Explaining General and Specific Factors in Longitudinal, Multimethod, and Bifactor Models: Some Caveats and Recommendations
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Transfer › begutachtet
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Explaining General and Specific Factors in Longitudinal, Multimethod, and Bifactor Models : Some Caveats and Recommendations. / Koch, Tobias; Holtmann, Jana; Bohn, Johannes et al.
in: Psychological Methods, Jahrgang 23, Nr. 3, 09.2018, S. 505-523.Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Transfer › begutachtet
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TY - JOUR
T1 - Explaining General and Specific Factors in Longitudinal, Multimethod, and Bifactor Models
T2 - Some Caveats and Recommendations
AU - Koch, Tobias
AU - Holtmann, Jana
AU - Bohn, Johannes
AU - Eid, Michael
PY - 2018/9
Y1 - 2018/9
N2 - An increasing number of psychological studies are devoted to the analysis of g-factor structures. One key purpose of applying g-factor models is to identify predictors or potential causes of the general and specific effects. Typically, researchers relate predictor variables directly to the general and specific factors using a classical mimic approach. However, this procedure bears some methodological challenges, which often lead to model misspecification and biased parameter estimates. We propose 2 possible modeling strategies to circumvent these problems: the multiconstruct bifactor and the residual approach. We illustrate both modeling approaches for the application of g-factor models to longitudinal and multitrait-multimethod data. Practical guidelines are provided for choosing an appropriate method in empirical applications, and the implications of this investigation for multimethod and longitudinal research are discussed.
AB - An increasing number of psychological studies are devoted to the analysis of g-factor structures. One key purpose of applying g-factor models is to identify predictors or potential causes of the general and specific effects. Typically, researchers relate predictor variables directly to the general and specific factors using a classical mimic approach. However, this procedure bears some methodological challenges, which often lead to model misspecification and biased parameter estimates. We propose 2 possible modeling strategies to circumvent these problems: the multiconstruct bifactor and the residual approach. We illustrate both modeling approaches for the application of g-factor models to longitudinal and multitrait-multimethod data. Practical guidelines are provided for choosing an appropriate method in empirical applications, and the implications of this investigation for multimethod and longitudinal research are discussed.
KW - Psychology
KW - Bifactor models
KW - CTC(M-1) model
KW - G-factor models
KW - Latent state-trait models
KW - MIMIC models
UR - http://www.scopus.com/inward/record.url?scp=85025116674&partnerID=8YFLogxK
U2 - 10.1037/met0000146
DO - 10.1037/met0000146
M3 - Journal articles
C2 - 28737413
VL - 23
SP - 505
EP - 523
JO - Psychological Methods
JF - Psychological Methods
SN - 1082-989X
IS - 3
ER -