On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

By Gitta H. Lubke, Conor V. Dolan, H. Kelderman, G. Mellenbergh. 2003

127 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5e485c71d60d52c6a1d75bfe3a09354abf35e6b6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. B. Muthén
2. Jelte M. Wicherts
3. Gitta H. Lubke
4. Conor V. Dolan
5. Daniel A. Sass
6. Eldad Davidov
7. Leslie Rutkowski
8. Dubravka Svetina

In Experimental and Cognitive Psychology

1. Gitta H. Lubke
2. Jelte M. Wicherts
3. Conor V. Dolan
4. B. Muthén
5. Daniel J. Bauer
6. T. Asparouhov
7. P. Curran
8. Cody Ding

In Management Science and Operations Research

1. Jelte M. Wicherts
2. B. Muthén
3. Conor V. Dolan
4. Gitta H. Lubke
5. Alexandre J. S. Morin
6. Daniel A. Sass
7. Eldad Davidov
8. Herbert W. Marsh

Citations in the corpus

• Measurement invariance in confirmatory factor analysis: an illustration using IQ test performance of minorities (2010, methodology): “The preferred method is fitting a series of increasingly restrictive models, which are presented in Table 12 (cf. Lubke et al., 2003a; Vandenberg & Lance, 2000).”
• Detecting Mixtures From Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics (2007, unknown): “Muthen & Muthen, 2000 ) and measurement invariance ( Lubke, Dolan, Kelderman, & Mellenbergh, 2003a , 2003b ; Lubke & Muthen, 2003; Rost, 1990 , 1991 ).”
• Investigating population heterogeneity with factor mixture models. (2005, background): “Evidence of measurement invariance almost certainly implies that a test or questionnaire measures the same underlying factors across classes and greatly simplifies the interpretation of results (Lubke et al., 2003; Meredith, 1993).”
• Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters. (2007, background): “All factor models in Part 1 are measurement invariant; that is, intercepts, loadings, and residual variances are class invariant ( Lubke, Dolan, Kelderman, & Mellenbergh, 2003; Meredith, 1993).”
• Are intelligence tests measurement invariant over time? Investigating the nature of the Flynn effect (2004, result): “The data from this last comparison stem from Must et al. (2003), who compared two Estonian data sets covering a period of 60 years, from 1934/1936 to 1997/1998.”