In the past three decades we have seen the emergence of the generalized linear model (GLM) techniques for analyzing discrete multivariate data when the independent and dependent variables are categorical, ordinal, or mixed. The primary statistical techniques are loglinear modeling, probit, and logistic regression. The purpose of this article is to (a) briefly describe the emergence of these discrete multivariate techniques in the medical and social sciences, (b) disclose their relationship to one another, and (c) demonstrate the utility of hierarchical loglinear modeling in managerial research
Get full access to this article
View all access options for this article.
References
1.
Agresti, A.
(1984). Analysis of ordinal categorical data. New York: John Wiley.
2.
Agresti, A.
(1990). Categorical data analysis. New York: John Wiley.
3.
Agresti, A.
(1996). An introduction to categorical data analysis. New York: John Wiley.
4.
Bishop, Y.
, Fienberg, S., & Holland, P. (1975). Discrete multivariate analysis. Boston: MIT Press.
5.
Clogg, C.
(1992). The impact of sociological methodology on statistical methodology. Statistical Science, 7, 183-195.
6.
Cramer, J.
(1991). The logit model for economists. New York: Routledge, Chapman, and Hall.
7.
Demaris, A.
(1992). Logit modeling: Practical applications (Sage University Paper Series on Quantitative Applications in the Social Sciences No. 07-086). Newbury Park, CA: Sage.
8.
DiPrete, T. A.
(1990). Adding covariates to loglinear models for the study of social mobility. American Sociological Review, 55, 757-773.
9.
Dixon, W. J.
(Ed.). (1981). BMDP statistical software 1981. Berkeley: University of California Press.
10.
Drazin, R.
, & Kazanjian, R. K. (1993). Applying the Del Technique to the analysis of cross-classification data: A test of CEO succession and top management team development. Academy of Management Journal, 36(6), 1374-1399.
11.
Fienberg, S. E.
(1980). The analysis of cross-classified categorical data (2nd ed.). Boston: MIT Press.
12.
Goodman, L. A.
(1978). Analyzing qualitative/categorical data: Log-linear models and latent structure analysis. Cambridge, MA: Abt Books.
13.
Hellevik, O.
(1988). Introduction to causal analysis: Exploring survey data by crosstabulations. New York: Norwegian University Press.
14.
Hosmer, D.
, & Lemeshow, S. (1989). Applied logistic regression. New York: John Wiley.
15.
Imrey, P. B.
, Koch, G. G., & Stokes, M. E. (1981). Categorical data analysis: Some reflections on the log linear model and logistic regression. Historical and Methodological Review, 49, 265-283.
16.
Kalbfleisch, J. D.
, & Prentice, R. L. (1980). Statistical analysis of failure time data. New York: John Wiley.
17.
Knoke, D.
, & Burke, P. J. (1980). Loglinear models (Sage University Paper Series on Quantitative Applications in the Social Sciences No. 07-020). Beverly Hills, CA: Sage.
18.
Lee, E. T.
(1992). Statistical methods for survival data analysis. New York: John Wiley.
19.
Maddala, G.
(1983). Limited-dependent and qualitative variables in econometrics. Cambridge, UK: Cambridge University Press.
20.
Reynolds, H. T.
(1977a). Analysis of nominal data (Sage University Paper Series on Quantitative Applications in the Social Sciences No. 07-007). Beverly Hills, CA: Sage.
21.
Reynolds, H. T.
(1977b). Some comments on the causal analysis of surveys with loglinear models. American Journal of Sociology, 83, 127-143.
22.
Rumelt, R.
(1974). Strategy, structure, and economic performance. Boston: Harvard Business School.
23.
Santner, T. J.
, & Duffy, D. E. (1989). The statistical analysis of discrete data. New York: Springer-Verlag.
24.
Sherif, M.
(1934). A study of some social factors in perception. Archives of Psychology, 187, 184-200.
25.
Sherif, M.
, Harvey, O., White, B., Hood, W., & Sherif, C. (1961). Intergroup conflict and cooperation: The robbers cave experiment. Norman, OK: University Book Exchange.
26.
Smith, M.
, & White, M. (1987). Strategy, CEO specialization, and succession. Administrative Science Quarterly, 32, 263-280.
27.
Tabachnick, B.
, & Fidell, L. (1989). Using multivariate statistics. New York: Harper & Row.
28.
Tansey, R.
, White, M., Long, R. G., & Smith, M. (1996). A comparison of loglinear modeling and logistic regression in management research. Journal of Management, 22(2), 339-358.
29.
Upton, G.
(1978). The analysis of cross-tabulated data. New York: John Wiley.
30.
White, M.
, Smith, M., & Barnett, T.(1994a). Strategic inertia: The enduring impact of CEO specialization and strategy on following strategies. Journal of Business Research, 31, 11-22.
31.
White, M.
, Smith, M., & Barnett, T. (1994b). A typology of executive career specialization. Human Relations, 47(5), 473-486.
32.
White, M.
, Smith, M., & Barnett, T. (1997). CEO succession: Overcoming forces of inertia. Human Relations, 50(7), 805-828.
33.
White, M.
, Smith, M., & Tansey, R. (1994). A causal modeling test of the relationship between chief executive officer experience and corporate strategy. Journal of Occupational and Organizational Psychology, 67, 259-278.
34.
White, M.
, Tansey, R., & Smith, M. (1994). A causal modeling test of the relationship between chief executive officer experience and corporate strategy. Journal of Occupational and Organizational Psychology, 67, 259-278.
35.
White, M.
, Tansey, R., Smith, M., & Barnett, T. (1993). Loglinear modeling in personnel research. Personnel Psychology, 46, 667-686.
36.
Wickens, T. D.
(1989). Multiway contingency tables analysis for the social sciences. Hillsdale, NJ: Lawrence Erlbaum.
