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  Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias: A simulation study

Barendse, M. T., Oort, F. J., & Garst, G. J. A. (2010). Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias: A simulation study. AStA Advances in Statistical Analysis, 94, 117-127. doi:10.1007/s10182-010-0126-1.

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Barendse_etal_2010_Using restricted factor analysis with....pdf (Publisher version), 283KB
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2010
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This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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 Creators:
Barendse, M. T.1, Author              
Oort, F. J., Author
Garst, G. J. A., Author
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1University of Amsterdam, Amsterdam, The Netherlands, ou_persistent22              

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 Abstract: Factor analysis is an established technique for the detection of measurement bias. Multigroup factor analysis (MGFA) can detect both uniform and nonuniform bias. Restricted factor analysis (RFA) can also be used to detect measurement bias, albeit only uniform measurement bias. Latent moderated structural equations (LMS) enable the estimation of nonlinear interaction effects in structural equation modelling. By extending the RFA method with LMS, the RFA method should be suited to detect nonuniform bias as well as uniform bias. In a simulation study, the RFA/LMS method and the MGFA method are compared in detecting uniform and nonuniform measurement bias under various conditions, varying the size of uniform bias, the size of nonuniform bias, the sample size, and the ability distribution. For each condition, 100 sets of data were generated and analysed through both detection methods. The RFA/LMS and MGFA methods turned out to perform equally well. Percentages of correctly identified items as biased (true positives) generally varied between 92% and 100%, except in small sample size conditions in which the bias was nonuniform and small. For both methods, the percentages of false positives were generally higher than the nominal levels of significance.

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Language(s): eng - English
 Dates: 2010-05-262010
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s10182-010-0126-1
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Title: AStA Advances in Statistical Analysis
Source Genre: Journal
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Pages: - Volume / Issue: 94 Sequence Number: - Start / End Page: 117 - 127 Identifier: ISSN: 1863-8171
ISSN: 1863-818X