F1 by a1 a2 a3; F2 by b1 b2 b3; F3 by c1 c2 c3 c4;
F2 on F3; F2 on F1;
They are all continuous variables. The results showed O.K. model fit. But the factor loadings of F3 look counterintuitive. The STDXY loadings for c1 c2 c3 and c4 are .82, .90, .20, and .23, respectively. I am wondering why there is such big difference between the factor loadings of c1 c2 and those of c3 c4? Could there be any problems? Would it affect the results of the entire model? I did CFA for F3, the loadings also look like that. Could you tell me what I can do to improve my model fit, or, how to ensure that my model is identified? Thank you!
A follow up of your response, what did you mean by "the reliabilities of two items"? C1 C2 C3 and C4 are observed variables each contains 5 to 14 items. The internal reliability (Cronbach Alpha) are: C1: .59; C2: .76; C3: .60; C4: .48. I don't see that Alphas of C3 is lower than C1. Could you clarify it? Are we talking about the same "reliability"?
Another questions, if this is a issue caused by the data itself, is there anyway to deal with it? Thank you!
I am referring to reliability in the factor analysis sense -- variance explained for each indicator by the factor.
This issue is due to the data and it is dealt with by statistical modeling. Less reliable items are given less weight.
Bee Jay posted on Sunday, March 25, 2012 - 2:57 pm
I am also interested in looking at StdYX for path coefficients. I can get a value by using STANDARDIZED, but I want the table with the p-values, like on page 643 of the manual. When I type in that command though "STANDARDIZED (STDYX);" I get an error saying STDYX is an unrecognized option for output. What is the correct option? Somewhere online it had said just "STDYX" but that didn't work for me either.