Abstract
Temperamental characteristics can be conceptualized as either continuous dimensions or qualitative categories. The distinction concerns the underlying temperamental characteristics rather than the measured variables, which can usually be recorded as either continuous or categorical variables. A finite mixture model captures the categorical view, and we apply such a model here to two sets of longitudinal observations of infants and young children. A measure of predictive efficacy is described for comparing the mixture model with competing models, principally a linear regression analysis. The mixture model performs mildly better than the linear regression model with respect to this measure of fit to the data; however, the primary advantage of the mixture model relative to competing approaches, is that, because it matches our a priori theory, it can be easily used to address improvements and corrections to the theory, and to suggest extensions of the research.
Similar content being viewed by others
References
Arcus, D.M. (1991) The experiential modification of temperamental bias in inhibited and uninhibited children. Unpublished doctoral dissertation, Harvard University.
Attneave, F. (1959) Applications of Information Theory to Psychology, New York: Holt.
Clogg, C.C (1981a). Latent class analysis across groups. Proceedings of the Social Statistics Section, 1981 Annual Meeting of the American Statistical Association, 299–304, Alexandria: American Statistical Association.
Clogg, C.C. (1981b). Latent structure model of mobility. American Journal of Sociology, 86, 838–868.
Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977) Maximum likelihood estimation from incomplete data via the EM algorithm, (with discussion) Journal of the Royal Statistical Society B, 39, 1–38.
Dunn, L.T. and Everitt, B.J. (1988) Double dissociations of the effects of amygdala and insular cortex lesions on condition taste aversion, passive avoidance and neophobia in the the rat using the excitotoxin ibotenic acid. Behavioral Neuroscience, 102, 3–9.
Everitt, B.S. and Hand, D.J. (1981) Finite Mixture Distributions. London: Chapman and Hall.
Gelman, A., Meng, X.L., and Stern, H.S. (1993) Bayesian model invalidation using tail area probabilities. Technical Report, Department of Statistics, Harvard University (submitted to Journal of the Royal Statistical Society B).
Goodman, L.A. (1974a). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215–231.
Goodman, L.A. (1974b). The analysis of a system of qualitative variables when some of the variables are unobservable. Part I: A modified latent structure approach. American Journal of Sociology, 79, 1179–1259.
Haberman, S.J. (1979) Analysis of Qualitative Data, Vol. 2, Chapter 10. New York: Academic Press.
Haberman, S J. (1988) A stabilized Newton-Raphson algorithm for log-linear models for frequency tables derived by indirect observation. Sociological Methodology, 18, 193–211.
Hinde, R.A. and Dennis, A. (1986) Categorizing individuals. International Journal of Behavioral Development, 9, 105–119.
Kagan, J. (1989) Temperamental contributions to social behavior. American Psychologist, 44, 668–674.
Kagan, J. and Snidman, N. (1991a). Temperamental factors in human development. American Psychologist, 46, 856–862.
Kagan, J. and Snidman, N. (1991b). Infant predictors of inhibited and uninhibited profiles. Psychological Science, 2, 40–44.
Kelley, A.E., Domesick, V.B. and Nauta, WJ.H. (1982) The amygdalostriatal projection in the rat: an anatomical study by antrograde and retrograde tracing techniques. Neuroscience, 7, 615–630.
Lazarsfeld, P.F. and Henry, N.W. (1968) Latent Structure Analysis. Boston: Houghton Mifflin.
Magnusson, D. and Allen, V J. (1983) Human Developmemnt: An Interactional Perspective. New York: Academic Press.
McCutcheon, A.L. (1987) Latent Class Analysts. Beverly Hills: Sage Publications.
Meethl, P.E. (1989) Schizotaxia revisited. Archives of General Schizophrenia, 46, 935–944.
Meng, X.L. and Rubin, D.B. (1991) Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm. Journal of the American Statistical Association, 86, 899–909.
Mishkin, M. and Aggleton, J. (1981) Multiple functional contributions of the amygdala in the monkey. In Y. Ben-Ari (ed), The Amygdala Complex, 409–420. Amsterdam: North Holland Press.
Rubin, D.B. (1984) Bayesianly justifiable and relevant frequency calculations for the applied statistician. The Annals of Statistics, 12, 1151–1172.
Rubin, D.B. and Stern, H.S. (1992) Testing in latent class models using a posterior predictive check distribution. To appear in C. Clogg and A. von Eye (eds.), Analysis of Latent Variables in Developmental Research.
Stern, H., Arcus, D., Kagan, J., Rubin, D.B. and Snidman, N. (1993) Using mixture models in temperament research. To appear in International Journal of Behavioral Development.
Titterington, D.M., Smith, A.F.M., and Makov, U.E. (1985) Statistical Analysis of Finite Mixture Distributions. New York: John Wiley.
Author information
Authors and Affiliations
About this article
Cite this article
Stern, H., Arcus, D., Kagan, J. et al. Statistical Choices in Infant Temperament Research. Behaviormetrika 21, 1–17 (1994). https://doi.org/10.2333/bhmk.21.1
Received:
Published:
Issue Date:
DOI: https://doi.org/10.2333/bhmk.21.1