Clarification(s), please:
On Wed, 23 Aug 2000, Ken Reed wrote:
> I'm trying to test whether a variable measures a group-level property,
> and so I'm looking for an analog to eta-squared, intra-class correlation
> etc for nominal or ordinal data.
Do you have a particular group-level property in mind, that is measured
by some variable(s) other than the one you're trying to test? Or are you
trying to infer the existence of some such property from the behavior of
this variable alone (or, perhaps, in concert with others)?
> I have data comprising 2000 workplaces, within samples of individuals
> drawn from each (n=20,000).
Random samples, or convenience samples? Ten from each workplace, or
variable (and if variable, why?)? Do the workplaces assort themselves
into categories, or are you looking at something like a 1-way ANOVA with
2000 groups?
> One variable has 4 categories (agree-neutral-disagree, don't know).
Are you trying to say that you have one such variable, and your other
variables are otherwise described; or that you have a number of such
variables and you want something like an item analysis of them all; or
that you have a number of such variables that you intend to combine in
some unspecified way to produce the variable you want to test; or ...?
By the way, this variable is not one variable, it is two: (1) degree of
agreement with whatever, and (2) whether the respondent has an opinion
about it. If you have a bunch of variables like this, what you can do
with them depends partly on how much missing data (= "don't know"
responses) you have.
> 1. How can I estimate how much of the total variability derives from
> between groups (workplaces) and within groups?
"Total variability" of one of these 4-category variables, or of a total
score derived from a bunch of them, or of the bunch of them considered as
a multivariate whole?
> 2. Is there a rule-of-thumb for what would be evidence of strong
> within-group agreement?
Rules of thumb exist only to help one avoid having to think hard about
some situation or problem. As such they are invariably heavily
dependent on contexts, about which we have very little information.
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