0.17 18. multiple regression: 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 multiple regression procedure. For detailed information regarding the assumptions underlying use of a multiple regression analysis, readers are referred to the Hyperstats Online Statistics Textbook at http://davidmlane.com/hyperstat/ ; to the Electronic Statistics Textbook (2011) at http://www.statsoft.com/textbook/ ; or to Andy Field’s (2009) Discovering Statistics Using SPSS at http://www.amazon.com/Discovering-Statistics-Introducing-Statistical-Method/dp/1847879071/ref=sr_1_1?s=books&ie=UTF8&qid=1304967862&sr=1-1

Research questions for which a multiple regression analysis is appropriate involve determining variables that predict a continuous variable. For example, if you want to predict the life expectancy of individuals and you have archival data available (e.g., health history, gender), then a multiple regression analysis procedure could be utilized. Such a procedure could identify specific variables that are predictive of a long or of a short life expectancy. As such, interventions could be developed and targeted toward the variables that were statistically significant predictors. Other sample research questions for which a multiple regression analysis might be appropriate: (a) What factors predict high scores on a scholastic aptitude measure?; and (b) What factors are predictive of high scores on a life satisfaction scale?

For the purposes of this chapter, our research question is: "What scholastic variables predict students' Full Scale IQ?"