Macready & Dayton (1980) showed that state mastery
models are handled optimally within the general
latent class framework for data from a single time
point. An extension of this idea is presented here for
longitudinal data obtained from repeated measurements
across time. The static approach is extended
using multiple-indicator Markov chain models. The
approach presented here emphasizes the dynamic aspects
of the process of change, such as growth, decay,
and stability. The general approach is presented, and
models with purely categorical and ordered categorical
states and several extensions of these models are discussed.
Problems of estimation, identification, assessment
of model fit, and hypothesis testing associated
with these models also are discussed. The applicability
of these models is demonstrated using data from a longitudinal
study on solving arithmetic word problems.
The advantages and disadvantages of using the approach
presented here are discussed. Index terms:
arithmetic word problems, dynamic latent class models,
latent class models, longitudinal categorical data,
Markov models, state mastery models.
Langeheine, Rolf, Stern, Elsbeth & van de Pol, Frank. (1994). State mastery learning: Dynamic models for longitudinal data. Applied Psychological Measurement, 18, 277-291. doi:10.1177/014662169401800308
Langeheine, Rolf; Stern, Elsbeth; Van de Pol, Frank.
State mastery learning: Dynamic models for longitudinal data.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.