Advertisement

Quality & Quantity

, Volume 50, Issue 5, pp 2121–2140 | Cite as

Research involving limited dependent variables: issues in the literature and recommendations for improvement

  • Ross H. Taplin
Article

Abstract

Despite previous recommendations for improvement, a literature review reveals a minority of recent papers in management journals provide correct interpretations of regression coefficients for analyses of limited dependent variables. Furthermore, the use of marginal effects to interpret relationships has resulted in confusing and inaccurate conclusions. This paper recommends simpler and more informative alternatives to the calculation and reporting of marginal effects. In particular, two key recommendations involve choosing and explicitly stating a suitable measurement scale for dependent variables and explicitly stating whether relationships with independent variables are multiplicative or additive effects. These recommendations for reporting hypotheses, analysis and interpretations will not only improve the precision of future research but also provide superior interpretations of past literature. Significantly, this paper shows how standard regression coefficients can be used to interpret relationships between variables for any values of all variables. Other approaches such as the recommended inclusion of marginal effects and plots requires fixing other variables to specific values (such as their mean value) and so are of less value to readers.

Keywords

Logistic regression Logarithmic regression Multiplicative effects Marginal effects Interaction effects 

References

  1. Ai, C., Norton, E.C.: Interaction terms in logit and probit models. Econ. Lett. 80, 123–129 (2003)CrossRefGoogle Scholar
  2. Bodolica, V., Spraggon, M.: The implementation of special attributes of CEO compensation contracts around M&A transactions. Strateg. Manag. J. 30, 985–1011 (2009)CrossRefGoogle Scholar
  3. Bowen, H.P., Wiersema, M.F.: Modelling limited dependent variables: methods and guidelines for researchers in strategic management. In: Ketchen, D.J., Bowen, H.P. (eds.) Research Methodology in Strategy and Management, pp. 87–134. Elsevier, Oxford (2004)Google Scholar
  4. Brambor, T., Clark, W.R., Golder, M.: Understanding interaction models: improving empirical analysis. Polit. Anal. 14, 63–82 (2006)CrossRefGoogle Scholar
  5. Caudill, S.B., Jackson, J.D.: Measuring marginal effects in limited dependent variable models. Statistician 38, 203–206 (1989)CrossRefGoogle Scholar
  6. Chatterji, A.K., Toffel, M.W.: How firms respond to being rated. Strateg. Manag. J. 31, 917–945 (2010)Google Scholar
  7. Diestre, L., Rajagopalan, N.: An environmental perspective on diversification: the effects of chemical relatedness and regulatory sanctions. Acad. Manag. J. 54, 97–115 (2011)CrossRefGoogle Scholar
  8. Edwards, J.R., Berry, J.W.: The presence of something or the absence of nothing: increasing theoretical precision in management research. Org. Res. Methods 13, 668–689 (2010)CrossRefGoogle Scholar
  9. Fernandez-Mateo, I., King, Z.: Anticipatory sorting and gender segregation in temporary employment. Manag. Sci. 57, 989–1008 (2011)CrossRefGoogle Scholar
  10. Folta, T.B., O’Brien, J.P.: Entry in the presence of duelling options. Strateg. Manag. J. 25, 121–138 (2004)CrossRefGoogle Scholar
  11. Greene, W.: Testing hypotheses about interaction terms in non-linear models. Econ. Lett. 107, 291–296 (2010)CrossRefGoogle Scholar
  12. Hass, M.R., Hansen, M.T.: When using knowledge can hurt performance: the value of organizational capabilities in a management consulting company. Strateg. Manag. J. 26, 1–24 (2005)CrossRefGoogle Scholar
  13. Hoetker, G.: The use of logit and probit models in strategic management research: critical issues. Strateg. Manag. J. 28(4), 331–343 (2007)CrossRefGoogle Scholar
  14. Marx, M., Strumsky, D., Fleming, L.: Mobility, skills, and the Michigan non-complete experiment. Manag. Sci. 55, 875–889 (2009)CrossRefGoogle Scholar
  15. McCullagh, P., Nelder, J.: Generalized Linear Models, 2nd edn. Chapman and Hall, London (1989)CrossRefGoogle Scholar
  16. Nanda, R., Sorensen, J.B.: Workplace peers and entrepreneurship. Manag. Sci. 56, 1116–1126 (2010)CrossRefGoogle Scholar
  17. Nelder, J., Wedderburn, R.: Generalized linear models. J. R. Stat. Soc. Ser. A 135(3), 370–384 (1972)CrossRefGoogle Scholar
  18. Nerkar, A., Roberts, P.W.: Technological and product-market experience and the success of new product introductions in the pharmaceutical industry. Strateg. Manag. J. 25, 779–799 (2004)CrossRefGoogle Scholar
  19. Petersen, T.: A comment on presenting results from logit and probit models. Am. Soc. Rev. 50(1), 130–131 (1985)CrossRefGoogle Scholar
  20. Reid, E.M., Toffel, M.W.: Responding to public and private politics: corporate disclosure of climate change strategies. Strateg. Manag. J. 30, 1157–1178 (2009)CrossRefGoogle Scholar
  21. Reitzig, M., Wagner, S.: The hidden costs of outsourcing: evidence from patent data. Strateg. Manag. J. 31, 1183–1201 (2010)CrossRefGoogle Scholar
  22. Weirsema, M.F., Bowen, H.P.: The use of limited dependent variable techniques in strategy research: issues and methods. Strateg. Manag. J. 30, 679–692 (2009)CrossRefGoogle Scholar
  23. Wiersema, M.F., Zhang, Y.: CEO dismissal: the role of investment analysts. Strateg. Manag. J. 32, 1161–1182 (2011)CrossRefGoogle Scholar
  24. Yu, J., Gilbert, B.A., Oviatt, B.M.: Effects of alliances, time, and network cohesion on the initiation of foreign sales by new ventures. Strateg. Manag. J. 32, 424–446 (2011)CrossRefGoogle Scholar
  25. Zellner, B.A.: Using simulation to interpret results from logit, probit, and other non-linear models. Strateg. Manag. J. 30, 1335–1348 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.School of Accounting, Curtin Business SchoolCurtin UniversityPerthAustralia

Personalised recommendations