Introduction to Multiple Regression

  • Scott M. Lynch


In the previous chapters, we have focused on relationships between only two variables at a time. Most relationships that we are interested in social science research are more complicated, however, than can be understood with only bivariate analyses. In some cases, the relationship between two variables depends entirely on a third variable, and so a bivariate analysis can be misleading. In some cases, the relationship between two variables depends on, or operates through, additional variables. In this chapter, we will discuss multiple regression. In multiple regression analysis, a single outcome variable is modeled as a linear combination of as many additional variables as desired. Multiple regression is sometimes used simply to understand factors that are relevant to predicting an outcome, but it is also used in science to help establish that the relationship between two variables is a causal one and to understand complex relationships that simply cannot be understood with bivariate analyses. In this chapter, before introducing the details of multiple regression, we will discuss causal thinking in order to lay the foundation for seeing why multivariate analyses are necessary.


Parental Education Multiple Regression Model Social Science Research Causal Claim Indirect Association 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Agin, D. (2006). Junk science: An overdue indictment of government, industry, and faith groups that twist science for their own gain. New York: Thomas Dunne Books.Google Scholar
  2. Babbie, E. (2004). The practice of social research (10th ed.). Belmont: Thomson Wadsworth.Google Scholar
  3. Bonevac, D. (2003). Deduction: Introductory symbolic logic (2nd ed.). Malden: Blackwell.Google Scholar
  4. Bunch, B. (1997). Mathematical fallacies and paradoxes. Mineola: Dover.MATHGoogle Scholar
  5. Campbell, S. K. (2004). Flaws and fallacies in statistical thinking. Mineoloa: Dover.Google Scholar
  6. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago: Rand-McNally.Google Scholar
  7. Cleveland, W. S. (1993). Visualizing data. Summit: Hobart Press.Google Scholar
  8. Coyne, J. A. (2009). Why evolution is true. New York: Viking.Google Scholar
  9. Davis, J. A. (1985). The logic of causal order (Sage University paper series on quantitative applications in the social sciences, series no. 07–55). Beverly Hills: Sage.Google Scholar
  10. DeGroot, M. H., & Schervish, M. J. (2012). Probability and statistics (4th ed.). Boston: Addison-Wesley.Google Scholar
  11. Dickens, W. T., & Flynn, J. R. (2006). Black Americans reduce the racial IQ gap: Evidence from standardization samples. Psychological Science, 16(10), 913–920.CrossRefGoogle Scholar
  12. Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method (3rd ed.). Hoboken: Wiley.Google Scholar
  13. Duneier, M. (2012). Qualitative methods. In G. Ritzer (Ed.), The Wiley-Blackwell companion to sociology (1st ed., pp. 73–81). West Sussex: Blackwell.CrossRefGoogle Scholar
  14. Durkheim, E. (1997). The division of labor in society (L. A. Coser, Trans.). New York: Free Press.Google Scholar
  15. Firebaugh, G. (2008). Seven rules for social research. Princeton: Princeton University Press.Google Scholar
  16. Fox, J. (2008). Applied regression analysis and generlized linear models (2nd ed.). Thousand Oaks: Sage.Google Scholar
  17. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). New York: McGraw-Hill.Google Scholar
  18. Hawking, S. (1988). A brief history of time. New York: Bantam.Google Scholar
  19. Hooke, R. (1983). How to tell the liars from the statisticians. New York: Marcel Dekker.Google Scholar
  20. Huff, D. (1993). How to lie with statistics. New York: W.W. Norton.Google Scholar
  21. Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: A review of twenty-seven community studies. Journal of Health and Social Behavior, 38(1), 21–37.CrossRefGoogle Scholar
  22. Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.Google Scholar
  23. Lieberson, S. (1987). Making it count: The improvement of social research and theory. Berkeley: University of California Press.Google Scholar
  24. Lohr, S. L. (1999). Sampling: Design and analysis. Pacific Grove: Duxbury Press.MATHGoogle Scholar
  25. Lynch, S. M. (2007). Introduction to applied Bayesian statistics and estimation for social scientists. New York: Springer.CrossRefMATHGoogle Scholar
  26. Marx, K. (1988). Economic and philosophic manuscripts of 1844 (M. Milligan, Trans.). Amherst: Prometheus Books.Google Scholar
  27. Merton, R. K. (1968). Social theory and social structure. New York: Free Press.Google Scholar
  28. Mlodinow, L. (2008). The Drunkard’s walk: How randomness rules our lives. New York: Pantheon Books.Google Scholar
  29. Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. New York: Cambridge University Press.CrossRefGoogle Scholar
  30. Paulos, J. A. (2001). Innumeracy: Mathematical illiteracy and its consequences. New York: Hill & Wang.Google Scholar
  31. Pigliucci, M. (2010). Nonsense on stilts: How to tell science from bunk. Chicago: The University of Chicago Press.CrossRefGoogle Scholar
  32. Popper, K. S. (1992). The logic of scientific discovery. New York: Routledge.Google Scholar
  33. Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Meauring and modeling population processes. Oxford: Blackwell.Google Scholar
  34. Scheaffer, R. L., Mendenhall, W., III, Ott, R. L., & Gerow, K. (2012). Elementary survey sampling (7th ed.). Boston: Brooks/Cole.Google Scholar
  35. Smith, T. W., Marsden, P., Hout, M., & Kim, J. (2011). General social surveys 1972–2010 [machine-readable data file]/Principal Investigator, T.W. Smith; Co-Principal Investigator, P.V. Marsden; Co-Principal Investigator, M. Hout; Sponsored by National Science Foundation.–NORC ed.–Chicago: National Opinion Research Center [producer]; Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut [distributer].Google Scholar
  36. von Hippel, P. T. (2005). Mean, median, and skew: Correcting a textbook rule. Journal of Statistics Education, 13(2).Google Scholar
  37. Western, B. (2009). Punishment and inequality in America. New York: Russell Sage Foundation.Google Scholar
  38. Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: The University of Chicago Press.Google Scholar
  39. Wimmer, A. (2013). Waves of war: Nationalism, state formation, and ethnic exclusion in the modern world. New York: Cambridge University Press.Google Scholar
  40. Ziliak, S. T., & McCloskey, D. N. (2008). The cult of statistical significance: How the standard error costs us jobs, justice, and lives. Ann Arbor: University of Michigan Press.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Scott M. Lynch
    • 1
  1. 1.Department of SociologyPrinceton UniversityPrincetonUSA

Personalised recommendations