I think this may be a very simple question, and it may just be about output interpretation, but I'm not sure.
I am using the auxiliary (r) option to explore the extent to which a covariate predicts latent class membership. i have done this successfully for boys and girls separately, looking at the pseudo-class draws. However, what I really want is to be able to compare across as well as within gender.
Can I use the knownclass option in combination in order to examine gender differences? When I run a model with:
classes = sex (2) ldf(4) ; knownclass = sex (kz021=1 kz021=2);
Either I don't get all comparisons I expect to, or I do, but I don't understand the output!
Sorry Linda, just to check, you mean that the gender variable is treated as a latent variable even though it's known?
So, second question then - is there a way to look at gender differences using the auxiliary (r) command? I'm after comparisons between and within gender for my 4 classes (grouping doesn't work, of course, because these are mixture models).
Following up on a similar question as posed in this thread. I am new to Latent Class/Profile Analysis and am currently attempting to get an LPA to run off of 5 continuous variables while also distinguishing differences between a treatment and control condition. I have used both Auxiliary and knowngroup variables to model this treatment/control, but appear to get slightly different results. What exactly is the mathematical difference between these two?
You mention auxiliary r. There is now a better approach in auxiliary R3STEP. See our web note 15.
Knownclass is basically the same as having a binary covariate. I assume that you include
c ON cg;
where cg is the knownclass variable. Including the covariate in the model implies that the classes are formed also based on information from this covariate, not only using the information from the LPA indicators.
My goal is to examine if the classes/profiles created by the LPA indicators differ across the knownclass covariate (in this case, a variable which specifies if the participant is in either a treatment or control group).
So ideally, I want to see the program create c (the profiles), and then examine the differences between the knowngroups.
So my current input is:
VARIABLE: NAMES ARE x y1-y5; CLASSES = c(4) xclass(2); knownclass = xclass( x=1 x=2); Plot: type is plot3; series is y1 (1) y2 (2) y3 (3) y4 (4) y5 (5); ANALYSIS: TYPE = MIXTURE; STARTS = 100 20; OUTPUT: TECH1 RESIDUAL; SAVEDATA: File is Teststhis2.out; save is cprob; format is free;
Does this leave the knowngroup as an auxiliary variable, or is it being included in the creation of the profiles?