Walden University Estimating

To prepare for this Discussion:

  • Review Warner’s Chapter 12 and Chapter 2 of the Wagner      course text and the media program found in this week’s Learning Resources      and consider the use of dummy variables.
  • Create a research question using the General Social      Survey dataset that can be answered by multiple regression. Using the SPSS      software, choose a categorical variable to dummy code as one of your      predictor variables.

Estimate a multiple regression model that answers your research question. Post your response to the following:

  1. What is your research question?
  2. Interpret the coefficients for the model, specifically      commenting on the dummy variable.
  3. Run diagnostics for the regression model. Does the model      meet all of the assumptions? Be sure and comment on what assumptions were      not met and the possible implications. Is there any possible remedy for      one the assumption violations?

Learning Resources

Required Readings

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.

  • Chapter 2, “Transforming Variables” 
  • 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. 

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)

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.

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.

Non-Normally Distributed Errors. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 41-49). Thousand Oaks, CA: SAGE Publications, Inc.

Fox, J. (1991). Regression diagnostics. Thousand Oaks, CA: SAGE Publications.

Discrete Data. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 62-67). Thousand Oaks, CA: SAGE Publications, Inc.

Nonconstant Error Variance. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 49-54). Thousand Oaks, CA: SAGE Publications, Inc.

Nonlinearity. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 54-62). Thousand Oaks, CA: SAGE Publications, Inc.

Outlying and Influential Data. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 22-41). Thousand Oaks, CA: SAGE Publications, Inc.

Fox, J. (Ed.). (1991). Regression diagnostics. Thousand Oaks, CA: SAGE Publications.

  • Chapter 3, “Outlying and Influential Data” (pp. 22–41)
  • Chapter 4, “Non-Normally Distributed Errors” (pp. 41–49)
  • Chapter 5, “Nonconstant Error Variance” (pp. 49–54)
  • Chapter 6, “Nonlinearity” (pp. 54–62)
  • Chapter 7, “Discrete Data” (pp. 62–67)

Note: You will access these chapters through the Walden Library databases.

Document: Walden University: Research Design Alignment Table

Required Media

Laureate Education (Producer). (2016m). Regression diagnostics and model evaluation [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 7 minutes.

In this media program, Dr. Matt Jones demonstrates regression diagnostics and model evaluation using the SPSS software.

Accessible player  –Downloads– Download Video w/CC Download Audio Download Transcript 

Laureate Education (Producer). (2016). Dummy variables [Video file]. Baltimore, MD: Author.

Note: This media program is approximately 12 minutes.

In this media program, Dr. Matt Jones demonstrates dummy variables using the SPSS software.

Accessible player  –Downloads– Download Video w/CC Download Audio Download Transcript 

Discrete Data. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 62-67). Thousand Oaks, CA: SAGE Publications, Inc.

Nonconstant Error Variance. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 49-54). Thousand Oaks, CA: SAGE Publications, Inc. 

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