Repeated Measures ANCOVA

Statistics Solutions provides a data analysis plan template for the repeated measures ANCOVA analysis.  You can use this template to develop the data analysis section of your dissertation or research proposal.

The template includes research questions stated in statistical language, analysis justification and assumptions of the analysis.  Simply edit the blue text to reflect your research information and you will have the data analysis plan for your dissertation or research proposal.

Data Analysis Plan: Repeated Measures ANCOVA

Copy and paste the following into a word document to use as your data analysis plan template.


Research Question

RQ: After controlling for covariate 1, are there differences on dependent variable by time (time 1 vs. time 2)?

H0: After controlling for covariate 1, there is no difference on dependent variable by time (time 1 vs. time 2).

Ha: After controlling for covariate 1, there is a difference on dependent variable by time (time 1 vs. time 2).

Data Analysis

To examine the research question, a repeated-measures analysis of covariance (ANCOVA) will be conducted to assess if mean differences exist on dependent variable by time (time 1 vs. time 2) after controlling for covariate 1….  An analysis of covariance (ANCOVA) is used to assess in one dependent variable measured several times after controlling for the effects of one or more covariates.  The covariates are chosen specifically because of their known effects on the dependent variable.  The purpose of ANCOVA is to partial-out the effects of those variables on the dependent variable to determine if the effects are strictly due to the covariate or if the differences are independent of the effects of that covariate.  With the repeated-measures ANCOVA, differences will be assessed by time (time 1 vs. time 2).

The repeated measures ANCOVA is used to test the effects of a continuous dependent variable measured several times while controlling for the effect of other continuous variables which co-vary with the dependent variable.  The F-test of significance is used to assess the effects of the covariate(s) and time.   If significance is found, comparison of the original and adjusted means can provide information about the role of the covariates.  Because predictable variances known to be associated with the dependent variable are removed from the error term, ANCOVA increases the power of the F test for the main effect or interaction.  Essentially, it removes the undesirable variance in the dependent variable.  The assumptions of ANCOVA include: the dependent variable must be continuous/interval and normally distributed, which will be checked with skewness and kurtosis values; the relationship between the covariate(s) and the dependent variable should be linear, which will be assessed by a scatterplot; and sphericity, which will be assessed through a Mauchly’s Test of Sphericity.


Statistics Solutions. (2013). Data analysis plan: Repeated Measures ANCOVA [WWW Document]. Retrieved from