I established a structural equation model for testing measurement invariance over two conditions in four groups and I tested by using the command grouping. That leads me to bad model fits, but if I leave out this command, it fits better. Can you help me to explain these results? Another question: I established these nested models by starting with configural invariance. To make mplus to test the configural model, I have to restrict the first factor loading to 1 and so I have to fix the first factor loadings in the weak, strong and strict invariance models, too,right? By keeping this restrictions, I achieve bad model fits. Is there any chance to avoid the restiction of the first factor loadings? Here are my commands: usevar = P_NEO_1 P_NEO_6 P_NEO_11 P_NEO_16 P_NEO_21 P_NEO_26 P_NEO_31 P_NEO_36 P_NEO_41 P_NEO_46 P_NEO_51 P_NEO_56 C_NEO_1 C_NEO_6 C_NEO_11 C_NEO_16 C_NEO_21 C_NEO_26 C_NEO_31 C_NEO_36 C_NEO_41 C_NEO_46 C_NEO_51 C_NEO_56 Reihe; missing = all(99); GROUPING IS Reihe (0=g1 1=g2 2=g3 3=g4); MODEL: N_P BY P_NEO_1 P_NEO_6 P_NEO_11 P_NEO_16 P_NEO_21 P_NEO_26 P_NEO_31 P_NEO_36 P_NEO_41 P_NEO_46 P_NEO_51 P_NEO_56; N_C BY C_NEO_1 C_NEO_6 C_NEO_11 C_NEO_16 C_NEO_21 C_NEO_26 C_NEO_31 C_NEO_36 C_NEO_41 C_NEO_46 C_NEO_51 C_NEO_56; [N_P-N_C@0];N_P-N_C@1; [P_NEO_1-P_NEO_56];[C_NEO_1-C_NEO_56]; N_P WITH N_C;
Thank you very much for your answer. That helped me a lot.
So I have to test my groups against each other. Can you tell me, how to use only a part of the data within one variable? So that I can test within one variable the group of person 1 till 73 against the group of person 143 till 202?
I did use the USEVARIABLES option, but all of my groups are in one variable and I need to test the model fit for example within only one group. If I consider four variables (one for each group) instead of one, mplus says "FATAL ERROR", because the data matrix is too big (more variables than 350 variables).
For the FATAL ERROR I made a programming fault, but I found and corrected it. Thank you very much for your offering.
Now, to test the sequence effects, I need to override the default, that fixes the factor loadings and intercepts to be equal over the groups. How can I test a configural or weak Modell of measurement invariance?
The command * does only work for different conditions and having different variables loading on different factors, doesn't it?
Here are my commands: GROUPING IS Reihe (1=t1 2=t2); DEFINE: IF (Reihe==0 OR Reihe==1) THEN Reihe=1; IF (Reihe==2 OR Reihe==3) THEN Reihe=2; MODEL: N_P BY P_NEO_1* (a) P_NEO_6 (b) P_NEO_11 (c) P_NEO_16 (d) P_NEO_21 (e) P_NEO_26 (f) P_NEO_31 (g) P_NEO_36 (h) P_NEO_41 (i) P_NEO_46 (j) P_NEO_51 (k) P_NEO_56;(l) [P_NEO_1-P_NEO_56]; [N_P@0]; N_P@1;
I am trying to test gender invariance in a path analysis model with continuous variables. I have looked at your Topics 1 handout but I am confused as to what I should specifiy exactly in my input file. The only thing I changed to test whether the models are different for each gender in the GROUPING command. Here is the input I have so far: ... VARIABLE: MISSING ARE ALL (-999); NAMES ARE..... USEVAR ARE Sexe azagg azpop bzpop czpop dzpop aengcpt7 bengcpt7 cengcpt7 dengcpt7 eengcpt7 azaggami bzaggami czaggami dzaggami;
GROUPING IS Sexe (0 = filles 1 = garçons); ANALYSIS: ESTIMATOR = MLR; MODEL: dzaggami ON cengcpt7 czaggami czpop; czaggami ON bengcpt7 bzaggami bzpop; bzaggami ON aengcpt7 azaggami azpop; dzpop ON czpop czaggami cengcpt7; czpop ON bzpop bzaggami bengcpt7; bzpop ON azpop azaggami aengcpt7; eengcpt7 ON dengcpt7 dzaggami dzpop; dengcpt7 ON cengcpt7 czaggami czpop; cengcpt7 ON bengcpt7 bzaggami bzpop; bengcpt7 ON aengcpt7 azaggami azpop; azpop ON azagg; azaggami ON azagg; aengcpt7 ON azagg;
Is there anything else I should be adding to test this correctly? Many thanks in advance for your help! Genevieve Taylor
The GROUPING option should be used in all but the first step of testing for measurement invariance. The first step is to run the model separately for each group. The correct inputs are shown in the Topic 1 course handout under Multiple Group Analysis. Please refer to these inputs.
I am trying to understand a new approach to measurement invariance (approximate measurement invariance)implemented in Version of Mplus 7.11.
Q1. I ran a two-group CFA model for testing measurement invariance based on example 5.33. Under the DIFFERENCE OUT, I got average of estimate, standard deviation, deviations from the mean for each parameter and each group. I specified difference between two options like, N(0,0.01).
For example, I got Average: 1.422 SD: 0.031 Deviation from the mean: -0.03 (Lamda1), 0.03(Lamda2) --> How do I know whether the deviations from the mean in Lamda1 and 2 are significant or not?
Q2. Based on Muthen (2013) paper, it says that " With only two groups/timepoints, the difference relative to the average can be augmented by the difference across the two groups/timepoints which can be expressed in MODEL CONSTRAINT. If I want to test approximate measurement invariance between two group, what kinds of model constraint I need?
I have one more question. Can I test approximate measurement invariance in multilevel context? For example, can I conduct approximate measurement invariance test for between-level factor loadings? I have tried to do it by extending ex5.33 code but I couldn't. Please let me know.