Ulrich Schroeders
Recalculating df in MGCFA testing
Plase cite as follows:
Schroeders, U. & Gnambs, T. (in press). Degrees of freedom in multi-group confirmatory factor analysis: Are models of measurement invariance testing correctly specified? European Journal of Psychological Assessment.
A. Number of indicators |
C. Number of cross-loadings |
E. Number of resid. covar. |
B. Number of factors |
D. Number of ortho. factors |
F. Number of groups |
MI testing | constraints | df | comparison | delta(df) |
---|---|---|---|---|
config. | (item:factor) | 0 | - | - |
metric | (loadings) | 2 | metric-config | 2 |
scalar | (loadings+intercepts) | 4 | scalar-metric | 2 |
residual | (loadings+residuals) | 5 | residual-metric | 3 |
strict | (loadings+intercepts+residuals) | 7 | strict-scalar | 3 |
Additional information
A | Indicates the number of indicators or items. |
B | Indicates the number of latent variables or factors. |
C | Indicates the number of cross-loadings. For example, in case of a bifactor model the number equals twice the number of indicators (A). |
D | Indicates the number of orthogonal factors. For example, in case of a nested factor model with six indicators loading on a common factor and three items additionally loading on a nested factors, you have to specify 2 factors (B) and 1 orthogonal factor (D). |
E | Indicates the number of residual covariances. |
F | Indicates the number of groups. |
Further reading
- Beaujean, A. A. (2014). Latent variable modeling using R: a step by step guide. New York: Routledge/Taylor & Francis Group.
- Millsap, R. E. & Olivera-Aguilar, M. (2012). Investigating measurement invariance using confirmatory factor analysis. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (pp. 380-392). New York: Guilford Press.
- Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: Guilford Press.