This problem is the major problem for Chapter 4, “Multiple Regression Analysis,” from the textbook. Stage 1: Definition Of The Research Problem In the first stage, we state the research problem, specify the variables to be used in the analysis, and specify the method for conducting the analysis: standard multiple regression, hierarchical regression, or stepwise … Continue reading “Illustration of Regression Analysis”
This is the sample problem presented in the text on pages 314 to 321. Consistent with the authors’ strategy for presenting the problem, we will divide the data set into a learning sample and a validation sample, after a brief overview of logistic regression. overview of logistic regression. Multiple regression requires that the dependent variable … Continue reading “An Illustrative Example of Logistic Regression”
In this exercise, we will focus on the research article by Theodore J. Eismeier, “Public Preferences About Government Spending: Partisan, Social, and Attitudinal Sources of Policy Differences.”Political Behavior, Volume 4, No. 2, 1982, 133-145. The data for this problem is GovernmentSpending.Sav, which contains the data recoded to meet the analytic requirements of the article. As … Continue reading “Problem from the Literature: Government Spending”
If you are using someone else’s data, as we are now, our first task is to examine the structure and contents of the data set. SPSS provides us with two tools for reviewing the contents of the data set: the Variables utility and the File Info utility which produces a data dictionary or code book … Continue reading “Exploring the Structure and Contents of a Data Set”
The purpose of this introductory example from pages 149-158 of the text is to demonstrate the basic concepts of regression analysis as one attempts to develop a predictive equation containing several independent variables. The dependent variable is the number of credit cards held by a family. The independent variables are family size and family income. … Continue reading “Example of Simple and Multiple Regression”