Data-Generating Mechanisms Versus Constructively Defined Latent Variables in Multitrait–Multimethod Analysis: A Comment on Castro-Schilo, Widaman, and Grimm (2013)

Structural Equation Modeling

Volumn 21, Issue 4, 2014, Pages 509-523

  Geiser, Christian ^a   Koch, Tobias ^b   Eid, Michael ^b  

^a UTAH STATE UNIVERSITY   (United States)

^b FREIE UNIVERSITÄT BERLIN   (Germany)

Author keywords

constructively defined latent variables; CT C(M 1) model; CT CM model; multitrait multimethod (MTMM) analysis; psychometric theory

Indexed keywords

MATHEMATICAL MODELS; STATISTICAL METHODS;

CONFIRMATORY FACTOR ANALYSIS; CORRELATED METHODS; GENERATING MECHANISM; GENERATING MODELS; LATENT VARIABLE; MULTI METHODS; PSYCHOMETRIC THEORIES; REPARAMETERIZATION;

FACTOR ANALYSIS;

EID: 84927564218     PISSN: 10705511     EISSN: 15328007     Source Type: Journal    
DOI: 10.1080/10705511.2014.919816     Document Type: Article

References (26)

1. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait–multimethod matrix. Psychological Bulletin, 56, 81–105.
2. Castro-Schilo, L., Widaman, K. F., & Grimm, K. J. (2013). Neglect the structure of multitrait–multimethod data at your peril: Implications for associations with external variables. Structural Equation Modeling, 20, 181–207.
3. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.
4. Eid, M. (2000). A multitrait–multimethod model with minimal assumptions. Psychometrika, 65, 241–261.
5. Eid, M., Lischetzke, T., Nussbeck, F. W., & Trierweiler, L. I. (2003). Separating trait effects from trait-specific method effects in multitrait–multimethod models: A multiple indicator CT-C(M–1) model. Psychological Methods, 8, 38–60.
6. Eid, M., Nussbeck, F. W., Geiser, C., Cole, D. A., Gollwitzer, M., & Lischetzke, T. (2008). Structural equation modeling of multitrait–multimethod data: Different models for different types of methods. Psychological Methods, 13, 230–253.
7. Fiske, D. W., & Campbell, D. T. (1992). Citations do not solve problems. Psychological Bulletin, 112, 393–395.
8. Geiser, C., Eid, M., & Nussbeck, F. W. (2008). On the meaning of the latent variables in the CT-C(M–1) model: A comment on Maydeu-Olivares & Coffman (2006). Psychological Methods, 13, 49–57.
9. Geiser, C., Eid, M., West, S. G., Lischetzke, T., & Nussbeck, F. W. (2012). A comparison of method effects in two confirmatory factor models for structurally different methods. Structural Equation Modeling, 19, 409–436.
10. Grayson, D., & Marsh, H. W. (1994). Identification with deficient rank loading matrices in confirmatory factor analysis: Multitrait–multimethod models. Psychometrika, 59, 121–134.
11. Grigorenko, E. L., Geiser, C., Slobodskaya, H. R., & Francis, D. J. (2010). Cross-informant symptoms from CBCL, TRF, and YSR: Trait and method variance in a normative sample of Russian youths. Psychological Assessment, 22, 893–911.
12. Kenny, D. A., & Kashy, D. A. (1992). The analysis of the multitrait–multimethod matrix by confirmatory factor analysis. Psychological Bulletin, 112, 165–172.
13. Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
14. Marsh, H. W. (1989). Confirmatory factor analysis of multitrait–multimethod data: Many problems and a few solutions. Applied Psychological Measurement, 13, 335–361.
15. Marsh, H. W. (1993). Multitrait–multimethod analyses: Inferring each trait/method combination with multiple indicators. Applied Measurement in Education, 6, 49–81.
16. Marsh, H. W., & Bailey, M. (1991). Confirmatory factor analysis of multitrait–multimethod data: A comparison of alternative models. Applied Psychological Measurement, 15, 47–70.
17. Marsh, H. W., & Hocevar, D. (1988). A new, more powerful approach to multitrait–multimethod analyses: Application of second-order confirmatory factor analysis. Journal of Applied Psychology, 73, 107–117.
18. Novick, M. R. (1966). The axioms and principal results of classical test theory. Journal of Mathematical Psychology, 3, 1–18.
19. Nussbeck, F. W., Eid, M., Geiser, C., Courvoisier, D. S., & Lischetzke, T. (2009). A CTC(M–1) model for different types of raters. Methodology, 5, 88–98.
20. Pohl, S., & Steyer, R. (2010). Modeling common traits and method effects in multitrait–multimethod analysis. Multivariate Behavioral Research, 45, 1–28.
21. Pohl, S., Steyer, R., & Kraus, K. (2008). Modelling method effects as individual causal effects. Journal of the Royal Statistical Society Series A, 171, 41–63.
22. Rindskopf, D. (1984). Structural equation models: Empirical identification, Heywood cases, and related problems. Sociological Methods & Research, 13,109–119.
23. Schmid, J., & Leiman, J. (1957). The development of hierarchical factor solutions. Psychometrika,22, 53–61.
24. Steyer, R. (1989). Models of classical psychometric test theory as stochastic measurement models: Representation, uniqueness, meaningfulness, identifiability, and testability. Methodika, 3, 25–60.
25. Steyer, R. (1995). Das MTMM-Modell ist nicht identifiziert [The MTMM model is not identified]. Newsletter der Fachgruppe Methoden, 3, 5.
26. Zimmermann, D. W. (1975). Probability spaces, Hilbert spaces, and the axioms of test theory. Psychometrika, 40, 395–412.