What Is a Control Variable? How to Choose and Include It in Your Research Model
What Is a Control Variable
A control variable is included in a quantitative model to hold constant a characteristic that may affect the dependent variable. This helps you assess the relationship between the independent and dependent variables more clearly. Common candidates include gender, age, income, education, length of product use and firm size.
For example, suppose you are studying the effect of service quality on satisfaction. Respondents' age may also be related to satisfaction. If you leave age out, the service quality coefficient may partly reflect age differences. When you add age to the model, you control for the effect of age and examine whether service quality is still related to satisfaction.
In a regression model, the control variable appears alongside the other explanatory variables:
Y = β0 + β1X1 + β2X2 + β3C1 + ε
Here, Y is the dependent variable, X1 and X2 are the independent variables, C1 is the control variable, and β3 is its coefficient. A control variable does not automatically become the main independent variable in the theoretical model. Its role depends on your research question and how you build the model.
If you still confuse an independent variable with a control variable, see what is an independent variable. The key distinction is that the main independent variable is usually tied to a research hypothesis, while a control variable is included to reduce the possibility that the model omits a relevant factor.
Why Control Variables Matter in Quantitative Research
Clarifying the Effect of the Independent Variable
When several factors are related to Y, the coefficient of X is estimated after the control variables have been entered into the model. You can read this as asking how strongly X is related to Y when the control characteristics are held constant.
This is especially useful when survey data contain substantial differences between groups. For example, respondents with high and low incomes may report different purchase intentions. If your study focuses on perceived quality, income can be included as a control variable so that the result for perceived quality is easier to interpret.
Reducing the Risk of Omitting a Relevant Variable
A model containing only the main independent variables may sometimes be too simple. If a background characteristic is related to both X and Y but is omitted, the result may be biased. Adding a control variable is one modelling response, but adding more variables does not automatically improve the model.
You need a reasonable theoretical or empirical reason for each variable. Entering every demographic variable simply because SPSS allows it makes the methods section difficult to defend. If the committee asks, you should be able to explain how the variable relates to the research question and how it was measured.
Supporting Comparisons Across Models
You can run two models. The first contains only the main independent variables. The second adds the control variables. If the coefficient of X changes substantially, this indicates that the control variables need to be discussed. Do not immediately conclude that the control variable caused the change, because you still need to consider the research design, measurement and regression conditions.
In your report, state clearly which variables are the main variables and which are controls. This classification helps readers distinguish statistical control from a mediator. A mediator explains the mechanism through which X affects Y, while a control variable remains in the model to adjust the relationship you want to examine. You can also read what is a mediator if your model contains a path from X to M and then from M to Y.
How Many Control Variables Are Acceptable
The question “how many control variables should I use” can be misleading. Control variables do not have one pass threshold like Cronbach's Alpha, AVE or outer loading. You need to assess the reason for including each variable, its coding, coefficient, p-value, VIF and the way the model changes.
The table below lists commonly used reference points for checking a model. Each point has meaning only in the stated context. It is not a requirement that every control variable meet every threshold.
| Check | Reference point | Source |
|---|---|---|
| VIF for a variable in the model | Below 5 | (Hair et al., 2019) |
| Regression sample size with m explanatory variables | At least 50 + 8m | (Tabachnick and Fidell, 2013) |
| Durbin-Watson in regression | From 1 to 3 | (Field, 2013) |
| Small f² effect size | 0.02 | (Cohen, 1988) |
| Medium f² effect size | 0.15 | (Cohen, 1988) |
| Large f² effect size | 0.35 | (Cohen, 1988) |
| R² in PLS-SEM | 0.25 weak, 0.50 moderate, 0.75 substantial | (Hair et al., 2011) |
A control variable with a p-value greater than 0.05 is not automatically a variable that has “failed.” The p-value describes the statistical evidence for the coefficient in your sample. It does not decide whether the variable belongs in the model. If you selected the variable for a clear theoretical reason, you can keep it and report that its coefficient is not statistically significant.
Conversely, a control variable with a p-value below 0.05 does not prove that the model is correct. You still need to check the direction of the effect, the confidence interval if available, VIF, coding and consistency with the research design. Do not remove a variable simply to add more stars to the results table.
How to Read Control Variables in the Output
Reading the Coefficients Table in SPSS
For linear regression, open Analyze > Regression > Linear. Move the dependent variable to Dependent, and move the independent and control variables to Independent(s). If you want to assess the additional effect of a group of variables, select Method: Enter and run the models in the order you specified in advance.
The key table is Coefficients. The B column contains the unstandardized coefficient, Std. Error is the standard error, Beta is the standardized coefficient, t is the test statistic, and Sig. is the p-value. For a categorical control variable such as gender, you must use appropriate coding. If the variable has several groups, you usually need to create dummy variables before entering it into the regression.
The following is illustrative output, not the result of a real study:
| Coefficients | B | Std. Error | Beta | t | Sig. |
|---|---|---|---|---|---|
| (Constant) | 1.204 | 0.312 | 3.859 | 0.000 | |
| Service quality | 0.428 | 0.071 | 0.391 | 6.028 | 0.000 |
| Perceived value | 0.267 | 0.068 | 0.251 | 3.926 | 0.000 |
| Age | 0.018 | 0.012 | 0.083 | 1.500 | 0.135 |
| Income | 0.094 | 0.041 | 0.126 | 2.293 | 0.023 |
In this illustrative table, age is a control variable with Sig. = 0.135. You can state that the age coefficient is not statistically significant at the 5% level in this sample. Income has Sig. = 0.023, so there is statistical evidence that income is related to the dependent variable after the other variables have been controlled. This result does not allow you to claim that income caused the change if your study uses a cross-sectional survey.
Do not look only at the Beta column. Check the direction of B, the p-value, VIF and the confidence interval if you asked SPSS to produce it. A positive coefficient for a gender control variable is meaningful only after you know which group was coded as 0 and which group was coded as 1.
Reading Control Variables in SmartPLS 4
If you run your model in SmartPLS 4, a control variable can be included in the structural model as a single-item observed variable or as a construct that matches the research design. Run Calculate > PLS-SEM Algorithm, then use Calculate > Bootstrapping to inspect Path Coefficients, T Statistics and P Values.
In SmartPLS, do not use the CFI, TLI or RMSEA thresholds for CB-SEM to evaluate a PLS-SEM model. For a PLS model, you generally report paths, R², VIF, f² and Q² according to the purpose of the study. The process of evaluating the measurement model before the structural model is presented in the PLS-SEM guidance by (Hair et al., 2022).
If the control variable is a single observed variable such as age, describe clearly how it was measured and coded. If you use a construct with several indicators, check the quality of the measurement model before interpreting its path to the dependent variable.
What to Do When a Control Variable Does Not Meet the Criteria
Check the Coding and Data Again
This is the first step and the easiest one to explain. Open Analyze > Descriptive Statistics > Frequencies to inspect the values of the categorical variable. Check missing data, out-of-range values and group coding. If a gender variable contains codes 1, 2 and 3 while the questionnaire has only two groups, that is a data error rather than a statistical finding.
For a continuous variable, inspect Descriptives or Explore to identify unusual values. Do not delete a data row simply because it makes the p-value less favourable. You need a cleaning rule established in advance, and you should record the number of observations removed and the reason for each removal.
Check for Multicollinearity
If VIF is high, the control variable may overlap strongly with an independent variable or another control variable. Review the correlation matrix, the definitions of the variables and how the scores were calculated. Do not combine or delete a variable based on one number before considering what the measurement represents.
In SmartPLS, check Inner VIF Values. A VIF below 5 is commonly reported according to (Hair et al., 2019). If VIF exceeds that point, review the model and your interpretation instead of rerunning it repeatedly to search for a better-looking result.
Keep the Variable and Report It Honestly
If a variable has a theoretical basis but its p-value is not significant, you can keep it in the model. State the coefficient, p-value and the limits of the conclusion clearly. Keeping the variable shows readers which factor you used to adjust the relationship of the main variable.
You should remove a variable only when there is a clear methodological reason, such as incorrect coding, too much missing data under a rule established in advance, or a mismatch with the specified model. If you remove it after examining the results, state clearly that this was a post-analysis adjustment and give the basis for the decision.
When to Reconsider the Research Model
If several control variables are very strongly correlated, the coefficients of the main variables reverse direction unexpectedly, or the results change substantially between model specifications, return to the research question. The model may contain too many variables, the constructs may overlap, or the sample size may be unsuitable. No SPSS operation can replace a well-reasoned model.
Distinguishing Control, Mediator and Moderator Variables
Control, mediator and moderator variables occupy different positions in a model. A control variable is included to adjust for the influence of a factor. A mediator explains the mechanism of an effect, usually through a sequence in which X affects M and M affects Y. A moderator changes the strength or direction of the X to Y relationship depending on the level of W.
For example, service quality is X and satisfaction is Y. Age may be a control variable. Perceived value may be a mediator if service quality affects perceived value and perceived value affects satisfaction. Price sensitivity may be a moderator if the effect of service quality on satisfaction differs between groups with low and high price sensitivity.
A moderator usually requires an interaction variable or an appropriate procedure. In SPSS, PROCESS Macro is a common option for mediation and moderation analysis. You can read what is PROCESS Macro and bootstrapping mediation in SPSS. If you are considering the Sobel test, see Sobel test analysis in SPSS, but present the limitations of each testing approach accurately.
A variable can be a control variable in one study and the main independent variable in another. The label does not determine its role. The research question, conceptual model and explanation of the variable's role are what you need to defend before the committee.
Common Mistakes
Entering Every Demographic Variable into the Model
Gender, age, income and education are often used as control variables, but not every study needs all four. Each variable needs a reason for inclusion, a measurement method and a coding scheme. A model with too many variables for the sample size may produce unstable results.
Calling the Control Variables Independent Variables Everywhere
If the hypotheses test only the effects of X1 and X2 on Y, but Chapter 4 calls age and income the main independent variables, readers will have difficulty following the analysis. Use consistent terminology in the model, hypothesis table, results table and discussion.
Relying Only on the p-value
Read the p-value together with the coefficient, direction of the effect, confidence interval, VIF and theoretical basis. Do not delete a variable with a large p-value and rerun the model repeatedly until the main variable becomes significant. That approach increases the risk that your explanation will be inconsistent.
Failing to State the Reference Group
For a categorical variable, the coefficient depends on the reference group. If you do not state which group was coded 0 and which was coded 1, readers cannot tell which groups the positive coefficient compares. This is a small detail, but it is often raised during the defence.
Frequently asked questions
What is a control variable in SPSS?
In SPSS, a control variable is entered into the same regression equation as the main independent variables to adjust for its influence when estimating the dependent variable. SPSS does not have a separate button called “control variable.” You place the variable in the Independent(s) list and explain its role in the model.
How many control variables should I use?
The right number changes from study to study. Choose enough variables to match the theoretical model, research question and sample size. For regression, you can refer to the sample-size requirement of 50 + 8m from (Tabachnick and Fidell, 2013), where m is the number of explanatory variables.
Does a control variable need a p-value below 0.05?
It is not required. A p-value below 0.05 indicates statistical evidence for the coefficient in the sample at the 5% level, while a p-value above 0.05 does not make the variable wrong if it has a theoretical basis. Report the result accurately and explain its limits.
How is a control variable different from a mediator?
A control variable is added to adjust for the influence of a factor. A mediator explains the mechanism from X to Y and usually lies on the path from X to M to Y. If you want to test a mechanism, do not simply enter M as a control variable and call that mediation analysis.
Should I remove a control variable when VIF is high?
A high VIF is a sign that you need to check multicollinearity, but it is not an automatic instruction to delete the variable. Review the correlations, variable definitions, measurement method and theoretical role. If you revise the model, record the reason and report the model version used in the thesis.
How to write this in your thesis
Open your data file now and create a table containing the name of each control variable, its coding, the reason for inclusion, and its VIF, coefficient and p-value before writing Chapter 4. If you need to run the model on your own .sav or .csv file, DoThesis M4 analysis supports this step in SPSS and SmartPLS.