Also, differences between groups, with Standard Error, and Bonferroni corrected P-value and 95% Confidence Interval of the differences are reported. In the following tables, the marginal means (sometimes referred to as 'corrected means') with standard error and 95% Confidence Interval are given for all levels of the two factors. When the 2-factor interaction (FactorA*FactorB) is significant the effect of factor A is dependent on the level of factor B, and it is not recommended to interpret the means and differences between means (see below) of the main factors. If the calculated P-values for the two main factors A and B, or for the 2-factor interaction is less than the conventional 0.05 (5%), then the corresponding null hypothesis is rejected, and you accept the alternative hypothesis that there are indeed differences among groups. If this assumption is not met (P<0.05) the ANCOVA results are unreliable. The interpretation of ANCOVA and the associated adjusted means relies on the assumption of homogeneous regression slopes for the various groups (Huitema, 1980). If the Levene test is positive (P<0.05) then the variances in the groups are different (the groups are not homogeneous), and therefore the assumptions for ANCOVA are not met. Prior to the ANCOVA test, Levene's test for equality of variances is performed. Optionally, select a Test for Normal distribution of the residuals.

Filter: optionally, a filter in order to include only a selected subgroup of cases.

Factors: use one categorical variable for a one-way ANCOVA or two categorical variables for a two-way factorial ANCOVA.

Dependent variable: the (continuous) dependent variable.

The variable 'VarY' is the dependent variable and there is one covariate 'VarX'.

In this example (data from Wildt & Ahtola, 1978) data are entered for 2 factor variables named 'FactorA' and 'FactorB'. Analysis of covariance (ANCOVA) allows to compare one variable in 2 or more groups taking into account (or to correct for) variability of other variables, called covariates.Īnalysis of covariance combines one-way or two-way analysis of variance with linear regression (General Linear Model, GLM).

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