I'm testing for gender differences an autoregressive cross-lagged model using a multiple group model (boys vs. girls). First I let the parameters free for boys and girls. Then I rerun the model with the parameter estimates equal between boys and girls. As a result, the chi-square of the model with free estimates has a higher chi-square (and of course fewer Df) than the one with the fixed parameters, resulting in a negative chi-square difference value.
Is a negative chi-square difference acceptable? And if yes, shall I report the value with the negative sign?
What did you mean by "the results are not meaningful"? Does it mean that the chi-square test is not valid? Or that the differences I've found between boys and girls in the free estimated model are not significant and are the results of the reduced sample sizes of model male and female? Or maybe something else?
I have a similar situation regarding Christina(the first poster in this thread) for my multi-group path analysis comparisons, using MLR, and getting a negative chi-square diff test.
I understand how to use model test for comparing one path at a time , but how would I go about comparing my unconstrained model to my partially constrained model using the model test command? Looking at the user guide, I am still not clear about this.
For example: Unconstrained t2a ON t2d; t2a ON t2p; t2a ON t2pm; t2d ON t2s; t2d ON t2p; t2s ON t2p; t2p ON t2m;
Constrained Model t2a ON t2d; t2aON t2p(1); t2a ON t2pm; t2d ON t2s(2); t2d ON t2p(3); t2s ON t2p; t2p ON t2m;
Thanks for your quick response. My apologies, I am still not quite understanding. This is what I did: MODEL: !unconstrained t2a ON t2d; t2a ON t2p(a1); t2a ON t2pm; t2d ON t2s(b1); t2d ON t2p; t2s ON t2p(c1); t2p ON t2m;
MODEL: !partial t2a ON t2d; t2aON t2p(a2); t2a ON t2pm; t2d ON t2s(b2); t2d ON t2p(c2); t2s ON t2p; t2p ON t2m;
MODEL TEST: 0=a1-a2; 0=b1-b2; 0=c1-c2; And I received this warning: "The following parameter label is ambiguous. Check that the corresponding parameter has not been changed. Parameter label: A1". Thanks again for the help!
You have to give parameter labels in each of your groups. So use this style:
Ashley Baker posted on Wednesday, October 12, 2016 - 1:02 pm
Thank you for your response and that worked for me.
My apologies for my questions (applied researcher, here), but I am unclear on how the syntax below is comparing the partially constrained model to the unconstrained model? Before I ran the constrained model and unconstrained models in separate runs, and then calculated out chi-square differences using the loglikelihood information, but had fit indices for both. Using model test I only receive fit indices for the constrained model. Are these two approaches the same, except for that the model test command helps to correct for the negative chi-square difference?
USEVARIABLES = t2m t2p t2s t2d t2a t2tran t2pm; GROUPING IS t2tran (0=PED 1=ADULT); MISSING ARE ALL (-999);
ANALYSIS: TYPE IS general; ESTIMATOR = MLR;
MODEL: t2a ON t2d; t2a ON t2p; t2a ON t2pm; t2d ON t2s; t2d ON t2p; t2s ON t2p(; t2p ON t2m;
MODEL PED: t2a ON t2d; t2a ON t2p(a1); t2a ON t2pm; t2d ON t2s(b1); t2d ON t2p; t2s ON t2p(c1); t2p ON t2m;
MODEL ADULT: t2a ON t2d; t2aON t2p(a2); t2a ON t2pm; t2d ON t2s(b2); t2d ON t2p(c2); t2s ON t2p; t2p ON t2m;
It looks like you set it up right. The two approaches provide the same testing, just two different statistical approaches - they are the same in large samples. You are right that the Model Test approach doesn't give the overall fit for the constrained model.
Ashley Baker posted on Wednesday, October 12, 2016 - 8:58 pm
Hi, I am conducting cross-lagged relationships between two variables (A and B). I would like to compare four models: the stability model, the standard causality model (from A to B), the reverse causality model (from B to A) and the reciprocal causality (from A to B and from B to A). I am conducting the analysis with MLR estimator and I calculated the Chi Square difference with the Satorra and Bentler formula. However, the difference is negative and thus, I cannot calculate the p value. Does this mean that there is not a significant difference between the models? I did the same analysis with ML estimator and the p values are not significant. I guess this means that model fit does not really improve from one to another. Is ML trustworhty or I should stick to MLR? Thank you very much