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What is the difference between fixed effects model and random effects model for a meta-analysis of sample correlations ?

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I am having difficulties to understand your question. Why does your title refer to "multivariate ANOVA" but your question not? You might want to give more details and then we might be able to answer your question. – Bernd Weiss Jun 14 '12 at 12:19
I edited the title to ensure that the question makes sense. – Jeromy Anglim Jun 15 '12 at 3:54
Possible duplicate of: stats.stackexchange.com/questions/4700/… – Macro Jul 9 '12 at 13:49
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Thanks for the comments offered by my senior colleagues.Hopefully, I shall implement the suggestions and be clear in asking the questios. I am a new User. Moreover, I have not done any fulfledged course in statistics. Please bear with me. – subhash c. davar Jul 9 '12 at 15:33
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Thanks @subhashdavar, my suggestion is as much to help you get useful answers as it is to help others find your questions (and the subsequent answers) useful in the future. Welcome to the site, by the way! – Macro Jul 9 '12 at 16:14
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In a fixed-effects model, you are assuming that the true correlation estimated in each study is the same. In the Random effects model you accept that there is variation in the true correlation being estimate in each study.

Thus, the fixed-effects model assumes that observed variation in estimated correlations is due only to effect of random sampling.

It deciding between the two, you would often use a combination of theoretical knowledge and observed data. Theory will often suggest that the true correlation should vary somewhat between studies. You can also examine various test statistics on the observed correlations to assess whether the variation appears more than you would expect based on random sampling (e.g., see this discussion about Cochran's Q and related indices).

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Thanks. I am interested in a clear understandig of the assumptions in the context of ANOVA.It seems we have an assumption in ANOVA and same in case of meta-analyis of sample-correlations. The assumption of random scores at primary level generates a fixed r value for a study. The random distribution of scores rules out the effects of several moderator and or extraneous factors on the r-value of a particular study.That is the reason, we get a fixed quantity for r and there is no possibility of a range of values for r everytime you work on a sample. – subhash c. davar Jun 22 '12 at 3:14
@subhashdavar It sounds like you may have a separate question in that comment. I suggest asking a new question and perhaps including a link to this one. – Jeromy Anglim Jun 22 '12 at 3:35
Thanks.Is fixed-effects assumption different in case of ANOVA and meta-analysis. I am not able to clarify properly. If you could spare time to read my articles dealing with mea-analysis of sample correlatio( Davar(2004) and Davar (2006)available on EBSCOhost, please do so and help me in clarifying the position. – subhash c. davar Jun 22 '12 at 4:17
My suggestion is that you press "ask question" and write up a separate question. You are more likely to get an answer to your question. – Jeromy Anglim Jun 22 '12 at 4:53
Dear Mr Weiss, I am planning to work on the idea that multivariate ANOVA type model can be applied for a meta-analysis of sample-correlations.Recently, random-effects models (eg Hedges and OLkin ) have been advocated. I wish I could get an idea about the difference in assumptions for a random effects model vis a vis fixed effects (ANOVA) type model. You may help me in further restructuring the question ? – subhash c. davar Jun 24 '12 at 5:12
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Fixed-effects model assumes that there is only one single effect that will be generated for a particular population ie there is a point-estimate and thus probably, satisfies the central limit theorm. The random effects model presumes that a number of effect-sizes can be generated (pobably have been generated) for a population and we have got just one of them for a meta-analysis. This is what I can infer from Hedges and Olkin (1985)saying that there is a super-population from which effect-sizes have been generated. For example, we have got one effect-size (study) from a particular population and other studies may not be traceable.It appears that we can estimate an interval and not the point estimate.

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