Meta-Analytic Methods of Pooling Correlation Matrices for Structural Equation Modeling under Different Patterns of Missing Data

Autor/inn/en	Furlow, Carolyn F.; Beretvas, S. Natasha
Titel	Meta-Analytic Methods of Pooling Correlation Matrices for Structural Equation Modeling under Different Patterns of Missing Data
Quelle	In: Psychological Methods, 10 (2005) 2, S.227-254 (28 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1082-989X
Schlagwörter	Rejection (Psychology); Monte Carlo Methods; Least Squares Statistics; Correlation; Structural Equation Models; Meta Analysis; Matrices; Indexes; Computation; Synthesis; Data Analysis; Multitrait Multimethod Techniques + Suchen Sie Ihr Suchwort? Ablehnung; Monte-Carlo-Methode; Korrelation; Meta-analysis; Metaanalyse; Matrizenrechnung; Indexdatei; Auswertung
Abstract	Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-COV GLS), was compared with univariate weighting with untransformed correlations (univariate r) and univariate weighting with Fisher's z-transformed correlations (univariate z). These 3 methods were crossed with listwise and pairwise deletion. Univariate z and W-COV GLS performed similarly, with W-COV GLS providing slightly better estimation of parameters and more correct model rejection rates. Missing not at random data produced high levels of relative bias in correlation and model parameter estimates and higher incorrect SEM model rejection rates. Pairwise deletion resulted in inflated standard errors for all synthesis methods and higher incorrect rejection rates for the SEM model with univariate weighting procedures. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10