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A Multilevel Higher Order Item Response Theory Model for Measuring Latent Growth in Longitudinal Data.

ABSTRACT: In educational and psychological testing, individuals are often repeatedly measured to assess the changes in their abilities over time or their latent trait growth. If a test consists of several subtests, the latent traits may have a higher order structure, and traditional item response theory (IRT) models for longitudinal data are no longer applicable. In this study, various multilevel higher order item response theory (ML-HIRT) models for simultaneously measuring growth in the second- and first-order latent traits of dichotomous and polytomous items are proposed. A series of simulations conducted using the WinBUGS software with Markov chain Monte Carlo (MCMC) methods reveal that the parameters could be recovered satisfactorily and that latent trait estimation was reliable across measurement times. The application of the ML-HIRT model to longitudinal data sets is illustrated with two empirical examples.

SUBMITTER: Huang HY

PROVIDER: S-EPMC5978597 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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A Multilevel Higher Order Item Response Theory Model for Measuring Latent Growth in Longitudinal Data.

Huang Hung-Yu HY

Applied psychological measurement 20150115 5

In educational and psychological testing, individuals are often repeatedly measured to assess the changes in their abilities over time or their latent trait growth. If a test consists of several subtests, the latent traits may have a higher order structure, and traditional item response theory (IRT) models for longitudinal data are no longer applicable. In this study, various multilevel higher order item response theory (ML-HIRT) models for simultaneously measuring growth in the second- and firs ...[more]

PMID: 29881013

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Project description:BackgroundThe International Classification of Functioning, Disability and Health (ICF) proposes three main health outcomes, Impairment (I), Activity Limitation (A) and Participation Restriction (P), but good measures of these constructs are needed The aim of this study was to use both Classical Test Theory (CTT) and Item Response Theory (IRT) methods to carry out an item analysis to improve measurement of these three components in patients having joint replacement surgery mainly for osteoarthritis (OA).MethodsA geographical cohort of patients about to undergo lower limb joint replacement was invited to participate. Five hundred and twenty four patients completed ICF items that had been previously identified as measuring only a single ICF construct in patients with osteoarthritis. There were 13 I, 26 A and 20 P items. The SF-36 was used to explore the construct validity of the resultant I, A and P measures. The CTT and IRT analyses were run separately to identify items for inclusion or exclusion in the measurement of each construct. The results from both analyses were compared and contrasted.ResultsOverall, the item analysis resulted in the removal of 4 I items, 9 A items and 11 P items. CTT and IRT identified the same 14 items for removal, with CTT additionally excluding 3 items, and IRT a further 7 items. In a preliminary exploration of reliability and validity, the new measures appeared acceptable.ConclusionNew measures were developed that reflect the ICF components of Impairment, Activity Limitation and Participation Restriction for patients with advanced arthritis. The resulting Aberdeen IAP measures (Ab-IAP) comprising I (Ab-I, 9 items), A (Ab-A, 17 items), and P (Ab-P, 9 items) met the criteria of conventional psychometric (CTT) analyses and the additional criteria (information and discrimination) of IRT. The use of both methods was more informative than the use of only one of these methods. Thus combining CTT and IRT appears to be a valuable tool in the development of measures.

Dataset Information

A Multilevel Higher Order Item Response Theory Model for Measuring Latent Growth in Longitudinal Data.

Publications

A Multilevel Higher Order Item Response Theory Model for Measuring Latent Growth in Longitudinal Data.

Similar Datasets

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