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Behaviormetrika

, Volume 21, Issue 1, pp 1–17 | Cite as

Statistical Choices in Infant Temperament Research

  • Hal Stern
  • Doreen Arcus
  • Jerome Kagan
  • Donald B. Rubin
  • Nancy Snidman
Invited Paper

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.

Key Words and Phrases

finite-mixture model latent class model EM algorithm average information measure 

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Copyright information

© The Behaviormetric Society 1994

Authors and Affiliations

  • Hal Stern
    • 1
  • Doreen Arcus
    • 2
  • Jerome Kagan
    • 2
  • Donald B. Rubin
    • 1
  • Nancy Snidman
    • 2
  1. 1.The Department of StatisticsHarvard UniversityCambridgeUSA
  2. 2.The Department of PsychologyHarvard UniversityCambridgeUSA

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