Jason--
I'm not sure I understand what you mean by "linear test" here, but (1)
you should not in general use -nosvyadjust- but it seems to make no
difference in your case; I suspect your weight variable is always near
unity, and your stratum variable takes on one value; (2) one model is
the chi-2 "test-of-independence" and one is a test of equality of
proportions; (3) I would use regression techniques here, and a joint
test, I'm guessing, but it's not clear what your counterfactual is
supposed to be; in any case, looking for overlap in confidence
intervals is emphatically a bad way to test equality, especially of
proportions.
On 1/28/07, Jason Ferris <[email protected]> wrote:
1. As this is survey data, when I do linear tests SHOULD I use the
nosvyadjust options?
2. Why is my D-B model F(1, 4363) = 4.9500; P = 0.0261 significant yet
the linear test is not: F(1, 4363) = 3.73; Prob > F = 0.0535
3. This difference becomes a little concerning when one of my variables
say year5 has more than 2 categories (e.g., 5 year groups 16-20, 21-25
etc). When I want to test for linear differences between groups I
question the results of the linear test (see below). In particular the
proportions test for group 2 (_prop_2) has overlapping CIs. Yet the
linear tests suggest that these are statistically different (this is the
same for using or not using the option nosvyadjust; see below). This
raises the question for me: Can I use the linear test output to
highlight which groups are contributing to any overall differences?
svy: proportion year5, over(qmc16)
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