Kovariatene

Kovariatene refers to a set of variables included in a statistical analysis to account for influences on the outcome variable other than the main variable of interest. In English-language statistics, they are typically called covariates, and the term is common in Norwegian, Swedish, and related Nordic literature.

Their primary roles are to adjust for confounding, reduce bias, and improve the precision of estimated effects. By including kovariatene, researchers aim to isolate the association between the primary predictor and the outcome when other factors may influence both.

Covariates can be continuous, binary, or categorical, and they may be measured at baseline or change over time. Examples include age, sex, smoking status, body mass index, or educational level. They are used not only to control for confounding but also to improve model specification and predictive accuracy.

Covariate selection is a critical step. It should be guided by substantive theory and prior evidence, with care to avoid unnecessary variables that reduce statistical power or introduce multicollinearity. Missing data on kovariatene require appropriate handling, such as imputation or principled complete-case analyses.

In practice, kovariatene are included in regression models—linear, logistic, or Cox—as well as in design-based approaches like propensity score adjustment or stratification. The concept aligns with the general statistical notion of covariates used to account for additional factors influencing the outcome.