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Koch, Tobias; Schultze, Martin; Burrus, Jeremy; Roberts, Richard D.; Eid, Michael – Journal of Educational and Behavioral Statistics, 2015
The numerous advantages of structural equation modeling (SEM) for the analysis of multitrait-multimethod (MTMM) data are well known. MTMM-SEMs allow researchers to explicitly model the measurement error, to examine the true convergent and discriminant validity of the given measures, and to relate external variables to the latent trait as well as…
Descriptors: Structural Equation Models, Hierarchical Linear Modeling, Factor Analysis, Multitrait Multimethod Techniques
Revilla, Melanie; Saris, Willem E. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Saris, Satorra, and Coenders (2004) proposed a new approach to estimate the quality of survey questions, combining the advantages of 2 existing approaches: the multitrait-multimethod (MTMM) and the split-ballot (SB) ones. Implemented in practice, this new approach led to frequent problems of nonconvergence and improper solutions. This article uses…
Descriptors: Multitrait Multimethod Techniques, Surveys, Monte Carlo Methods, Correlation
Castro-Schilo, Laura; Widaman, Keith F.; Grimm, Kevin J. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
In 1959, Campbell and Fiske introduced the use of multitrait-multimethod (MTMM) matrices in psychology, and for the past 4 decades confirmatory factor analysis (CFA) has commonly been used to analyze MTMM data. However, researchers do not always fit CFA models when MTMM data are available; when CFA modeling is used, multiple models are available…
Descriptors: Multitrait Multimethod Techniques, Factor Analysis, Structural Equation Models, Monte Carlo Methods
Grimm, Kevin J.; Pianta, Robert C.; Konold, Timothy – Multivariate Behavioral Research, 2009
Multitrait-multimethod (MTMM) confirmatory factor models were combined with longitudinal structural equation models to examine trait and method stability over time. A longitudinal correlated-trait correlated-method (CT-CM) model allowed for the study of trait and method variance in observed scores over time. Longitudinal measurement invariance was…
Descriptors: Multitrait Multimethod Techniques, Structural Equation Models, Longitudinal Studies, Children
LaGrange, Beth; Cole, David A. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
This article examines 4 approaches for explaining shared method variance, each applied to a longitudinal trait-state-occasion (TSO) model. Many approaches have been developed to account for shared method variance in multitrait-multimethod (MTMM) data. Some of these MTMM approaches (correlated method, orthogonal method, correlated method minus one,…
Descriptors: Structural Equation Models, Longitudinal Studies, Multitrait Multimethod Techniques, Correlation
Eid, Michael; Nussbeck, Fridtjof W.; Geiser, Christian; Cole, David A.; Gollwitzer, Mario; Lischetzke, Tanja – Psychological Methods, 2008
The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods…
Descriptors: Structural Equation Models, Multitrait Multimethod Techniques, Statistical Analysis, Error of Measurement
Spangler, Tamara; Gazelle, Heidi – Social Development, 2009
This study examines convergent and divergent validity for middle childhood anxious solitude, unsociability, and peer exclusion as assessed by five informants (peers, teachers, observers, the self, and parents). Participants were 163 (67 male, 96 female) third grade children (M age = 8.70 years). Parent reports were available for a subset of the…
Descriptors: Structural Equation Models, Multitrait Multimethod Techniques, Validity, Peer Relationship
Marsh, Herbert W.; Nagengast, Benjamin; Morin, Alexandre J. S.; Parada, Roberto H.; Craven, Rhonda G.; Hamilton, Linda R. – Journal of Educational Psychology, 2011
Existing research posits multiple dimensions of bullying and victimization but has not identified well-differentiated facets of these constructs that meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differentiated factors that are not so highly correlated as to detract…
Descriptors: Locus of Control, Test Bias, Bullying, Structural Equation Models
Li, Yuan H.; Tompkins, Leroy J. – International Journal of Testing, 2004
The primary objective of this study was to examine the construct validity for the 2 multiple-content testing programs-the multiple-choice Comprehensive Tests of Basic Skills (CTBS/5) together with the performance-based Maryland School Performance Assessment Program (MSPAP)-by evaluating the true-score longitudinal associations among…
Descriptors: Testing Programs, Structural Equation Models, Performance Based Assessment, Multitrait Multimethod Techniques
Furlow, Carolyn F.; Beretvas, S. Natasha – Psychological Methods, 2005
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…
Descriptors: Rejection (Psychology), Monte Carlo Methods, Least Squares Statistics, Correlation
Bechger, Timo M.; Maris, Gunter – Psicologica: International Journal of Methodology and Experimental Psychology, 2004
This paper is about the structural equation modelling of quantitative measures that are obtained from a multiple facet design. A facet is simply a set consisting of a finite number of elements. It is assumed that measures are obtained by combining each element of each facet. Methods and traits are two such facets, and a multitrait-multimethod…
Descriptors: Structural Equation Models, Multitrait Multimethod Techniques, Schematic Studies, Correlation