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雷达卡
I am trying to replicate an analysis done by a colleague (who used a different program for data analysis), and I believe that using a mixed model is the best way to handle the data. However, I'm unfamiliar with using mixed models and I'm getting an error every time I run the analysis. I imagine this is because the model is overspecified, but I'm not sure what I'm doing
wrong. I included my syntax at the end of the message.
The experimental design is like this: I have one variable, subjects, that I want to include as a random factor. The subjects saw letter strings on a computer screen and had to decide whether the string was a word or non-word. I have two within-subjects factors: "wordness" (whether the string was a word or non-word) and "relatedness" (whether the string was related to
another stimuli on the screen or unrelated). Together, I think, this forms a nested repeated measures design. The dependent variable is reaction time.
Here is my syntax for this model:
MIXED Rt2Adj_mean BY Relatedness Wordness Subj
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1)
SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
PCONVERGE(0.000001, ABSOLUTE)
/FIXED=Relatedness Wordness Relatedness*Wordness | SSTYPE(3)
/METHOD=ML
/PRINT=DESCRIPTIVES
/RANDOM=Subj | COVTYPE(VC)
/REPEATED=Relatedness*Wordness | SUBJECT(Subj) COVTYPE(UN).
I've also tried it with this slight adjustment, adding the intercept as a
random effect (which I think may be more correct?)
MIXED Rt2Adj_mean BY Relatedness Wordness Subj
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1)
SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
PCONVERGE(0.000001, ABSOLUTE)
/FIXED=Relatedness Wordness Relatedness*Wordness | SSTYPE(3)
/METHOD=ML
/PRINT=DESCRIPTIVES
/RANDOM=INTERCEPT Subj | COVTYPE(VC)
/REPEATED=Relatedness*Wordness | SUBJECT(Subj) COVTYPE(UN).
Either way, when I run the analysis, I get the following error: "Warnings Iteration was terminated but convergence has not been achieved. The MIXED procedure continues despite this warning. Subsequent results produced are based on the last iteration. Validity of the model fit is uncertain." Output is produced, but it doesn't come close to the the output I should be getting
that was produced by my colleague. I am wondering if the error is due to specifying subjects as a random effect but also as a subjects variable (using the GUI). However, I'm not sure how to get around this, as I do need
subjects to be a random effect.
It seems like this should be a simple enough design to analyze but I think my unfamiliarity with mixed models is causing me to run into problems. I've been trying to read up on them, but nothing has jumped out at me as a solution (although I do think that overspecification is potentially my problem). Any help would be greatly appreciated!
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