Effects of ignoring clustered data structures in factor analysis with applications to psychiatry

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11510%2F13%3A10194857" target="_blank" >RIV/00216208:11510/13:10194857 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.lap-publishing.com/catalog/details//store/gb/book/978-3-659-41191-5/effects-of-ignoring-clustered-data-structures-in-factor-analysis" target="_blank" >https://www.lap-publishing.com/catalog/details//store/gb/book/978-3-659-41191-5/effects-of-ignoring-clustered-data-structures-in-factor-analysis</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Effects of ignoring clustered data structures in factor analysis with applications to psychiatry

Popis výsledku v původním jazyce

In psychiatric research, data for analysis originate principally from two sources: directly from the patients themselves and from interviews conducted by health care professionals. In the latter case, statistical theory indicates that clustering by interviewers or raters needs to be considered when performing any analyses including regression, factor analysis (FA) or item response theory (IRT) modelling of binary or ordinal data. We use simulated data to study the bias of factor analytic estimates and model fit indices when data clustering is fully or partly ignored. Robustness of different estimators, such as maximum likelihood, weighted least squares and Markov chain Monte Carlo is also presented. In the second part, we analyse two real datasets containing responses to the Positive and Negative Syndrome Scale (PANSS) to show the differences when the data are analysed using the correct multilevel approach rather than a traditional aggregated analysis.

Název v anglickém jazyce

Effects of ignoring clustered data structures in factor analysis with applications to psychiatry

Popis výsledku anglicky

In psychiatric research, data for analysis originate principally from two sources: directly from the patients themselves and from interviews conducted by health care professionals. In the latter case, statistical theory indicates that clustering by interviewers or raters needs to be considered when performing any analyses including regression, factor analysis (FA) or item response theory (IRT) modelling of binary or ordinal data. We use simulated data to study the bias of factor analytic estimates and model fit indices when data clustering is fully or partly ignored. Robustness of different estimators, such as maximum likelihood, weighted least squares and Markov chain Monte Carlo is also presented. In the second part, we analyse two real datasets containing responses to the Positive and Negative Syndrome Scale (PANSS) to show the differences when the data are analysed using the correct multilevel approach rather than a traditional aggregated analysis.

Druh

B - Odborná kniha

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

ISBN

978-3-659-41191-5

Počet stran knihy

100

Název nakladatele

Lambert Academic Publishing

Místo vydání

Saarbrücken

Kód UT WoS knihy

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