Which is the best CFA model for measuring invariance?
Configural invariance model: A multi-group CFA model fitted for each group separately, without any equality constraints. This model allows us to test whether the same factorial structure holds across all groups.
How to test for configural invariance in a model?
To test configural invariance, you fit the model you have specified onto each of the age groups, leaving all factor loadings and item intercepts free to vary for each group. You then compare model fit across all age groups — a good multi-group model fit suggests that the overall factor structure holds up similarly for all ages.
What are the three phases of measurement invariance?
The three typical phases of measurement invariance testing are as follows. Configural Invariance. Using age as an example, a configural invariance test allows you to examine whether the overall factor structure stipulated by your measure fits well for all age groups in your sample.
How to test for measurement invariance in R?
The R codes for this model are nearly identical to those from the configural model. However, we add group.equal = “loadings” to fix the factor loadings to be the same for the female and male models. After estimating the metric model, we will compare it against the configural model using the compareFit function from semTools.
What is the critical value of chi square?
The critical value of Chi-Square with 4 df is 9.49. Since 8.217 does not exceed 9.49, we conclude that the unconstrained model does not fit significantly better than the constrained model.
When do you not need to test metric invariance?
If the test is significant, then there is a lack of metric invariance and thus there is no need to test scalar and strict invariance. If, however, the test is not significant, then metric invariance is established and thus we can move to the next step, scalar invariance.
How to test group invariance with confirmatory factor?
Before we show how you can analyze this via tests of group invariance using confirmatory factor analysis, let’s consider some other methods that you might use. Exploratory Factor Analysis – You might try running an exploratory factor analysis for the graduate students and a separate exploratory factor analysis for the faculty members.