On Fri, 09 Feb 2001 16:17:04 GMT, [EMAIL PROTECTED]
(Sylvain Clément) 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
First, it seems to me, we need to know the scale type of your "M"
data, i.e., interval, ordinal, etc. Are your data means? Scores on
some type of testing device? Are they like: "hears"/"doesn't hear" or
what? If your data are nominal or ordinal, you may be required to use
another statistical mode. From what I glean from your account is
that the M factor is the response variable, right? Or is it truly a
factor as well which you want to analyze for main effects,
interaction, etc.?
The experimental condition variable is fixed (unless it is meant to
be a random selection from a population of experimental conditions).
Essentially, a repeated measures ANOVA is treated similarly to a
randomized block design with your subjects as the random factor. Each
subject receives all 3 treatments.
I think it might help for you to clarify the hypotheses first, then
develop a statistical plan which will correctly fit your data. Data
collection and good results are possible if the hypotheses and
statistical modality are determined a priori.
=================================================================
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
http://jse.stat.ncsu.edu/
=================================================================
