Testing measurement invariance in the target rotated multigroup exploratory factor model

Structural Equation Modeling

Volumn 16, Issue 2, 2009, Pages 295-314

  Dolan, Conor V ^a   Oort, Frans J ^a   Stoel, Reinoud D ^a   Wicherts, Jelte M ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL DATA; FACTOR MODEL; MULTI-GROUP; TARGET ROTATION;

STATISTICAL METHODS;

EID: 70449568738     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705510902751416     Document Type: Article

References (45)

1. Basilevsky, A. (1983). Applied matrix algebra in the statistical sciences. Mineola, NY: Dover.
2. Bernaards, C. A., & Jennrich, R. I. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65, 676-696.
3. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
4. Bollen, K. A., & Long, J. S. (1993). Introduction. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 1-9). Newbury Park, CA: Sage.
5. Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111-150.
6. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
7. Browne, M. W., Cudeck, R., Tateneni, K., & Mels, G. (1998). CEFA: Comprehensive Exploratory Factor Analysis [Document and computer software]. Retrieved April 5, 2004, from http://quantrm2.psy.ohio-state.edu/browne/software.htm
8. Chan, W., Ho, R. M., Leung, K., Chan, D. K., & Yung, Y. (1999). An alternative method for evaluating congruence coefficients with Procrustes rotation: A bootstrap procedure. Psychological Methods, 4, 378-402.
9. Chen, F. F., Sousa, K. H., & West, S. G. (2005). Testing measurement invariance of second-order factor models. Structural Equation Modeling, 12, 471-492.
10. Costa, P. T., & McCrae, R. R. (1992). Revised NEO personality inventory (NEO-PI-R) and the NEO Five-Factor inventory (NEO-FFI) professional manual. Odessa, FL: Psychological Assessment Resources.
11. Cudeck, R., & O'Dell, L. L. (1994). Application of standard error estimates in unrestricted factor analysis: Significance tests for factor loadings and correlations. Psychological Bulletin, 115, 475-487.
12. Dolan, C. V. (2000). A model-based approach to Spearman's hypothesis. Multivariate Behavioral Research, 35, 21-50.
13. Dolan, C. V., Bechger, T., & Molenaar, P. C. M. (1999). Using structural equation modeling to fit models incorporating principal components. Structural Equation Modeling, 6, 233-261.
14. Dolan, C. V., Roorda, W., & Wicherts, J. M. (2004). Two failures of Spearman's hypothesis: The GATB in Holland and the JAT in South Africa. Intelligence, 32, 155-173.
15. Ferrando, P. J., & Lorenzo-Seva, U. (2000). Unrestricted versus restricted factor analysis of multidimensional test items: Some aspects of the problem and some suggestions. Psicologica, 21, 301-323.
16. Flury, B. D., Nel, D. G., & Peinaar, I. (1995). Simultaneous detection of shift in means and variances. Journal of the American Statistical Association, 90, 1474-1481.
17. Hattie, J., & Fraser, C. (1988). The constraining of parameters in restricted factor analysis. Applied Psychological Measurement, 12, 115-162.
18. Hessen, D. J., Dolan, C. V., & Wicherts, J. M. (2006). Multi-group exploratory factor analysis and the power to detect uniform bias. Applied Psychological Research, 30, 233-246.
19. Horn, J. L., & McArdle, J. J. (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research, 18, 117-144.
20. Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409-426.
21. Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443-477.
22. Jöreskog, K. G. (1993). Testing structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 294-316). Newbury Park, CA: Sage.
23. Jöreskog, K. G., & Sörbom, D. (1999). LISREL 8 [Computer program]. Chicago: Scientific Software International.
24. Lawley, D. N., & Maxwell, A. E. (1971). Factor analysis as a statistical method. London: Butterworth.
25. Little, T. D. (1997). Mean and covariance structures (MACS) analysis of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32, 53-76.
26. Little, T. D., Slegers, D. W., & Card, N. A. (2006). A non-arbitrary method of identifying and scaling latent variables in SEM and MACS models. Structural Equation Modeling, 13, 59-72.
27. Luria, A. R. (1978). The working brain. Middlesex, UK: Penguin.
28. McArdle, J. J., & Cattell, R. B. (1994). Structural equation models of factorial invariance in parallel proportional profiles and oblique confactor problems. Multivariate Behavioral Research, 29, 63-113.
29. McCrae, R. R., & Costa, P. T. (1997). Personality trait structure as a human universal. American Psychologist, 52, 509-516.
30. McCrae, R. R., Zonderman, A. B., Costa, P. T., Bond, M. H., & Paunonen, S. V. (1996). Evaluating replicability of factors in the Revised NEO Personality Inventory: Confirmatory factor analysis versus Procrustes rotation. Journal of Personality and Social Psychology, 70, 552-566.
31. McDonald, R. P. (1999). Test theory: A unified treatment. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
32. Mellenbergh, G. J. (1989). Item bias and item response. International Journal of Educational Statistics, 13, 127-143.
33. Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525-543.
34. Millsap, R. E. (2001). When trivial constraints are not trivial: The choice of uniqueness constraints in confirmatory factor analysis. Structural Equation Modeling, 8, 1-17.
35. Millsap, R. E., & Everson, H. (1991). Confirmatory measurement model comparisons using latent means. Multivariate Behavioral Research, 26, 479-497.
36. Millsap, R. E., & Kwok, O. (2004). Evaluating the impact of partial factorial invariance on selection in two populations. Psychological Methods, 9, 93-115.
37. Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2003). Mx: Statistical modeling (6th ed.). Richmond, VA: Virginia Commonwealth University, Department of Psychiatry.
38. Neale, M. C., & Miller, M. B. (1997). The use of likelihood-based confidence intervals in genetic models. Behavior Genetics, 27, 113-120.
39. Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50, 83-90.
40. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
41. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8, 23-74.
42. Schönemann, P. H. (1966). A generalized solution of the orthogonal procrustes problem. Psychometrika, 31, 1-10.
43. Sörbom, D. (1974). A general method for studying differences in factor means and factor structure between groups. British Journal of Mathematical and Statistical Psychology, 27, 229-239.
44. van der Sluis, S., Posthuma, D., Dolan, C. V., de Geus, E. J. C., Colom, R., & Boomsma, D. I. (2005). Sex differences on the Dutch WAIS-IIIR. Intelligence, 34, 273-289.
45. Wicherts, J. M., Dolan, C. V., Hessen, D. J., Oosterveld, P., van Baal, G. C. M., Boomsma, D. I., et al. (2004). Are intelligence tests measurement invariant over time? Investigating the nature of the Flynn effect. Intelligence, 32, 509-537.