Because there is little guidance for substantive researchers, the paper reviews the techniques for detecting latent variable interactions and quadratics. After examining plausible research situations where including these nonlinear variables might be appropriate, the paper describes the available detection techniques. Recent advances in nonlinear structural equation analysis are given particular attention, and suggestions for substantive researchers are made.
Get full access to this article
View all access options for this article.
References
1.
Aiken, L.S. & West, S.G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.
2.
Anderson, J.C. & Gerbing, D.W. (1982). Some methods for respecifying measurement models to obtain unidimensional construct measurement. Journal of Marketing Research, 19(November): 453-460.
3.
Anderson, T.W. & Amemiya, Y. (1985). The asymptotic normal distribution of estimators in factor analysis under general conditions. Technical Report 12. Econometric Workshop, Stanford University.
4.
Bentler, P.M. (1983). Some contributions to efficient statistics for structural models: Specification and estimation of moment structures. Psychomerrika, 48: 493-517.
5.
Bentler, P.M. (1989). EQS structural equations program manual. Los Angeles: BMDP Statistical Software.
Blalock, H.M., Jr. (1965). Theory building and the concept of interaction. American Sociological Review, 30: 374-381.
8.
Bohrnstedt, G.W. & Carter, T.M. (1971). Robustness in regression analysis. Pp. 118-146 in H.L. Costner (Ed.), Sociological methodology. San Francisco: Jossey-Bass.
9.
Bollen, K.A. (1989). Structural equations with latent variables. New York: Wiley.
10.
Boomsma, A. (1983). On the robustness of LISREL (maximum likelihood estimation) against small sample size and nonnormality. Unpublished dissertation, University of Groningen.
11.
Browne, M.W. (1982). Covariance structures. Pp. 72-141 in D.M. Hawkins (Ed.), Applied multivariare analysis. Cambridge: Cambridge University Press.
12.
Browne, M.W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37: 62-83.
13.
Browne, M.W. (1987). Robustness of statistical inference in factor analysis and related models. Biometrika, 74: 375-384.
14.
Busemeyer, J.R. & Jones, L.E. (1983). Analysis of multiplicative combination rules when the causal variables are measured with error. Psychological Bulletin, 93: 549-562.
15.
Chow, G.C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3): 591-605.
16.
Cohen, J. (1968). Multiple regression as a general data-analytic system. Psychological Bulletin, 70: 426-443.
17.
Cohen, J. (1978). Partialed products are interactions; Partialed powers are curve components. Psychological aBulletin, 85: 858-866.
18.
Cohen, J. & Cohen, P. (1975). Applied multiple regression/correlation analyses for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum.
19.
Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analyses for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum.
20.
Cortina, J.M. (1993). Interaction, nonlinearity, and multicollinearity: Implications for multiple regression. Journal of Management, 19(4): 915-922.
21.
Dillon, W.R. & Goldstein, M. (1984). M&variate analysis methods and applications. New York: Wiley.
Feucht, T.E (1989). Estimating multiplicative regression terms in the presence of measurement error. Sociological Methods and Research, 17(3): 257-282.
24.
Gerbing, D.W. & Anderson, J.C. (1985). The effects of sampling error and model characteristics on parameter estimation for maximum likelihood confirmatory factor analysis. Multivariate Behavioral Research, 20: 255-271.
25.
Gerbing, D.W. & Anderson, J.C..(1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(May): 186-192.
26.
Harlow, L.L. (1985). Behavior of some elliptical theory estimators with nonnormal data in a covariance structures framework: A Monte Carlo study. Unpublished dissertation, University of California, Los Angeles.
27.
Hayduk, L.A. (1987). Structural equation modeling with LISREL: essential and advances. Baltimore, MD: Johns Hopkins Press.
28.
Hedges, L.V. & Olkin, I. (1985). Statistical methods for meta analysis, New York: Academic Press.
29.
Heise, D.R. (1986). Estimating nonlinear models correcting for measurement error. Sociological Methods and Research, 14(4): 447-472.
30.
Howard, J.A. (1977). Consumer behavior: Application of theory. New York: McGraw Hill.
31.
Howard, J.A. (1989). Consumer behavior in marketing strategy. Englewood Cliffs, NJ: Prentice Hall.
32.
Howard, J.A. & Sheth, J.N. (1969). The theory of buyer behavior. New York: Wiley.
33.
Hu. L., Bentler, P.M. & Kano, Y. (1992). Can test statistics in covariance structure analysis be trusted?Psychological Bulletin, 112: 351-362.
34.
Humphreys, L.G. & Fleishman, A. (1974). Pseudo-orthogonal and other analysis of variance designs involving individual-differences variables. Journal ofEducafiona1 Psychology, 66: 464-472.
35.
Jaccard, J. & Wan, C.K. (1995). Measurement error in the analysis of interaction effects between continuous predictors using multiple regression: multiple indicator and structural equation approaches. Psychological Bulletin, 117(2): 348-357.
36.
Jaccard, J., Turrisi R. & Wan, C.K. (1990). Interaction effects in multiple regression. Newbury Park, CA: Sage.
37.
Jöreskog, K.G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 57: 409-426.
38.
Jöreskog, K.G. (1993). Testing structural equation models. Pp. 294-316 in Kenneth A. Bollen and J. Scott Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage.
39.
Jöreskog, K.G. & S&born, D. (1989). LZSREL 7 a guide tothe program and applications, 2nd ed.Chicago: SPSS.
40.
Kenny, D.A. (1979). Correlation and causality. New York: Wiley.
41.
Kenny, D.A. (1985). Quantitative methods for social psychology. Pp. 487-508 in G. Lindzey and E. Aronson (Eds.), Handbook of Social Psychology, 3rd ed., Vol. 1. New York: Random House.
42.
Kenny, D.A. & Judd, C.M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96: 201-210.
43.
Lubinski, D. & Humphreys, L.G. (1990). Assessing spurious ‘moderator effects’: Illustrated substantively with the hypothesized (‘synergistic’) relation between spatial and mathematical ability. Psychological Bulletin, 107: 385-393.
44.
Mardia, K.V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57: 519-530.
45.
Maxwell, SE., Delaney, H.D. & Dill, C.A. (1984). Another look at ANCOVA versus blocking. Psychological Bulletin, 95: 136-147.
46.
Maxwell, SE. & Delaney, H.D. (1993). Bivariate median splits and spurious statistical significance. Psychological Bulletin, 113: 181-190.
47.
Neter, J., Wasserman, W. & Kunter, M.H. (1985), Applied linear statistical models. Homewood, IL: Irwin.
48.
Ping, R.A. (1994a). Interactions in unobserved variables: Regression-based detection, the deleterious effects of data characteristics, and suggestions. Rike’s Technical Reports, Wright State University.
49.
Ping, R.A. (1994b). Does satisfaction moderate the association between alternative attractiveness and exit intention in a marketing channel?Journal of The Academy of Marketing Science, 22(Fal1): 364-371.
50.
Ping, R.A. (1995). A parsimonious estimating technique for interaction and quadratic latent variables. Journal of Marketing Research, 32(November): 336-347.
51.
Ping, R.A. (1996). Latent variable interaction and quadratic effect estimation: A two-step technique using structural equation analysis. Psychological Bulletin, 119(1): 166-175.
52.
Ping, R.A. (Forthcoming). Latent variable regression: A technique for estimating interaction and quadratic coefficients. Conditionally accepted for M&variate Behavioral Research.
53.
Podsakoff, P.M., Tudor, W.D., Grover, R.A. & Huber, V.L. (1984). Situational moderators of leader reward and punishment behaviors: Fact or fiction?Organizational Behavior and Human Performance, 34(August): 21-63.
54.
Rusbult, C.E., Zembrodt, I.M., & Gunn, L.K. (1982). Exit, voice, loyalty, and neglect: Responses to dissatisfaction in romantic involvement. Journal of Personal and Social Psychology, 43: 1230-1242.
55.
Rusbult, C.E., Farrell, D., Rogers, G. & Mainous, A.G., III (1988). Impact of exchange variables on exit, voice, loyalty, and neglect: An integrative model of responses to declining job satisfaction. Academy of Management Journal, 31(September): 599-627.
56.
Satorra, A. & Bentler, P.M. (1988). Scaling corrections for chi-squared statistics in covariance structure analysis. Proceedings of the American Statistical Association: 308-313.
57.
Sharma, S., Durand, R.M. & Gur-Arie, O. (1981). Identification and analysis of moderator variables. Journal of Marketing Research, 1 & August): 291-300.
58.
Sharma, S., Durvasula, S. & Dillon, W.R. (1989). Some results on the behavior of alternative covariance structure estimation procedures in the presence of nonnormal data. Journal of Marketing Research, 26(May): 214-221.
59.
Tanaka, J.S. (1984). Some results on the estimation of covariance structure models. Unpublished dissertation, University of California, Los Angeles.
60.
Warren, R.D., White, J.K. & Fuller, W.A. (1974). Errors in variables analysis of managerial role performance. Journal of the American Statistical Association, 69: 886-893.
61.
Wong, S.K. & Long, J.S. (1987). Reparameterizing nonlinear constraints in models with latent variables. Technical Report, Washington State University.