## Description

Part 1

Answer a social research question with multiple regression. As you begin the Assignment, be sure and pay close attention to the assumptions of the test. Specifically, make sure the variables are metric level variables.- Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose).
- Based on the dataset you chose, construct a research question that can be answered with a multiple regression analysis.Write a 1- to 2-page analysis of your multiple regression results for each research question. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be.Use proper APA format, citations, and referencing for your analysis, research question, and display of output.
- Part 2
- Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) .
- Consider the following: Create a research question with metric variables and one variable that requires dummy coding. Estimate the model and report results.
**Note:**You are expected to perform regression diagnostics and report that as well. Then write a 2 page analysis of your multiple regression using dummy variables results for each research question. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be. Use proper APA format, citations, and referencing for your analysis, research question, and display of output. #### Required Readings

Wagner, W. E. (2016).

*Using IBM® SPSS® statistics for research methods and social science statistics*(6th ed.). Thousand Oaks, CA: Sage Publications.- Chapter 2, “Transforming Variables” (pp. 14–32)
- Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, 8, and 9)

Allison, P. D. (1999).

*Multiple regression: A primer*. Thousand Oaks, CA: Pine Forge Press/Sage Publications.Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.- Chapter 6, “What are the Assumptions of Multiple Regression?” (pp. 119–136)

Allison, P. D. (1999).

*Multiple regression: A primer*. Thousand Oaks, CA: Pine Forge Press/Sage Publications.Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.- Chapter 7, “What can be done about Multicollinearity?” (pp. 137–152)

Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

Warner, R. M. (2012).

*Applied statistics from bivariate through multivariate techniques*(2nd ed.). Thousand Oaks, CA: Sage Publications.Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.- Chapter 12, “Dummy Predictor Variables in Multiple Regression”

## Explanation & Answer

Attached.

Running Head: TESTING FOR MULTIPLE REGRESSION

Testing for Multiple Regression

Institutional affiliation

Date

TESTING FOR MULTIPLE REGRESSION

2

PART ONE

Multiple regression is identified as an extension of simple linear regression. Multiple regression

is used when predicting the value of a variable when two or more other variables are involved in

the analysis. The predicted variable is the dependent variable or the outcome. In this analysis, I

selected the High School Longitudinal Study dataset. The research question selected for the

analysis is; “Can student’s mathematics self-efficacy be predicted based on the parent’s highest

educational level and the years a math teacher has taught high school math?” To analyze our

research question, the dependent variable selected was “student’s mathematics selfefficacy” while the independent variables were, “parent’s highest educational level” and “years

a math teacher has taught high school math.”

Multiple regression, in this case, will be significant in predicting if the independent variables

have an impact on the dependent variable (Allison, 1999).

From the first table, an indication of the descriptive statistics of the variables identified from the

study has been included. A second table is an indication of the correlation coefficient value of

the variables. For instance, from the table, there is a low correlation value of .135 based on

“student’s mathematics self-efficacy�...