-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]]On Behalf Of KKARMA
Sent: Wednesday, July 11, 2001 2:04 AM
To: [EMAIL PROTECTED]
Subject: Bayesian analyses in education
As a teacher of research methodology in (music) education I am
interested in the relation between traditional statistics and the
bayesian approach. Bayesians claim that their approach is superior
compared with the traditional, for instance because it does not assume
normal distributions, is intuitively understandable, works with small
samples, predicts better in the long run etc.
If this is so, why is it so rare in educational research? Are there some
hidden flaws in the approach or are the researchers just ignorant?
Comments?
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S.F. Thomas gave a good reply.
I might add that probably most statisticians use methods that are
appropriate to the problem. In some problems, only a Bayesian approach
works. There are other problems in which a Bayesian approach is not
appropriate or is not a part of the problem.
In much of educational research, the focus is on the application of
measurement theory to develop relationships between factors, and ways to
measure concepts. The issue of the probability of a parameter value is not
of major concern, since general concepts of means, normality (multivariate)
and chi-square distributions are considered adequate in presenting results.
One example is the use of structural equation modeling (SEM), where the
focus is on model fit, and the cause and effect relationships that the model
implies.
A more recent interest is in the application of Bayesian concepts to
causality, such as, "We will adhere to the Bayesian interpretation of
probability, according to which probabilities encode degrees of belief about
events in the world and data are used to strengthen, update, or weaken those
degrees of belief. In this formalism, degrees of belief are assigned to
propositions in some language, and these degrees of of belief are combined
and manipulated according to the rules of probability calculus." (Judea
Pearl, Causality, Cambridge Press, 2000). The SEM modeling is non-Bayesian,
but the nature of the conclusions may be expressed in the form of "Bayesian
Networks". I would expect to see more of these concepts to show up in
educational research.
It allows one to express a degree of uncertainty in conclusions in a highly
technical language in which very few understand.
DAHeiser
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