# One Way MANOVA

Statistics Solutions provides a data analysis plan template for the One Way MANOVA 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: One Way MANOVA

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

Research Question:

Do dependent variable 1 and dependent variable 2 differ by independent variable (group 1 vs. group 2)?

Ho: Dependent 1 and dependent variable 2 do not differ by (independent variable (group 1 vs. group 2).

Ha: Dependent 1 and dependent variable 2 differ by independent variable (group 1 vs. group 2).

Data Analysis

To examine the research question, a multivariate analysis of variance (MANOVA) will be conducted to assess if mean differences exist.  The MANOVA is an appropriate statistical analysis when the purpose of research is to assess if mean differences exist on more than one continuous dependent variable by one or more discrete independent variables.  For this research question, the continuous dependent variables are dependent variable 1 and dependent variable 2; the independent variable has the following groups (group 1 vs. group 2, …).  MANOVA assesses whether mean differences among groups on a combination of dependent variables are likely to have occurred by chance.  The MANOVA creates a linear combination of the dependent variables to create a grand mean and assesses whether there are group differences on the set of dependent variables.  The MANOVA uses the F test. The F-test allows researchers to make the overall comparison on whether group means differ.  If the obtained F-value is larger than the critical F, the null hypothesis is rejected.  The assumptions of normality and homogeneity of variance/covariance matrices will be assessed.  Normality assumes that the scores are normally distributed (e.g., 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.

Reference