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From: SPSSX(r) Discussion [mailto:[hidden email]]
On Behalf Of Alexandra
Sent: Tuesday, December 30, 2014 1:06 PM
To: [hidden email]
Subject: Re: REPEATED MIXED MODELS-post hoc tests/contrasts
As you said, indeed the correlations between LSRT and anxiety in LL1 and LL2 are very similar, hence the plot shows almost parallel lines for these two list lengths. Only for LL3 the slope has a upward direction intersecting the regression
lines for both LL2 and LL1.
You also said this:
"I also want to acknowledge that I am unsure of whether there are additional issues or considerations because you are looking at the interaction of a covariate with a repeated factor."
You wanted to mean that I shouldn't search for these points of significance because I am looking at the interaction of a covariate with a repeated factor that could as you mentioned : " not have to be a possible
anxiety scale score."
>>What I meant was that I have seen the Johnson-Neyman method used for a regression equation having a between person dependent variable (DV). You have a within
person DV. It seems like it ought to work here as well. I have not read about using that method in this situation.
I still tried to do what you recommended. So, in order to search for points of significance I should just look at the value at which there is an interaction between LL1-LL3 and LL2-LL3 and just write
that value? for example if for the first interaction the plot shows an anxiety score between 10-20, I should just write the same command with every consecutive score from 10-20 and see when the mean is higher in LL3 compared to LL1? (that was obtained in the
estimates of fixed effects-negative t result that shows a stronger relationship between anx-lsrt in the LL3 compared to LL1-is that correct???):
/EMMEANS=TABLES(LISTLENGTH) with (Trait_anxiety=17).
>>Yes, but you can be more efficient. Suppose the plot shows that LL1 and LL3 regression lines cross at about 6. Suppose the possible anxiety score range (mean
of items) is 1 to 7. Check whether the LL1-LL3 effect is significant for 1. If no, you’re done (do you know why?) If yes, check halfway between 1 and 6, 3.5, and so on.
The first smallest anx value in this range where the mean of LL1 is smaller that LL3 is when anx has the value 17. Is this the value for the first sign anx-ls rt interaction (by LL1-LL3)? how should I interpret this result? should I state
the confidence intervals and the mean Rt for every list length?
For the second interaction (LL2-LL3) the smallest value of anx where the same relationship appears (LL2<LL3) is for the value of 24 in anx (supported by the plot). Every anx score beyond 24 leads to the same relationship L1<L2<L3 (LS RT
means).
>>Not sure what you are telling me. Are you saying that when anxiety=17 or less, the LL1-LL3 difference is significant and when anxiety is greater than 17, say
18, the LL1-LL3 difference is not significant?
>>And the LL2-LL3 difference was significant for anxiety GREATER THAN 24? Or was it significant for anxiety LESS THAN 24?
I also have some questions concerning LLength.
- When I run the LMM with List lenght as the repeated measure or only as a random
slope, then the model does not acheive convergence. But when I use only a random intercept and subject combinations (random option), convergence is achieved. The resulting intercepts and betas from these models are slightly different, but they're all significant
except for LLength's main effect.
>>I do not believe that this analysis statement is correct. You do not have a repeated factor and a random factor. I may be wrong but I think that the fact that
you are having convergence problems supports my assertion.
WITH REPEATED LLENGHT
MIXED LSRT BY LISTLENGTH gender WITH Trait_anxiety
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,
ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)
/FIXED=LISTLENGTH gender Trait_anxiety LISTLENGTH*Trait_anxiety | SSTYPE(3)
/METHOD=ML
/PRINT=SOLUTION TESTCOV
/RANDOM=INTERCEPT | SUBJECT(COD_subiect) COVTYPE(UN)
/REPEATED=LISTLENGTH | SUBJECT(COD_subiect) COVTYPE(UN).
WITHOUT REPEATED LLENGTH (BUT LLENGTH RANDOM SLOPE)
MIXED LSRT BY LISTLENGTH gender WITH Trait_anxiety
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,
ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)
/FIXED=LISTLENGTH gender Trait_anxiety LISTLENGTH*Trait_anxiety | SSTYPE(3)
/METHOD=ML
/PRINT=SOLUTION TESTCOV
/RANDOM=INTERCEPT LISTLENGTH | SUBJECT(COD_subiect) COVTYPE(UN)
ONLY WITH RANDOM INTERCEPT
MIXED LSRT BY LISTLENGTH gender WITH Trait_anxiety
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,
ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)
/FIXED=LISTLENGTH gender Trait_anxiety LISTLENGTH*Trait_anxiety | SSTYPE(3)
/METHOD=ML
/PRINT=SOLUTION TESTCOV
/RANDOM=INTERCEPT | SUBJECT(COD_subiect) COVTYPE(ID).
1)
What's the difference between using LLength as REPEATED, RANDOM SLOPE or using just a random intercept with subject combinations?
>>Basically, they are different was of constructing the analysis.
2) I still wonder, does it make sense to look for the Llength-anx interaction when LLength's main effect becomes insignificant when I introduce the interaction
in the model. Before using the interaction, Llength's effect is sign.
Yes! Absolutely! Your result shows that reaction time depends on both list length and anxiety level. I’m guessing that lower anxiety predicts shorter reaction
times as the list length increases. Stated differently, anxiety may not matter much when the list is short but as the list lengthens higher anxiety impedes reaction time.
Gene Maguin
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Re: REPEATED MIXED MODELS-post hoc tests/contrasts
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