivered to participants three times a day.
  • To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
  • To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other variables.

“Controlling for a variable” means modelling control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

A control variable isn’t the same as a control group . Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

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The Palgrave Handbook of Research Design in Business and Management pp 239–261 Cite as

Control Variables: Problematic Issues and Best Practices

  • Leon Schjoedt &
  • Krittaya Sangboon  

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Schjoedt and Sangboon hold a positivist ideology. In this chapter they discuss an important aspect of the unit of analysis strategy in research designs: How does one account for or control factors that the researcher is aware of in the model but are beyond the focus of a within-groups or between-groups comparison? In other words, control factors are confounding, moderating, or mediating variables. The reason it is important to identify and control (or account for) these factors is so that the researcher can generalize to other populations, that is, by identifying the confounding factors that are present but are beyond the unit of analysis interest. When participants are samples for a between-group unit of analysis comparison, individual attributes in each participant often differ. Designing control variables is one approach among others to address this.

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Chapter 3 -- Introducing a Control Variable (Multivariate Analysis)

Human behavior is usually too complicated to be studied with only two variables. Often we will want to consider sets of three or more variables (called multivariate analysis ). We will want to consider three or more variables when we have discovered a relationship between two variables and want to find out 1) if this relationship might be due to some other factor, 2) how or why these variables are related, or 3) if the relationship is the same for different types of individuals. In each situation, we identify a third variable that we want to consider. This is called the control or the test variable . (Although it is possible to use several control variables simultaneously, we will limit ourselves to one control variable at a time.) To introduce a third variable, we identify the control variable and separate the cases in our sample by the categories of the control variable. For example, if the control variable is age divided into these two categories--younger and older, we would separate the cases into two groups. One group would consist of individuals who are younger and the other group would be those who are older. We would then obtain the crosstabulation of the independent and dependent variables for each of these age groups. Since there are two categories in this control variable, we obtain two partial tables , each containing part of the original sample. (If there were three categories in our control variable, for example, young, middle aged, and old, we would have three partial tables.) The process of using a control variable in the analysis is called elaboration and was developed at Columbia University by Paul Lazarsfeld and his associates. There are several different types of outcomes to the elaboration process. We will discuss each briefly. Table 2.3 showed that females were more likely than males to say they were willing to vote for a woman. Let's introduce a control variable and see what happens. In this example we are going to use age as the control variable. Table 3.1 is the three-variable table with voting preference as the dependent variable, sex as the independent variable, and age as the control variable. When we look at the older respondents (the left-hand partial table), we discover that this partial table is very similar to the original two-variable table (Table 2.3). The same is true for the younger respondents (the right-hand partial table). Each partial table is very similar to the original two-variable table. This is often referred to as replication because the partial tables repeat the original two-variable table (see Babbie 1997: 393-396). It is not necessary that they be identical; just that each partial table be basically the same as the original two-variable table. Our conclusion is that age is not affecting the relationship between sex and voting preference. In other words, the difference between males and females in voting preference is not due to age. Table 3.1 -- Voting Preference by Sex Controlling for Age   Older Younger   Male  %  Female  %  Total  %  Male  %  Female  %  Total  %  Voting Preference             Willing to Vote for a Woman 43.8  56.1  49.0  44.2  55.8  52.9  Not Willing to Vote for a Woman 56.2  43.9  51.0  55.8  44.2    100.0  100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  Since this is a hypothetical example, imagine a different outcome. Suppose we introduce age as a control variable and instead of getting Table 2.1, we get Table 3.2. How do these two tables differ? In Table 3.2, the percentage difference between males and females has disappeared in both of the partial tables. This is called explanation because the control variable, age, has explained away the original relationship between sex and voting preference. (We often say that the relationship between the two variables is spurious , not genuine.) When age is held constant, the difference between males and females disappears. The difference in the relationship does not have to disappear entirely, only be reduced substantially in each of the partial tables. This can only occur when there is a relationship between the control variable (age) and each of the other two variables (sex and voting preference). Next, we are interested in how or why the two variables are related. Suppose females are more likely than males to vote for a woman and that this difference cannot be explained away by age or by any other variable we have considered. We need to think about why there might be such a difference in the preferences of males and females. Perhaps females are more often liberal Table 3.2 -- Voting Preference by Sex Controlling for Age   Older Younger   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 32.9  33.9  33.3  65.8  66.9  66.7  Not Willing to Vote for a Woman 67.1  66.1  66.7  34.2  33.1  33.3    100.0 100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  than males, and liberals are more likely to say they would vote for a woman. So we introduce liberalism/conservatism as a control variable in our analysis. If females are more likely to support a woman because they are more liberal, then the difference between the preferences of men and women should disappear or be substantially reduced when liberalism/conservatism is held constant. This process is called interpretation because we are interpreting how one variable is related to another variable. Table 3.3 shows what we would expect to find if females supported the woman because they were more liberal. Notice that in both partial tables, the differences in the percentages between men and women has disappeared. (It is not necessary that it disappears entirely, but only that it is substantially reduced in each of the partial tables.) Table 3.3 -- Voting Preference by Sex Controlling for Liberalism/Conservatism   Older Younger   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 32.9  33.9  33.3  65.8  66.9  66.7  Not Willing to Vote for a Woman 67.1  66.1  66.7  34.2  33.1  33.3    100.0  100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  Finally, let's focus on the third of the situations outlined at the beginning of this section--whether the relationship is the same for different types of individuals. Perhaps the relationship between sex and voter preference varies with other characteristics of the individuals. Maybe among whites, females are more likely to prefer women candidates than the males are, but among blacks, there is little difference between males and females in terms of voter preference. This is the outcome shown in Table 3.4. This process is called specification because it specifies the conditions under which the relationship between sex and voter preference varies. In the earlier section on bivariate analysis, we discussed the use of chi square. Remember that chi square is a test of independence used to determine if there is a relationship between two variables. Chi square is used in multivariate analysis the same way it is in bivariate analysis. There will be a separate value of chi square for each partial table in the multivariate analysis. You should keep a number of warnings in mind. Chi square assumes that the expected frequencies for each cell are five or larger. As long as 80% of these expected frequencies are five or larger and no single expected frequency is very small, we don't have to worry. However, the expected frequencies often drop below five when the number of cases in a column or row gets too small. If this should occur, you will have to either recode (i.e., combine columns or rows) or eliminate a column or row from the table. Table 3.4 -- Voting Preference by Sex Controlling for Race   White African American   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 42.9  56.5  51.2  50.0  50.0  50.0  Not Willing to vote for a Woman 57.1  43.5  48.8  50.0  50.0  50.0    100.00  100.00  100.00  100.00  100.00  100.00    (310)  (490)  (800)  (50)  (50) (100)  Another point to keep in mind is that chi square is affected by the number of cases in the table. With a lot of cases it is easy to reject the null hypothesis of no relationship. With a few cases, it can be quite hard to reject the null hypothesis. Also, consider the percentages within the table. Look for patterns. Do not rely on any single piece of information. Look at the whole picture. We have concentrated on crosstabulation and chi square. There are other types of statistical analysis such as regression and log-linear analysis. When you have mastered these techniques, look at some other types of analysis. REFERENCES AND SUGGESTED READING Methods of Social Research Riley, Matilda White. 1963. Sociological Research I: A Case Approach . New York: Harcourt, Brace and World. Survey Research and Sampling Babbie, Earl R. 1990. Survey Research Methods (2 nd Ed.). Belmont, CA: Wadsworth. Babbie, Earl R. 1997. The Practice of Social Research (8 th Ed.). Belmont, CA: Wadsworth.   Statistical Analysis K noke, David, and George W. Bohrnstedt. 1991. Basic Social Statistics . Itesche, IL: Peacock. Riley, Matilda White. 1963. Sociological Research II Exercises and Manual . New York: Harcourt, Brace & World. Norusis, Marija J. 1997. SPSS 7.5 Guide to Data Analysis . Upper Saddle River, New Jersey: Prentice Hall. Elaboration and Causal Analysis Hirschi, Travis and Hanan C. Selvin. 1967. Delinquency Research--An Appraisal of Analytic Methods . New York: Free Press. Rosenberg, Morris. 1968. The Logic of Survey Analysis . New York: Basic Books. Data Sources The Field Institute. 1985. California Field Poll Study, July, 1985 . Machine-readable codebook. The Field Institute. 1991. California Field Poll Study, September, 1991 . Machine-readable codebook. The Field Institute. 1995. California Field Poll Study, February, 1995. Machine-readable codebook.

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant in an experiment.

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

  • They make it easier to reproduce the experiment.
  • The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

  • Duration of the experiment
  • Size and composition of containers
  • Temperature
  • Sample volume
  • Experimental technique
  • Chemical purity or manufacturer
  • Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

  • Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
  • Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
  • Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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  1. Control Variables

    Published on March 1, 2021 by Pritha Bhandari . Revised on June 22, 2023. A control variable is anything that is held constant or limited in a research study. It's a variable that is not of interest to the study's objectives, but is controlled because it could influence the outcomes.

  2. PDF Control variable use and reporting in operations management: a

    ning 2010-2020. Control variable analyses are conducted and organized into inter-disciplinary domains and DVs, providing researchers with insights in use of specic control variables from a micro-level perspective. Next, we identify strengths and weaknesses in current control variable use among a ten-year span from a macro per-spective.

  3. (PDF) Control Variable Use and Reporting in Macro and ...

    1. An Examination of Various Dimensions of Control Variable (CV) Use Both Across and Between Macro Studies Value Studies Studies Studies Separate section CV (80.6) (70.4) 611 425 812 Pearson w...

  4. 4.1: Overview of the Control Variable

    The main reason we want to include control variables is that the control variables are having an effect on the dependent variable we are studying. Since control variables are not the independent variables in our research, they could potentially confound the results of the study if left unattended. In other words, they can impose threats to the ...

  5. PDF Control Variables: Problematic Issues and Best Practices

    empirical business research is control variables. Due to the widespread use of control variables in the academic business lit-erature, we would expect that in graduate school a substantial amount of time is allocated toward the issue of control variables, at least to the same degree as attention that is allocated to the independent variables.

  6. Control Variables in Leadership Research: A Qualitative and

    questions by connecting the methodological work on control variables with the study of leadership. Leadership Research and Control Variables: Searching for Answers As alluded to in the introduction, the metatheory for including statistical controls is best known as the purification principle (for a detailed discussion, see Spector & Brannick ...

  7. A Critical Review and Best‐Practice Recommendations for Control

    PDF Tools Share Abstract The use of control variables plays a central role in organizational research due to practical difficulties associated with the implementation of experimental and quasi-experimental designs.

  8. Control Variables in Research

    Control variables refer to variables whose effects on an outcome variable are statistically adjusted in order to estimate causal effects of an explanatory variable. This article discusses...

  9. The selection, use, and reporting of control variables in international

    Control variables (CVs) constitute a central element of the research design of any empirical study. Confounding variables are likely to covary with the hypothesized focal independent variables thus limiting both the elucidation of causal inference as well as the explanatory power of the model (Pehazur & Schmelkin, 1991; Stone-Romero, 2009).

  10. PDF A Critical Review and Best‐Practice Recommendations for Control

    RECOMMENDATIONS FOR CONTROL VARIABLE USAGE JEREMY B. BERNERTH Louisiana State University HERMAN AGUINIS Indiana University The use of control variables plays a central role in organizational re-search due to practical difficulties associated with the implementation of experimental and quasi-experimental designs. As such, we conducted

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  12. [PDF] Control variables in marketing research

    Control variables in marketing research. M. Klarmann, Sven Feurer. Published 2018. Business. Marketing Science. TLDR. A synthesis of the theoretical knowledge on control variables with a review of control variable practices is combined to develop and discuss sixteen recommendations for control variable use in marketing research. Expand. View PDF.

  13. What Are Control Variables?

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    Control variables (CVs) constitute a central element of the research design of any empirical study. Confounding variables are likely to covary with the hypothesized focal independent variables thus limiting both the elucidation of causal inference as well as the explanatory power of the model ( Pehazur & Schmelkin, 1991Stone-Romero, 2009 ).

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  18. Control variables and causal inference: a question of balance

    1. The issue I present here should not be confused with the problem that stems from conditioning on a 'collider' variable. In the case I present here, X 1, X 2, and X 3, are correlated with, but do not have a causal influence on, one another.If X 2 and X 3 have a causal influence on X 1, then X 1 is a collider variable. There are a number of clear presentations on why using a collider as a ...

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  20. (PDF) Control Variable

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  22. What Is a Control Variable? Definition and Examples

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    Variables are therefore the names that are given to the variance we wish to explain and it is very critical to the research because the way the researcher uses or handles them in the research...