MANOVA

Multivariate variate analysis of variance (MANOVA) is the statistical procedure of comparing the means of several groups rather than a single group as you would find in an ANOVA. It is appropriate to use a MANOVA if the IV has 2+ levels and there are 2+ DV. Assumptions for use of MANOVA include: normal distribution. linearity, homogeneity of variances and homogeneity of variances and covariances. [1].

contributed by Raymond Manka

For example, we may conduct a study where we look at math achievement based on standardized test scores and ELA acheivement based on standardized test scores between students in different socioeconomic groups (low, moderate, high). The two dependent variables would be math achievement vased on standardized test scores and ELA achievement based on standardized test scores. The independent variable is the socioeconomic status with three levels: low, moderate, and high. (Based on a case scenario provided by Dr. Frank Labanca)

Instead of a univariate F value, we would use a multivariate F value Wilk's λ.

contributed by Mary Fernand

Prior calculating a MANOVA (or other statistics) in SPSS, it may be necessary to "clean the data" in order to obtain a good sample of numbers. Here is a video that discusses how to clean the data in SPSS. While it may be easier to do this prior to importing to SPSS, this video to be a bit long, but helpful in understanding the process. https://www.youtube.com/watch?v=Ik4Dyn8e8vA

contributed by Sheri Prendergast

How is a MANOVA different than a ANOVA?

A MANOVA differs from ANOVA in that it allows the researcher to analyze data that involve more than one dependent variable. MANOVA can test hypotheses regarding the effect of one or more independent variables on two or more dependent variables.

Contributed by Joseph W. Sullivan

Why MANOVAs are a good test for dissertations

A rationale for using a MANOVA as the statistical test for our dissertations includes the following: 1. A MANOVA is a test that allows us to measure multiple dependent variables together, which is likely given the scope and scale of the quantitative data collection for dissertations; 2. Passing the Box’s M test for significance .05 (Meyers) or .01 (Huberty & Olenjnik), mitigates risk that we’ve committed a Type I error (LaBanca, 2019, slides 5 & 12).

References: LaBanca, F. (2019). Multivariate Analysis of Variance (MANOVA) [PowerPoint slides]. Retrieved from http://moodle.labanca.net/course/view.php?id=4.

contributed by Emily Kilbourn