Latent Class Models for Stage-Sequential Dynamic Latent Variables

Linda M. Collins, Stuart E. Wugalter

Research output: Contribution to journalArticle

217 Citations (Scopus)

Abstract

Stage-sequential dynamic latent variables are of interest in many longitudinal studies. Measurement theory for these latent variables, called Latent Transition Analysis (LTA), can be found in recent generalizations of latent class theory. LTA expands the latent Markov model to allow applications to more complex latent variables and the use of multiple indicators. Because complex latent class models result in sparse contingency tables, that may lead to poor parameter estimation, a simulation study was conducted in order to determine whether model parameters are recovered adequately by LTA, and whether additional indicators result in better measurement or in impossibly sparse tables. The results indicated that parameter recovery was satisfactory overall, although as expected the standard errors were large in some conditions with few subjects. The simulation also indicated that at least within the conditions examined here, the benefits of adding indicators outweigh the costs. Additional indicators improved standard errors, even in conditions producing extremely sparse tables. An example of LTA analysis of empirical data on math skill development is presented.

Original languageEnglish (US)
Pages (from-to)131-157
Number of pages27
JournalMultivariate Behavioral Research
Volume27
Issue number1
DOIs
StatePublished - Jan 1 1992

Fingerprint

Latent Class Model
Latent Variables
Longitudinal Studies
Costs and Cost Analysis
Standard error
Tables
Measurement Theory
Latent Class
Longitudinal Study
Contingency Table
Complex Variables
Markov Model
Expand
Parameter Estimation
Recovery
Simulation Study
Costs
Simulation

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

Cite this

@article{330fe70917f3416987d91c68e98b6f11,
title = "Latent Class Models for Stage-Sequential Dynamic Latent Variables",
abstract = "Stage-sequential dynamic latent variables are of interest in many longitudinal studies. Measurement theory for these latent variables, called Latent Transition Analysis (LTA), can be found in recent generalizations of latent class theory. LTA expands the latent Markov model to allow applications to more complex latent variables and the use of multiple indicators. Because complex latent class models result in sparse contingency tables, that may lead to poor parameter estimation, a simulation study was conducted in order to determine whether model parameters are recovered adequately by LTA, and whether additional indicators result in better measurement or in impossibly sparse tables. The results indicated that parameter recovery was satisfactory overall, although as expected the standard errors were large in some conditions with few subjects. The simulation also indicated that at least within the conditions examined here, the benefits of adding indicators outweigh the costs. Additional indicators improved standard errors, even in conditions producing extremely sparse tables. An example of LTA analysis of empirical data on math skill development is presented.",
author = "Collins, {Linda M.} and Wugalter, {Stuart E.}",
year = "1992",
month = "1",
day = "1",
doi = "10.1207/s15327906mbr2701_8",
language = "English (US)",
volume = "27",
pages = "131--157",
journal = "Multivariate Behavioral Research",
issn = "0027-3171",
publisher = "Psychology Press Ltd",
number = "1",

}

Latent Class Models for Stage-Sequential Dynamic Latent Variables. / Collins, Linda M.; Wugalter, Stuart E.

In: Multivariate Behavioral Research, Vol. 27, No. 1, 01.01.1992, p. 131-157.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Latent Class Models for Stage-Sequential Dynamic Latent Variables

AU - Collins, Linda M.

AU - Wugalter, Stuart E.

PY - 1992/1/1

Y1 - 1992/1/1

N2 - Stage-sequential dynamic latent variables are of interest in many longitudinal studies. Measurement theory for these latent variables, called Latent Transition Analysis (LTA), can be found in recent generalizations of latent class theory. LTA expands the latent Markov model to allow applications to more complex latent variables and the use of multiple indicators. Because complex latent class models result in sparse contingency tables, that may lead to poor parameter estimation, a simulation study was conducted in order to determine whether model parameters are recovered adequately by LTA, and whether additional indicators result in better measurement or in impossibly sparse tables. The results indicated that parameter recovery was satisfactory overall, although as expected the standard errors were large in some conditions with few subjects. The simulation also indicated that at least within the conditions examined here, the benefits of adding indicators outweigh the costs. Additional indicators improved standard errors, even in conditions producing extremely sparse tables. An example of LTA analysis of empirical data on math skill development is presented.

AB - Stage-sequential dynamic latent variables are of interest in many longitudinal studies. Measurement theory for these latent variables, called Latent Transition Analysis (LTA), can be found in recent generalizations of latent class theory. LTA expands the latent Markov model to allow applications to more complex latent variables and the use of multiple indicators. Because complex latent class models result in sparse contingency tables, that may lead to poor parameter estimation, a simulation study was conducted in order to determine whether model parameters are recovered adequately by LTA, and whether additional indicators result in better measurement or in impossibly sparse tables. The results indicated that parameter recovery was satisfactory overall, although as expected the standard errors were large in some conditions with few subjects. The simulation also indicated that at least within the conditions examined here, the benefits of adding indicators outweigh the costs. Additional indicators improved standard errors, even in conditions producing extremely sparse tables. An example of LTA analysis of empirical data on math skill development is presented.

UR - http://www.scopus.com/inward/record.url?scp=0001398981&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0001398981&partnerID=8YFLogxK

U2 - 10.1207/s15327906mbr2701_8

DO - 10.1207/s15327906mbr2701_8

M3 - Article

AN - SCOPUS:0001398981

VL - 27

SP - 131

EP - 157

JO - Multivariate Behavioral Research

JF - Multivariate Behavioral Research

SN - 0027-3171

IS - 1

ER -