All-Possible-Subsets for MANOVA and Factorial MANOVAs: Less than a Weekend Project

All-Possible-Subsets for MANOVA and Factorial MANOVAs: Less than a Weekend Project

Kim Nimon (University of Texas at Tyler, Tyler, TX, USA), Linda Reichwein Zientek (Sam Houston State University, Huntsville, TX, USA) and Amanda Kraha (Indiana University East, Richmond, IN, USA)
DOI: 10.4018/IJAVET.2016040107
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Abstract

Multivariate techniques are increasingly popular as researchers attempt to accurately model a complex world. MANOVA is a multivariate technique used to investigate the dimensions along which groups differ, and how these dimensions may be used to predict group membership. A concern in a MANOVA analysis is to determine if a smaller subset of variables may be used in the classification functions without any loss of explanatory power when precision of parameter estimates or parsimony needs to be addressed (cf. Huberty, 1984; Huberty & Olejnik, 2006). One way to address these concerns is through the use of all possible subsets. However, not all common statistical packages easily facilitate this analysis, and the analysis can be a weekend project (Huberty & Olejnik, 2006). As such, the purpose of the current paper is to examine and demonstrate R and SPSS solutions to conduct an all-possible-subsets MANOVA, including all-possible-subsets factorial MANOVA.
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Manova

Multivariate analyses are conducted when a researcher has a desire to consider group differences among several dependent variables simultaneously. A MANOVA is an extension of analysis of variance (ANOVA) in that, instead of examining if a variable depends on group membership, several theorized variables are examined simultaneously to determine if those variables depend on group membership (i.e., independent variable). Thus, a MANOVA is a multivariate analysis that allows researchers to address whether scores on a set of dependent variables differ as a function of a grouping variable (e.g., intervention, gender, race). When researchers decide to conduct a MANOVA, dependent variables are theoretically or empirically related and ideally both (Weinfurt, 1995). Advantages to conducting a MANOVA versus multiple ANOVAs is that a MANOVA (a) helps control for Type I error and (b) takes into account the pattern covariation among the dependent variables. As noted by Thompson (1991), one of the most important reasons for conducting multivariate methods, even more so than limiting the inflation of Type I error rates, is that “multivariate methods best honor the reality to which the researchers is purportedly trying to generalize” (p. 80).

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