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The Reliability Paradox in Assessing Structural Relations Within Covariance Structure Models

Bibliographic Data

ID20281161
AuthorsGregory R Hancock (0000-0002-6313-006X, University of Maryland, College Park, MD, USA,, corresponding author), Ralph O Mueller (University of Hartford, West Hartford, CT, USA)
Year2011
Volume71
Issue2
Pages306-324
Publication date2011-04-01
Open AccessYes
TypeARTICLE
VenueEducational and Psychological Measurement (JOURNAL)
Journal identifiersISSN: 0013-1644 • E-ISSN: 1552-3888
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/0013164410384856
OpenAlexW2080116415
LanguageEN
Citations received45
References cited13

A two-step process is commonly used to evaluate data–model fit of latent variable path models, the first step addressing the measurement portion of the model and the second addressing the structural portion of the model. Unfortunately, even if the fit of the measurement portion of the model is perfect, the ability to assess the fit within the structural portion is affected by the quality of the factor–variable relations within the measurement model. The result is that models with poorer quality measurement appear to have better data–model fit, whereas models with better quality measurement appear to have worse data–model fit. The current article illustrates this phenomenon across different classes of fit indices, discusses related structural assessment problems resulting from issues of measurement quality, and endorses a supplemental modeling step evaluating the structural portion of the model in isolation from the measurement model

Covariance · Econometrics · Goodness of fit · Latent variable · Quality (philosophy) · Reliability (semiconductor) · Statistics · Structural equation modeling · Variable (mathematics) · Advanced Statistical Modeling Techniques · Computer Science · Mathematics · Psychometric Methodologies and Testing · Statistical Methods and Applications

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Citations per year3.21
Citation span2012 - 2026 (15)
Highly citedNo

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