Conducting All-Possible-Subsets for MANOVA and Factorial MANOVA: No Longer a Weekend Project

Conducting All-Possible-Subsets for MANOVA and Factorial MANOVA: No Longer a Weekend Project

Kim Nimon (The University of Texas at Tyler, USA), Linda Reichwein Zientek (Sam Houston State University, USA) and Amanda Kraha (Indiana University East, USA)
DOI: 10.4018/978-1-5225-5164-5.ch019


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. One way to address these concerns is with all possible subsets. However, not all common statistical packages easily facilitate this analysis and the analysis can be a weekend project. As such, the purpose of this chapter 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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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). For a MANOVA, consideration is given to the interrelations among variables (Huberty & Morris, 1989). According to Huberty and Morris (1989), “The basic MANOVA question is, Are there any overall (interaction, main) effects present?” (p. 304), then other research questions follow. These questions relate to “(a) determining outcome variable subsets that account for group separation; (b) determining the relative contribution to group separation of the outcome variables in the final subset; and (c) identifying underlying constructs associated with the obtained MANOVA results” (Huberty & Morris, 1989, p. 304).

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