Residual Structures in Growth Models With Ordinal Outcomes

Kevin Grimm, Y. Liu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Growth models allow for the study of within-person change and between-person differences in within-person change. Typically, these models are applied to continuous variables where the residuals are assumed to be normally distributed. With normally distributed residuals there are a variety of residual structures that can be imposed and tested, which have been shown to affect model fit and parameter estimation. This article concerns residual structures in growth models with binary and ordered categorical outcomes using the probit link function. Different residual structures and their appropriateness for growth data are discussed and their use is illustrated with longitudinal data collected as part of Head Start’s Family and Child Experiences Survey 1997 Cohort. We close with recommendations for the specification and parameterization of growth models that use the probit link.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalStructural Equation Modeling
DOIs
StateAccepted/In press - Jan 10 2016

Fingerprint

Growth Model
Probit
Person
human being
Link Function
Continuous Variables
Longitudinal Data
Parameterization
Categorical
Parameter estimation
Parameter Estimation
Recommendations
Growth model
Binary
Specification
Specifications
Model
experience

Keywords

  • change
  • growth
  • ordinal

ASJC Scopus subject areas

  • Modeling and Simulation
  • Decision Sciences(all)
  • Economics, Econometrics and Finance(all)
  • Sociology and Political Science

Cite this

Residual Structures in Growth Models With Ordinal Outcomes. / Grimm, Kevin; Liu, Y.

In: Structural Equation Modeling, 10.01.2016, p. 1-10.

Research output: Contribution to journalArticle

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