# One-Way ANCOVA

Statistics Solutions provides a data analysis plan template for the one-way ANCOVA.  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: One-Way ANCOVA

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

Research Question:

After controlling for covariate 1, are there differences on dependent variable by independent variable (group 1 vs. group 2,...).

Ho: After controlling for covariate 1, there are no differences on dependent variable by independent variable (group 1 vs. group 2,...).

Ha: After controlling for covariate 1, there are differences on dependent variable by independent variable (group 1 vs. group 2,...).

Data Analysis

To investigate the research question, an Analysis of Covariance (one-way ANCOVA) will be conducted to assess differences between groups on a single dependent variable after controlling for the effects of one or more covariates.  The one-way ANCOVA is used to test the main effects of categorical independent variable on a continuous dependent variable while controlling for the effect of other continuous variables which co-vary with the dependent.  For this analysis, the dependent variable is dependent variable.  The control variables are covariate 1, covariate 2, ….  The covariates are chosen specifically because of their known effects on the dependent variable.  The purpose 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 the independent variable.  There is one independent variable with two levels (group 1 vs. group 2).

The F-test of significance will be used to assess for differences.  If significance is found, comparison of the original and adjusted group 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. The assumptions of ANCOVA are similar to those of ANOVA. The assumptions of normality and homogeneity of variance will be assessed.  Normality assumes that the scores are normally distributed (bell-shaped) and will be assessed using the One-Sample Kolmogorov-Smirnov test.  Homogeneity of variance assumes that both groups have equal error variances and will be assessed using Levene’s Test for the Equality of Error Variances.  The t-test will be two- tailed with the probability of rejecting the null hypothesis when it is true set at p < 0.05.  This ensures a 95% certainty that the differences did not occur by chance. The relationship between the covariate and the dependent variable should be linear and will be assessed with a scatterplot.

Reference