A Second-Order Longitudinal Model for Binary Outcomes: Item Response Theory Versus Structural Equation Modeling

Chun Wang, Nidhi Kohli, Lisa Henn

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Measuring academic growth, or change in aptitude, relies on longitudinal data collected across multiple measurements. The National Educational Longitudinal Study (NELS:88) is among the earliest, large-scale, educational surveys tracking students’ performance on cognitive batteries over 3 years. Notable features of the NELS:88 data set, and of almost all repeated measures educational assessments, are (a) the outcome variables are binary or at least categorical in nature; and (b) a set of different items is given at each measurement occasion with a few anchor items to fix the measurement scale. This study focuses on the challenges related to specifying and fitting a second-order longitudinal model for binary outcomes, within both the item response theory and structural equation modeling frameworks. The distinctions between and commonalities shared between these two frameworks are discussed. A real data analysis using the NELS:88 data set is presented for illustration purposes.

Original languageEnglish (US)
Pages (from-to)455-465
Number of pages11
JournalStructural Equation Modeling
Volume23
Issue number3
DOIs
StatePublished - May 3 2016

Keywords

  • binary outcomes
  • item response theory
  • longitudinal
  • structural equation modeling

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