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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Published on: September 17, 2019

A model for incomplete longitudinal multivariate ordinal data.

Li C Liu1

  • 1Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 W. Taylor Street, Room 979, Chicago, IL 60612, USA. liliu@uic.edu

Statistics in Medicine
|September 4, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new longitudinal item response theory model to analyze repeated ordinal outcomes with missing data. The model effectively handles missing data and estimates item parameters for longitudinal studies.

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Area of Science:

  • Statistics
  • Psychometrics
  • Longitudinal Data Analysis

Background:

  • Missing data is a common challenge in longitudinal studies with repeated outcome measures.
  • Traditional methods may not adequately address complex missing data patterns in multivariate ordinal outcomes.
  • Longitudinal item response theory (IRT) offers a framework for analyzing such data.

Purpose of the Study:

  • To propose a novel longitudinal item response theory model for multivariate ordinal outcomes with missing data.
  • To accommodate missing data at any level (item or time point) under the MAR assumption.
  • To allow for multiple random subject effects and estimate item discrimination parameters.

Main Methods:

  • A longitudinal IRT model for multivariate ordinal outcomes.
  • Assumes data are Missing At Random (MAR).
  • Employs maximum marginal likelihood estimation with multidimensional Gauss-Hermite quadrature and an iterative Fisher-scoring solution.

Main Results:

  • The proposed model effectively handles missing data in longitudinal studies.
  • It allows for the estimation of item discrimination parameters for multiple outcome items.
  • Covariates can be incorporated at any level within the model.

Conclusions:

  • The developed longitudinal IRT model provides a robust method for analyzing repeated multivariate ordinal outcomes with missing data.
  • This approach is applicable to various fields, including health behavior research.
  • The model's flexibility in handling missing data and incorporating random effects enhances its utility in complex longitudinal studies.