Hierarchical Multiple Regression in Counseling Research: Common Problems and Possible Remedies

Multiple regression is a powerful set of methods for examining specific scientific hypotheses and relationships among experimental, quasiexperimental, and nonexperimental data. Typically, multiple regression is used as a data-analytic strategy to explain or predict a criterion (dependent) variable with a set of predictor (independent) variables. Wampold and Freund (1987) provided an important and useful overview of the practical uses of multiple regression procedures for counseling research. They also described the distinction between simultaneous, stepwise, and hierarchical regression. In short, simultaneous regression involves cases in which the investigator enters all of the predictors into the analysis at once. Stepwise regression involves choosing which predictors to analyze on the basis of statistics. Hierarchical regression involves theoretically based decisions for how predictors are entered into the analysis. Simultaneous and stepwise regression are typically used to explore and maximize prediction, whereas hierarchical regression is typically used to examine specific theoretically based hypotheses (Aron & Aron, 1999; B. H. Cohen, 2001). For an extensive description of how these methods of multiple regression are computed, please see Pedhazur (1982). Although Wampold and Freund (1987) noted that use of multiple regression procedures in counseling research was uncommon, it appears that their overall use has become more frequent in recent years. Wampold and Freund reported that only 14% of the research described in articles published in the Journal of Counseling Psychology used multiple regression procedures. During the years 1997–2001, of the quantitative research articles published in the Journal of Counseling Psychology and the Journal of Counseling & Development, 26.82% (70) have used some form of multiple regression (not including structural equation modeling, hierarchical linear modeling, canonical analysis, or any of the various analysis of variance procedures). Thus, the use of multiple regression in explaining relationships among counseling variables of interest has become quite common. Until the 1990s, stepwise regression was one of the most frequently used statistical methods in psychological research (Thompson, 1989). Like other researchers who have focused efforts on developing appropriate methods of multiple regression (J. Cohen & Cohen, 1983; Pedhazur, 1982), Wampold and Freund (1987) warned against the routine use of stepwise regression. Stepwise, forward, and backward methods of regression, have received more criticism than any of the other forms of multiple regression (Aron & Aron,