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Covariate

As explained in Effective Data Analysis

A covariate is a variable included in or considered by a statistical model because it may vary with a predictor or outcome.

A covariate is a factor that varies with both an independent variable and a dependent variable. When it is not accounted for, its effect can become mixed with the relationship being studied, producing misleading results. section 5.3.1

Covariates matter because they can:

  • Create an apparent relationship or distort the size and direction of an observed relationship. section 5.3.1
  • Be identified early through peer review of the study design, especially with domain-specific input. section 5.3.1
  • Be addressed through a combination of research-design strategies and statistical approaches. section 5.3.1

Recognizing a confounding covariate

A relationship between sleep duration and academic performance may be misleading if caffeine consumption varies with both. Likewise, geographic differences in online product preferences may reflect large differences in users’ median ages. section 5.3.1

How Effective Data Analysis covers it

How deep it goes
Defines confounding and outlines study-design and statistical responses without detailing specific adjustment procedures.
Examples
Illustrates covariates through caffeine in sleep research and median age in geographic comparisons of product preferences.
What you can do after
Apply study-design review and statistical approaches to reduce misleading conclusions from confounding.
How it connects
Connects covariates with confounding variables, independent variables, dependent variables, and non-parametric tests.

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This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.