Control variables
Account for more variance in multivariate models
When researchers say they want to "control" for a variable, the way they do this is to enter that variable into a multivariate model with an independent variable and a dependent variable to see if it changes their association. Control variables are chosen to make more valid inferences regarding treatment effects and outcomes, when taking secondary, tertiary, and ancillary variables and phenomena into consideration.
The way to "control for" a variable is to enter it into a multivariate model with other independent and dependent variables of interest. Control variables are chosen because the researcher has an informed hypothesis that the control variable is important when understanding treatment effects and needs to be accounted for in a multivariate analysis.
The way to "control for" a variable is to enter it into a multivariate model with other independent and dependent variables of interest. Control variables are chosen because the researcher has an informed hypothesis that the control variable is important when understanding treatment effects and needs to be accounted for in a multivariate analysis.
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Eric Heidel, Ph.D. will provide the following statistical consultingservices for undergraduate and graduate students at $50/hour. Secure checkout is available with Stripe, Venmo, Zelle, or PayPal.
- Statistical Analysis
- Research Design
- Sample Size Calculations
- Diagnostic Testing and Epidemiological Calculations
- Survey Design and Psychometrics