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Item response theory and Structural Equation Modelling for ordinal data: describing the relationship between KIDSCREEN and Life-H

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Item response theory and Structural Equation Modelling for ordinal data : describing the relationship between KIDSCREEN and Life-H. / Titman, Andrew; Lancaster, Gillian; Colver, Allan.

In: Statistical Methods in Medical Research, Vol. 25, No. 5, 10.2016, p. 1892-1924.

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@article{2d28520efcca4ff9809fbfba3568e1a2,
title = "Item response theory and Structural Equation Modelling for ordinal data: describing the relationship between KIDSCREEN and Life-H",
abstract = "Both item response theory (IRT) and structural equation modelling (SEM) are useful in the analysis of ordered categorical responses from health assessment questionnaires. We highlight the advantages and disadvantages of the IRT and SEM approaches to modelling ordinal data, from within a community health setting. Using data from the SPARCLE project focussing on children with cerebal palsy, this paper investigates the relationship between two ordinal rating scales, the KIDSCREEN, which measures quality-of-life, and Life-H, which measures participation. Practical issues relating to fitting models, such as non-positive definite observed or fitted correlation matrices, and approaches to assessing model fit are discussed. IRT models allow properties such as the conditional independence of particular domains of a measurement instrument to be assessed. When, as with the SPARCLE data, the latent traits are multidimensional, SEMs generally provide a much more convenient modelling framework.",
keywords = "item response theory, structural equation modelling, ordinal data, health assessment, cerebral palsy",
author = "Andrew Titman and Gillian Lancaster and Allan Colver",
year = "2016",
month = oct
doi = "10.1177/0962280213504177",
language = "English",
volume = "25",
pages = "1892--1924",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",
number = "5",

}

RIS

TY - JOUR

T1 - Item response theory and Structural Equation Modelling for ordinal data

T2 - describing the relationship between KIDSCREEN and Life-H

AU - Titman, Andrew

AU - Lancaster, Gillian

AU - Colver, Allan

PY - 2016/10

Y1 - 2016/10

N2 - Both item response theory (IRT) and structural equation modelling (SEM) are useful in the analysis of ordered categorical responses from health assessment questionnaires. We highlight the advantages and disadvantages of the IRT and SEM approaches to modelling ordinal data, from within a community health setting. Using data from the SPARCLE project focussing on children with cerebal palsy, this paper investigates the relationship between two ordinal rating scales, the KIDSCREEN, which measures quality-of-life, and Life-H, which measures participation. Practical issues relating to fitting models, such as non-positive definite observed or fitted correlation matrices, and approaches to assessing model fit are discussed. IRT models allow properties such as the conditional independence of particular domains of a measurement instrument to be assessed. When, as with the SPARCLE data, the latent traits are multidimensional, SEMs generally provide a much more convenient modelling framework.

AB - Both item response theory (IRT) and structural equation modelling (SEM) are useful in the analysis of ordered categorical responses from health assessment questionnaires. We highlight the advantages and disadvantages of the IRT and SEM approaches to modelling ordinal data, from within a community health setting. Using data from the SPARCLE project focussing on children with cerebal palsy, this paper investigates the relationship between two ordinal rating scales, the KIDSCREEN, which measures quality-of-life, and Life-H, which measures participation. Practical issues relating to fitting models, such as non-positive definite observed or fitted correlation matrices, and approaches to assessing model fit are discussed. IRT models allow properties such as the conditional independence of particular domains of a measurement instrument to be assessed. When, as with the SPARCLE data, the latent traits are multidimensional, SEMs generally provide a much more convenient modelling framework.

KW - item response theory

KW - structural equation modelling

KW - ordinal data

KW - health assessment

KW - cerebral palsy

U2 - 10.1177/0962280213504177

DO - 10.1177/0962280213504177

M3 - Journal article

VL - 25

SP - 1892

EP - 1924

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 5

ER -