Whether or not to use random effects should depend on whether you wish to generalize the results to some populations that the sample is (hopefully) representative of. Usually we wish to generalize to some population of subjects. Typically (but not neccesarily) we are not interested in generalizing to a population of treatments that our current treatments represent. The measure issue is also more commonly seen as fixed rather than random. I am assuming you have some interest in comparing measures to each other (assuming they are comparable) rather than considering this design a multivariate one. The latter case would give rise to a fourth possibility of a two way multivariate anova rather than a three way univariate anova. In either case, I suspect you want subjects random and treatments fixed.
At 04:17 PM 2/9/01 GMT, you wrote:
>
>We have data from an experiment in psychology of hearing. There are 3
>experimental conditions (factor C). We have collected data from 5
>subjects (factor S). For each subject we get 4 measures of performance
>(M for Measure factor) in each condition. What is the best way to
>analyse these data?
>
>We've seen these possibilities :
>
>a) ANOVA with repeated measures with 2 fixed factors : subjects &
>conditions and the different measures as the repeated measure factor
>(random factor).
>
>b) ANOVA with two fixed factor (condition & measure) and a random
>factor (repeated measure-> subject factor).
>
>c) ANOVA with one fixed factor (condition) and the other two as
>random.
>
>We think that the a) design is correct (assuming and verifying that
>there is no special effect of the measure factor such as training
>effects).
>
>Other psychologist advised us to use the b) design because
>psychologists use to consider the subject effect as random. (in
>general experiments in psychology are ran with at least 20 to 30
>subjects).
>
>The last design (c)) is a possibility if we declare that we have no
>hypothesis on the effects of subject & repetition factors.
>
>
>I have only little theoretical background in stats and I like to know
>what exactly imply these possible designs.
>
>Thanks in advance for your help
>
>Sylvain Clement
>"Auditory function team"
>Bordeaux, France
>
>
>
>
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------------------------------------
Paul R. Swank, PhD.
Professor & Advanced Quantitative Methodologist
UT-Houston School of Nursing
Center for Nursing Research
Phone (713)500-2031
Fax (713) 500-2033
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