Factor
- A factor is an experimental variable, typically categorical, whose distinct values are called levels.
- Factors let researchers control for potential confounders and isolate the effect of a variable on an outcome.
- Multiple factors can be combined in factorial designs to examine interactions between them.
Definition
Section titled “Definition”In statistics, a factor is a variable that can be controlled or manipulated in an experiment. Factors are often categorical in nature, with each level representing a different category or group.
Explanation
Section titled “Explanation”Factors identify the categorical variables researchers assign or observe in an experiment (for example, types of treatment or participant groupings). By treating a variable as a factor, researchers can control for it in analysis and thereby isolate the specific effect of that variable on the outcome of interest. When experiments include more than one factor, factorial designs allow examination of both main effects and interactions among factors. In statistical models, factors are often encoded using dummy variables—binary variables that represent the different levels of a factor—so their effects can be estimated numerically.
Examples
Section titled “Examples”Exercise regime
Section titled “Exercise regime”In a study on the effects of different exercise regimes on weight loss, the type of exercise (aerobic vs. resistance training) is a factor with two levels: aerobic exercise and resistance training.
Gender in a medication study
Section titled “Gender in a medication study”In a study on the effects of a new medication on blood pressure, the gender of participants could be a factor with two levels: male and female.
Factorial design (2x2)
Section titled “Factorial design (2x2)”If a researcher also examines the effects of age on blood pressure alongside gender, the study design would be a 2x2 factorial design, with two levels of the gender factor (male and female) and two levels of the age factor (young and old).
Dummy variable representation
Section titled “Dummy variable representation”Factors can be represented using dummy variables. For example, the gender factor could be encoded with 0 representing male and 1 representing female.
Use cases
Section titled “Use cases”- Controlling for potential confounders to isolate the effect of a specific variable on an outcome.
- Using factorial designs to examine interactions between multiple factors and their combined effects on the outcome variable.
Notes or pitfalls
Section titled “Notes or pitfalls”- Treating relevant variables as factors enables explicit control for potential confounders in the analysis.
Related terms
Section titled “Related terms”- Levels (of a factor)
- Factorial design
- Interaction (between factors)
- Dummy variables
- Confounders