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Quasi-variance (qv) estimates are a statistical approach to overcome the reference category problem when estimating the effects of a categorical explanatory variable within a statistical model.

Quasi-variances are approximations of variances. Quasi-variances are statistics associated with the parameter estimates (coefficients) of the different levels of categorical explanatory variables within regression models. Quasi-variances should routinely be presented alongside parameter estimates to enable readers to assess differences between any combinations of parameter estimates for a categorical explanatory variable. The approach is beneficial because such comparisons are not usually possible without access to the full variance-covariance matrix for the estimates.

The underlying idea was first proposed by Ridout^[1] but the technique was set out by Professor David Firth^[2].

The suitability of this technique for social science data analysis has been demonstrated^[3].

An on-line tool for the calculation of quasi-variance estimates is available and a short technical description of the methodology is provided.

Quasi-variances can be calculated in Stata using the QV module^[4] and can also be calculated in R using the package qvcalc. An extended set of resources are with examples in Stata and SPSS are also available.