Topic: Multiple Regression Analysis
Topic: Multiple Regression Analysis
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.
- 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
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, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
- Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, and 8)
Walden University Library. (n.d.). Course Guide and Assignment Help for RSCH 8210. Retrieved from http://academicguides.waldenu.edu/rsch8210
For help with this week’s research, see this Course Guide and related weekly assignment resources.
Document: Walden University: Research Design Alignment Table
Document: Data Set 2014 General Social Survey (dataset file)
Use this dataset to complete this week’s Discussion.
Note: You will need the SPSS software to open this dataset.
Laureate Education (Producer). (2016g). Multiple regression [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 7 minutes.
In this media program, Dr. Matt Jones demonstrates multiple regression using the SPSS software. –Downloads–Download Video w/CCDownload AudioDownload Transcript
Skill Builder: Interpreting the Results from Regression Models
To access these Skill Builders, navigate back to your Blackboard Course Home page, and locate “Skill Builders” in the left navigation pane. From there, click on the relevant Skill Builder link for this week.
You are encouraged to click through these and all Skill Builders to gain additional practice with these concepts. Doing so will bolster your knowledge of the concepts you’re learning this week and throughout the course.
Discussion: Multiple Regression
This Discussion assists in solidifying your understanding of statistical testing by engaging in some data analysis. This week you will work with a real, secondary dataset to construct a research question, estimate a multiple regression model, and interpret the results.
Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.
To prepare for this Discussion:
- Review this week’s Learning Resources and media program related to multiple regression.
- Create a research question using the General Social Survey that can be answered by multiple regression.
BY DAY 3
Use SPSS to answer the research question. Post your response to the following:
- What is your research question?
- What is the null hypothesis for your question?
- What research design would align with this question?
- What dependent variable was used and how is it measured?
- What independent variable is used and how is it measured?
- What other variables were added to the multiple regression models as controls?
- What is the justification for adding the variables?
- If you found significance, what is the strength of the effect?
- Explain your results for a lay audience, explain what the answer to your research question.
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly