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Quasi-Variance
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 but the technique was set out by Professor David Firth.

The suitability of this technique for social science data analysis has been demonstrated.

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 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.