The insidious effects of failing to include design-driven correlated residuals in latent-variable covariance structure analysis.

Dados Bibliográficos

ID23349877
AutoresDavid A Cole (0000-0002-7535-1230, Vanderbilt University), Jeffrey A Ciesla (0000-0002-2635-3061, Vanderbilt University), James H Steiger (Vanderbilt University)
Ano2007
Volume12
Fascículo4
Páginas381-398
Data de publicação2007-12-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoPsychological Methods (JOURNAL)
Identificadores do periódicoISSN: 1082-989X • E-ISSN: 1939-1463
EditoraAmerican Psychological Association (APA) (PUBLISHER)
DOI10.1037/1082-989x.12.4.381
PMID18179350
OpenAlexW2002538709
IdiomaEN
Citações recebidas105
Referências citadas14

In practice, the inclusion of correlated residuals in latent-variable models is often regarded as a statistical sleight of hand, if not an outright form of cheating. Consequently, researchers have tended to allow only as many correlated residuals in their models as are needed to obtain a good fit to the data. The current article demonstrates that this strategy leads to the underinclusion of residual correlations that are completely justified on the basis of measurement theory and research design. In many designs, the absence of such correlations will not substantially harm the fit of the model; however, failure to include them can change the meaning of the extracted latent variables and generate potentially misleading results. Recommendations include (a) returning to the full multitrait-multimethod design when measurement theory implies the existence of shared method variance and (b) abandoning the evil-but-necessary attitude toward correlated residuals when they reflect intended features of the research design.

Analysis of covariance · Cheating · Covariance · Econometrics · Economics · Harm · Latent variable · Latent variable model · Residual · Statistics · Structural equation modeling · Variable (mathematics) · Variance (accounting) · Advanced Statistical Methods and Models · Advanced Statistical Modeling Techniques · Mathematics · Psychology · Psychometric Methodologies and Testing · Social Psychology