ABSTRACT
This chapter examines simultaneous regression to two other types of multiple regression, sequential regression and stepwise regression. The different types of multiple regressions serve different purposes and have different interpretations and different strengths and weaknesses. The chapter analyzes one problem several different ways to illustrate the differences in the three regression approaches. Simultaneous regression is primarily useful for explanatory research to determine the extent of the influence of one or more variables on some outcome. Simultaneous regression is useful for determining the relative influence of each of the variables studied. Sequential regression is another common method of multiple regressions and, like simultaneous regression, is often used in an explanatory manner. Stepwise multiple regressions is similar to sequential regression in that predictor variables are entered one at a time in a sequential order. The difference is that with stepwise multiple regressions the computer chooses the order of entry, rather than the researcher.
