A Cross-Classified CFA-MTMM Model for Structurally Different and Nonindependent Interchangeable Methods
Research output: Journal contributions › Journal articles › Research › peer-review
Standard
In: Multivariate Behavioral Research, Vol. 51, No. 1, 02.01.2016, p. 67-85.
Research output: Journal contributions › Journal articles › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - A Cross-Classified CFA-MTMM Model for Structurally Different and Nonindependent Interchangeable Methods
AU - Koch, Tobias
AU - Schultze, Martin
AU - Jeon, Minjeong
AU - Nussbeck, Fridtjof W.
AU - Praetorius, Anna Katharina
AU - Eid, Michael
PY - 2016/1/2
Y1 - 2016/1/2
N2 - Multirater (multimethod, multisource) studies are increasingly applied in psychology. Eid and colleagues (2008) proposed a multilevel confirmatory factor model for multitrait-multimethod (MTMM) data combining structurally different and multiple independent interchangeable methods (raters). In many studies, however, different interchangeable raters (e.g., peers, subordinates) are asked to rate different targets (students, supervisors), leading to violations of the independence assumption and to cross-classified data structures. In the present work, we extend the ML-CFA-MTMM model by Eid and colleagues (2008) to cross-classified multirater designs. The new C4 model (Cross-Classified CTC[M-1] Combination of Methods) accounts for nonindependent interchangeable raters and enables researchers to explicitly model the interaction between targets and raters as a latent variable. Using a real data application, it is shown how credibility intervals of model parameters and different variance components can be obtained using Bayesian estimation techniques.
AB - Multirater (multimethod, multisource) studies are increasingly applied in psychology. Eid and colleagues (2008) proposed a multilevel confirmatory factor model for multitrait-multimethod (MTMM) data combining structurally different and multiple independent interchangeable methods (raters). In many studies, however, different interchangeable raters (e.g., peers, subordinates) are asked to rate different targets (students, supervisors), leading to violations of the independence assumption and to cross-classified data structures. In the present work, we extend the ML-CFA-MTMM model by Eid and colleagues (2008) to cross-classified multirater designs. The new C4 model (Cross-Classified CTC[M-1] Combination of Methods) accounts for nonindependent interchangeable raters and enables researchers to explicitly model the interaction between targets and raters as a latent variable. Using a real data application, it is shown how credibility intervals of model parameters and different variance components can be obtained using Bayesian estimation techniques.
KW - Bayesian analysis
KW - cross-classification
KW - MTMM modeling
KW - structurally different and interchangeable methods
KW - Economics, empirical/statistics
KW - Bayesian analysis
KW - cross-classification
KW - structurally different and interchangeable methods
KW - MTMM modeling
UR - http://www.scopus.com/inward/record.url?scp=84958794078&partnerID=8YFLogxK
U2 - 10.1080/00273171.2015.1101367
DO - 10.1080/00273171.2015.1101367
M3 - Journal articles
C2 - 26881958
AN - SCOPUS:84958794078
VL - 51
SP - 67
EP - 85
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
SN - 0027-3171
IS - 1
ER -