hello!Sorry for the long pause. I found model 9 but don't see where can i fit the covariate-anxiety. Anxiety was measured only once. There's just LS which is the DV with the three repeated levels and Listlength that is a factor variable computed to separate the three levels. Spss data set is in long format:LS LISTLENGTHID1 1223 1ID1 1345 2ID1 1567 3This is the syntax from model 9 adapted for my variables:MIXED LS BY LISTLENGTH/FIXED = LISTLENGTH/REPEATED = LISTLENGTH | SUBJECT(SUBJECT_NAME) COVTYPE(CS).Results: LISTLENGTH effect-significantWhere can I fit the covariate? Also, going back to the SPSS menu, should I keep Listlength as a factor or as a covariate? If I put it as a covariate then I can't use the EMMEANS command for post hoc tests. Also from the first window from mixed if I choose both Subject and repeated then is it correct to include an intercept also? I read that it is equivalent to use subjects + random intercept (without repeated variable) OR subject + repeated.Another important question: how can I test the interaction of the covariate (anxiety) for every level of LS variable? (ANXIETY* LISTLENGTH). It's obvious that with EMMEANS command I can only do posthocs for the factors includes as fixed effects NOT for interaction. I found a syntax for testing contrasts and interaction effects at ucla's site here but it's for a between factor (diet) with two levels and a repeated exercise type (exertype) with three levels. The Helmert coding is for comparing levels 1,2 vs level 3 of the rep variable but I would like something like a repeated contrast comparing every list level with the previous (there should be a liniar increase as I have the same memory measure with increasing difficulty)Mixed pulse by diet exertype time/fixed = diet exertype time diet*exertype /repeated = time | subject(id) covtype(ar1) /test = 'exertype 12v3 & diet 1v2' diet*exertype -.5 -.5 1 .5 .5 -1.On Tue, Nov 25, 2014 at 4:25 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARDAttachments won’t pass through the list serv so the attachment wasn’t distributed although I think it will be able to be seen on nabble.
In the help dropdown you should see either “Syntax Reference” or “Command Syntax Reference”
You’ve done the analysis it sounds like. Would you post the syntax and state what effects were significant.
Gene Maguin
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Alexandra
Sent: Monday, November 24, 2014 5:27 PM
To: [hidden email]
Subject: Re: REPEATED MIXED MODELS-post hoc tests/contrasts
Thanks for your response. Trait anxiety was measured only once. I did a scatterplot on List length and Anxiety interaction. I attach the plot. You said I should make another plot with the fixed effect but not random effect and find the interaction where there are regions of significance....should I do then a different plot then the one attached?
On Mon, Nov 24, 2014 at 8:11 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
I'm assuming that LMM means the Mixed command. I urge you to look in the Syntax Reference at Models 9 and 10 in the examples for Mixed. It's time to learn and use syntax. You don't say but if you measured anxiety once, i.e., trait anxiety, then Model 9 is a good example. If you measured anxiety, i.e., state anxiety, prior to each presentation, then Model 10 is a good example because anxiety is a time varying covariate.
With respect to the random intercept, I want to defer to others who are more experienced.
That you have that anxiety covariate and are looking for a list-length by anxiety interaction is complicated. Perhaps you've done this already but I suggest plotting the DV by anxiety by list length to get an idea of where the interactions may exist. You also ought to save and plot the fixed predicted values (assuming no random effects) by anxiety and list length (and for the same reason). If there is an interaction, you need to identify what is called, I believe, "regions of significance". At least one person has written a macro for this and I don't recall who it is. I believe that you can iteratively identify the region boundaries using emmeans because you can iterate on covariate (i.e., anxiety) values and see the significance of the difference. The Test subcommand is more powerful but harder to use.
Gene Maguin
-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Alexandra
Sent: Monday, November 24, 2014 11:02 AM
To: [hidden email]
Subject: REPEATED MIXED MODELS-post hoc tests/contrasts
Hello!
I am studying the effect of trait anxiety on working memory efficiency
(RT) and accuracy and I need some help with using LMM.
The working memory task had four blocks varying in list length presentation, presented to each subject (1,2,3,4 words to recall). I was thinking to run a LMM with a random intercept with RT / ACC as a DV, list length as a repeated within factor (or covariate?), anxiety as a covariate.
Therefore, I have the fixed effects: list length, anxiety and list length * anxiety interaction and at random effects, include intercept and Subjects (is that ok?)
So I have the following variables:
DV: efficiency score/ accuracy for every list length (continuous-within repeated measure)
IV: list length (categorical)
Covariate: trait anxiety (continuous)
*each repeated measure (level 1) is within subject category (level 2). I already put the data in long format.
My questions are:
1) in order for SPSS to know it's repeated data, should I choose from the
first window of LMM, Subject ID at subjects and list length as a repeated measure? Or is it enough subjects and then include the random intercept?
2) * how do I compare between list lengths? I want to show for example that list length’s effect is more significant at list length 4 that list length 1. So there are sign differences between lists. Should I use post hoc tests?
Also, how do I compare which list length is more affected by it’s interaction with anxiety? Is there a contrast option in LMM for that?*
Thanks a lot! any kind of input would be very very helpful...
Alexandra
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