Estimation and comparison of latent means across cultures

Cross-Cultural Analysis: Methods and Applications

Volumn , Issue , 2012, Pages 85-116

  Steinmetz, Holger ^a  

^a JUSTUS LIEBIG UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84881612932     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203882924     Document Type: Chapter

References (63)

1. Arbuckle, J. (1999). Amos 4. 0 users guide. Chicago, IL: SPSS.
2. Bagozzi, R. P. (2007). On the meaning of formative measurement and how it differs from reflective measurement: Comment on Howell, Breivik, and Wilcox (2007). Psychological Methods, 12, 229-237.
3. Bauer, D. J., & Curran, P. J. (2004). The integration of continuous and discrete latent variable models: Potential problems and promising opportunities. Psychological Methods, 9, 3-29.
4. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.
5. Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.
6. Bollen, K. A. (2007). Interpretational confounding is due to misspecification, not to type of indicator: Comment on Howell, Breivik, and Wilcox (2007). Psychological Methods, 12, 219-228.
7. Bollen, K. A., & Davis, W. R. (1994). Causal indicator models: Identification, estimation, and testing. Unpublished paper, University of North Carolina at Chapel Hill.
8. Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110, 305-314.
9. Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation modeling. Sociological Methods & Research, 21, 205-229.
10. Borsboom, D. (2008). Latent variable theory. Measurement, 6, 25-53.
11. Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110, 203-219.
12. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford.
13. Byrne, B., Shavelson, R. J., & Muthen, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456-466.
14. Cadogan, J. W., Souchon, A. L., & Procter, D. B. (2008). The quality of market-oriented behaviors: Formative index construction. Journal of Business Research, 61, 1263-1277.
15. Chen, F. F., Sousa, K. H., & West, S. G. (2005). Testing measurement invariance of secondorder factor models. Structural Equation Modeling, 12, 471-492.
16. Cheung, G. W., & Rensvold, R. B. (1999). Testing factorial invariance across groups: A reconceptualization and proposed new method. Journal of Management, 25, 1-27.
17. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233-255.
18. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum.
19. Cohen, P., Cohen, J., Teresi, J., Marchi, M., & Velez, C. N. (1990). Problems in the measurement of latent variables in structural equations causal models. Applied Psychological Measurement, 14, 183-196.
20. Cole, D. A., Maxwell, S. E., Arvey, R. D., & Salas, E. (1993). Multivariate group comparisons of variable systems: MANOVA and structural equation modeling. Psychological Bulletin, 114, 174-184.
21. De Beuckelaer, A. (2005). Measurement invariance issues in international management research. Limburg, Belgium: Limburgs Universitair Centrum.
22. Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61, 1203-1218.
23. Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38, 269-277.
24. Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8, 128-141.
25. Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8, 430-457.
26. European Social Survey. (2002). Questionnaire development report. Unpublished report: Retrieved from http://www. europeansocialsurvey. org.
27. Finney, S. J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269-314). Greenwich, CT: Information Age.
28. Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466-491.
29. Franke, G. R., Preacher, K. J., & Rigdon, E. E. (2008). Proportional structural effects of formative indicators. Journal of Business Research, 61, 1229-1237.
30. Gagne, P. (2006). Mean and covariance structure mixture models. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 197-224). Greenwich, CT: Information Age.
31. Gerbing, D. W., Hamilton, J. G., & Freeman, E. B. (1994). A large-scale second-order structural equation model of the influence of management participation on organizational planning benefits. Journal of Management, 20, 859-885.
32. Hofstede, G. (1980). Culture's consequences: International differences in work-related values. Beverly Hills, CA: Sage.
33. Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and meta-analysis. Sociological Methods & Research, 26, 329-367.
34. House, R., Javidan, M., Hanges, P., & Dorfman, P. (2002). Understanding cultures and implicit leadership theories across the globe: An introduction to project GLOBE. Journal of World Business, 37, 3-10.
35. Howell, R. D., Breivik, E., & Wilcox, J. B. (2007a). Is formative measurement really measurement? Reply to Bollen (2007) and Bagozzi (2007). Psychological Methods, 12, 238-245.
36. Howell, R. D., Breivik, E., & Wilcox, J. B. (2007b). Reconsidering formative measurement. Psychological Methods, 12, 205-218.
37. Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 31, 199-218.
38. Joreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70, 631-639.
39. Little, T. D. (1997). Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32, 53-76.
40. 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.
41. Lubke, G., & Muthen, B. (2004). Applying multigroup confirmatory factor models for continuous outcomes to Likert scale data complicates meaningful group comparisons. Structural Equation Modeling, 11, 514-534.
42. Lubke, G., & Muthen, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21-39.
43. MacCallum, R. C., & Browne, M. W. (1993). The use of causal indicators in covariance models: Some practical issues. Psychological Bulletin, 114, 533-541.
44. Marsh, H. W., Tracey, D. K., & Craven, R. G. (2006). Multidimensional self-concept structure for preadolescents with mild intellectual disabilities: A hybrid multigroup-MIMIC approach to factorial invariance and latent mean differences. Educational and Psychological Measurement, 66, 795-818.
45. Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525-543.
46. Muthen, B. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557-585.
47. Muthen, B. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 1-33). Mahwah, NJ: Lawrence Erlbaum Associates.
48. Ployhardt, R. E., & Oswald, F. L. (2004). Applications of mean and covariance structure analysis: Integrating correlational and experimental approaches. Organizational Research Methods, 7, 27-65.
49. Rindskopf, D., & Rose, T. (1988). Some theory and applications of confirmatory secondorder factor analysis. Multivariate Behavioral Research, 23, 67.
50. Rosenthal, R., & DiMatteo, M. R. (2001). Meta-analysis: Recent developments in quantitative methods for literature reviews. Annual Review of Psychology, 52, 59-92.
51. Saris, W. E., Satorra, A., & van der Veld, W. M. (2009). Testing structural equation models or detection of misspecifications? Structural Equation Modeling, 16, 561-582.
52. Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. Clogg (Eds.), Latent variables analysis: Applications for Developmental Research (pp. 399-419). Thousand Oaks, CA: Sage.
53. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
54. Schafer, J. L., & Olsen, M. K. (1998). Multiple imputation for multivariate missing-data problems: A data analyst's perspective. Multivariate Behavioral Research, 33, 545-571.
55. Sinharay, S., Stern, H. S., & Russell, D. (2001). The use of multiple imputation for the analysis of missing data. Psychological Methods, 6, 317-329.
56. Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in crossnational consumer research. Journal of Consumer Research, 25, 78-90.
57. Steinmetz, H. (2010). Analyzing observed composite differences across groups: Is partial measurement invariance enough? Unpublished Manuscript.
58. Tabachnik, B. G., & Fidell, L. S. (2005). Using multivariate statistics. New York, NY: HarperCollins.
59. Thompson, M. S., & Green, S. B. (2006). Evaluating between-group differences in latent means. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 119-169). Greenwich, CT: Information Age.
60. Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4-69.
61. Wilcox, J. B., Howell, R. D., & Breivik, E. (2008). Questions about formative measurement. Journal of Business Research, 61, 1219-1228.
62. Yuan, K.-H., & Bentler, P. M. (2004). On chi-square difference and z tests in mean and covariance structure analysis when the base model is misspecified. Educational and Psychological Measurement, 64, 737-757.
63. Yuan, K.-H., Chan, W., & Bentler, P. M. (2000). Robust transformation with applications to structural equation modeling. British Journal of Mathematical and Statistical Psychology, 53, 31-50.