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Multivariate Analysis

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Multivariate Analysis

This topic teaches students about techniques for examining the relationship between a particular dependent variable and several different independent variables.

Multivariate regression analysis is an especially important topic because it allows additional factors to enter the analysis separately so that the effect of each on the dependent variable can be estimated. Multiple regression analysis is also used in an attempt to overcome omitted variable bias that can occur in simple regression analysis. For this reason, multivariate analysis is also frequently employed even when researchers are interested only or primarily in the effects of a specific independent variable.

Topic Learning Outcome: Upon mastering this topic, students will understand the strengths and potential weaknesses of multivariate regression analysis as a tool for understanding the causal relationship between variables. Students will understand the importance of multivariate regression analysis for social science research, will understand how multivariate analysis can be used to mitigate the problem of omitted variable bias.

Core Concepts Associated With This Topic: Multiple Regression Analysis; Environmental Validity; Data Collector Bias; External Validity; Hawthorne Effect; John Henry Effect; Natural Experiment; Omitted Variable Bias; Selection Bias

Recommended Reading

University of Toronto: PPG-1004

Stock, James H. and Mark W. Watson. 2011. Introduction to Econometrics, 3rd ed. Pearson/Addison-Wesley. Chapters: 8 (pp. 252-296), 9 (pp. 312-341), and 11 (pp. 381-394, 398-408)

Carleton: PADM-5114

Moore, D., McCabe G., & Craig, B. (2009). Introduction to the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company. Chapter 11.

Harvard Kennedy School: API-201

Moore, D., McCabe G., & Craig, B. (2014). Introduction to the Practice of Statistics, 8th Revised Edition. New York: W. H. Freeman and Company. Chapter 11.

NYU Wagner: GP-1011

Jan Blustein. SPSS The Wagner Way. Chapters 8 and 9.

Johnson Shoyama Graduate School of Public Policy: JSGS-803

Kahane, Leo H. Regression Basics. Thousand Oaks: Sage, 2008. Chapter 6, pp. 110-119.

Jeffrey E. Cohen, “Economic Perceptions and Executive Approval in Comparative Perspective,” Political Behaviour 26:1 (March, 2004), pp. 27-43.

M. Reza Nakhaie, “Electoral Participation in Municipal, Provincial and Federal Elections in Canada,”  Canadian Journal of Political Science 39:2 (June, 2006), pp. 363-390.

George Washington: PPPA-6002

De Veaux, Richard D., Paul F. Velleman, and David E. Bock. Stats: Data and Models. Boston: Pearson/Addison Wesley, 2005. Chapter 25

American: PUAD-605

Healey, J.H. (2011) Statistics: A Tool for Social Research. 9th Edition. Cengage Learning. Chapter 15 (the sections “controlling for a third variable” and “where do control variables come from?”) and Chapter 16.

UCLA: PP-208

Wooldridge, Jeffrey. Introductory Econometrics: A Modern Approach. Cengage Learning, 2012. Chapters 3, 4, 5.

Rutgers: Analytical Methods I and Analytical Methods II

Wang, XiaoHu. Performance analysis for public and nonprofit organizations. Jones & Bartlett Learning. Chapter 10.

Berman, Evan, and XiaoHu Wang (2011). Essential statistics for public managers and policy analysts. CQ Press. Chapter 15.

Healey (2011). Statistics: A Tool for Social Research, 9th Edition. Cengage Learning. Chapter 16.

Schroeder, Larry D., Sjoquist, David L., & Stephan, Paula E. (1986). “Understanding Regression Analysis: An Introductory Guide”. Quantitative Applications in the Social Sciences 57. Newbury Park: Sage Publications.

Possible Assessment Questions:

1.)   How is multivariate regression analysis different from simple regression analysis?

2.)   What does it mean to “control for” a specific variable when conducting a regression analysis?

3.)   A researcher is interested in studying the causal impact of higher education on lifetime earnings. Describe how the researcher might use multiple regression analysis to estimate the impact of education on earnings. What variables might the researcher control for and why?

Page last edited by Ben Eisen and Joshua Tan on 3 November 2014.


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