A Cross-Classified CFA-MTMM Model for Structurally Different and Nonindependent Interchangeable Methods

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A Cross-Classified CFA-MTMM Model for Structurally Different and Nonindependent Interchangeable Methods. / Koch, Tobias; Schultze, Martin; Jeon, Minjeong et al.

In: Multivariate Behavioral Research, Vol. 51, No. 1, 02.01.2016, p. 67-85.

Research output: Journal contributionsJournal articlesResearchpeer-review

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Koch T, Schultze M, Jeon M, Nussbeck FW, Praetorius AK, Eid M. A Cross-Classified CFA-MTMM Model for Structurally Different and Nonindependent Interchangeable Methods. Multivariate Behavioral Research. 2016 Jan 2;51(1):67-85. doi: 10.1080/00273171.2015.1101367

Bibtex

@article{2e77d054d9fb4a4ba2136b08263844e3,
title = "A Cross-Classified CFA-MTMM Model for Structurally Different and Nonindependent Interchangeable Methods",
abstract = "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.",
keywords = "Bayesian analysis, cross-classification, MTMM modeling, structurally different and interchangeable methods, Economics, empirical/statistics, Bayesian analysis, cross-classification, structurally different and interchangeable methods, MTMM modeling",
author = "Tobias Koch and Martin Schultze and Minjeong Jeon and Nussbeck, {Fridtjof W.} and Praetorius, {Anna Katharina} and Michael Eid",
year = "2016",
month = jan,
day = "2",
doi = "10.1080/00273171.2015.1101367",
language = "English",
volume = "51",
pages = "67--85",
journal = "Multivariate Behavioral Research",
issn = "0027-3171",
publisher = "Taylor & Francis",
number = "1",

}

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 -