0.13 14. discriminant analysis: assumptions

About the Authors

• John R. Slate is a Professor at Sam Houston State University where he teaches Basic and Advanced Statistics courses, as well as professional writing, to doctoral students in Educational Leadership and Counseling. His research interests lie in the use of educational databases, both state and national, to reform school practices. To date, he has chaired and/or served over 100 doctoral student dissertation committees. Recently, Dr. Slate created a website ( Writing and Statistical Help ) to assist students and faculty with both statistical assistance and in editing/writing their dissertations/theses and manuscripts.
• Ana Rojas-LeBouef is a Literacy Specialist at the Reading Center at Sam Houston State University where she teaches developmental reading courses. Dr. LeBoeuf recently completed her doctoral degree in Reading, where she conducted a 16-year analysis of Texas statewide data regarding the achievement gap. Her research interests lie in examining the inequities in achievement among ethnic groups. Dr. Rojas-LeBouef also assists students and faculty in their writing and statistical needs on the Writing and Statistical Help website.

In this set of steps, readers will learn how to conduct a canonical discriminant analysis procedure. For detailed information regarding the assumptions underlying use of a discriminant analysis, readers are referred to the Hyperstats Online Statistics Textbook at (External Link) ; to the Electronic Statistics Textbook (2011) at (External Link) ; or to Andy Field’s (2009) Discovering Statistics Using SPSS at (External Link)&ie=UTF8&qid=1304967862&sr=1-1

Research questions for which a discriminant analysis procedure is appropriate involve determining variables that predict group membership. For example, if two groups of persons are present such as completers and non-completers and archival data are available, then a discriminant analysis procedure could be utilized. Such a procedure could identify specific variables that differentiate group membership. As such, interventions could be developed and targeted toward the variables that predicted group membership. Other sample research questions for which a discriminant analysis might be appropriate: (a) What factors differentiates successful from unsuccessful students?; (b) What factors differentiate delinquents from nondelinquents?; (c) What set of test scores best differentiates students with LD, students who are failing, and students with MR?; and (d) What set of factors differentiates drop-outs from persisters?

For purposes of this chapter, our research question is: “What scholastic variables differentiate boys from girls?”

First, open up the dataset you intend to analyze for your canonical discriminant analysis.