Multiple Regression

Multiple Regression

Wk 9: Multiple Regression

Last week you explored the predictive nature of bivariate, simple linear regression. As you found out, and its name implies, bivariate regression only uses one predictor variable. As social scientists, we frequently have questions that require the use of multiple predictor variables. Moreover, we often want to include control variables (i.e., workforce experience, knowledge, education, etc.) in our model. Multiple regression allows the researcher to build on bivariate regression by including all of the important predictor and control variables in the same model. This, in turn, assists in reducing error and provides a better explanation of the complex social world.

In this week, you will examine multiple regression. In your examination, you will construct research questions, evaluate research design, and analyze results related to multiple regression.

Learning Objectives

Students will:

· Construct research questions

· Evaluate research design through research questions

· Analyze multiple regression

· Analyze measures multiple regression

· Evaluate significance of multiple regression

· Analyze results for multiple regression testing

· Analyze assumptions of correlation and bivariate regression (assessed in Week 10)

· Analyze implications for social change (assessed in Week 10)

· Evaluate research related to correlation and bivariate regression

Learning Resources

Required Readings

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: Sage Publications.

· Chapter 12, “Regression and Correlation” (pp. 325-371) (previously read in Week 8)

Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.

· Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, and 8)