An overview of Markov chain methods for the study of stage-sequential developmental processes.
This article presents an overview of quantitative methodologies for the study of stage-sequential development based on extensions of Markov chain modeling. Four methods are presented that exemplify the flexibility of this approach: the manifest Markov model, the latent Markov model, latent transition analysis, and the mixture latent Markov model. A special case of the mixture latent Markov model, the so-called mover-stayer model, is used in this study. Unconditional and conditional models are estimated for the manifest Markov model and the latent Markov model, where the conditional models include a measure of poverty status. Issues of model specification, estimation, and testing using the Mplus software environment are briefly discussed, and the Mplus input syntax is provided. The author applies these 4 methods to a single example of stage-sequential development in reading competency in the early school years, using data from the Early Childhood Longitudinal Study--Kindergarten Cohort.
Kapland
David
D
Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI 53706, USA. dkaplan@education.wisc.edu
D002648:Child; D002657:Child Development; D002675:Child, Preschool; D032882:Comprehension; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D008432:Mathematical Computing; D011203:Poverty / Q000523:psychology / Q000706:statistics & numerical data; D011594:Psychometrics / Q000706:statistics & numerical data*; D011932:Reading; D012984:Software