Well, I suppose that when the sample is too big almost every
relation will prove to be "significant". A lot of
pseudo-relations will occur. It will become difficult to detect
intermediate(1) variables or neutralise them because there will
be many candidates - if not all the variables will be
intermediating in some way or another. You will need to have a
lot of insight in the matter to distinguish between absurdities
and relevant relations, which makes it difficult for 'the
reader' to follow your analytical thinking and make the
separation between logical statistical steps and interpretation.
Also, at a certain point, if the sample contains a majority of
the population, chances that the non-responding group is
"significantly" different from the other increase.
Therefore, apart from economical arguments (increase in size
costs a lot but does not add value anymore) there are also
scientific arguments to put a limit to sample sizes.
In general, too large samples might distract you from all the
other elements you need to keep an eye on in judging if a survey
is conducted in a serious way.
E.g. I prefer a nicely stratified sample of 800 persons which
represents a response of say 80% in a smart design than a poorly
designed sample of 2500 persons representing a response of 37%.
Of course the latter is not a case of statistical tests as such
and more a question of professional or scientific attitude or
morale.
I would be happy to read your comments on that.
--DeLa
(1) I hope this is the correct word in English
P.G.Hamer <[EMAIL PROTECTED]> schreef in
berichtnieuws [EMAIL PROTECTED]
| DeLa wrote:
|
| > I have been trying to explain to some co-workers that a
sample
| > can be too big.
| > That is not very easy because it is contratictory to what
| > intuition says.
| >
| > Can someone point me to some good arguments or literature?
| > Or correct me if my assumption is wrong?
|
| I can see that huge sample might create problems by making
| some sorts of analysis too computationally demanding.
|
| A large sample will also expose the absurdity of some tests of
| significance.
|
| Can you explain your thinking?
|
| Peter
|
|
|
|
|
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