Hi all,
if you want to test for first difference in an interaction model
(logistic regression) of the following sort: y=a+b1x1+b2x2+b3x1x3 where
both x1 and x2 are dummy variables you would normally write:
simqi , prval(1) fd(prval(1)) changex(x1 0 1). Right?
but would the program than know for itself that when x1==0 the
interaction effect x1x2 is also 0? can one take a significant difference
to be indicative or does the model not make any sense? or is there an
alternative command for this situation?
Thanks in advance,
a sociologist
--
Oshrat Hochman, PhD
Department of Sociology and Anthropology
Tel Aviv University
Dear Clarify users,
I have a question. Is there a way with CLARIFY to calculate the impact of a change in the LEVEL of an explanatory variable (X) on the LEVEL of the dependent variable(Y)?
With OLS, the way to do it is simply to make the following calculation:
Delta Y = b Delta X, where b is the unstandardized regression coefficient (Achen 1982).
With logistic regression, the above formula is inappropriate. Possible solutions are:
(1) To use weighted least-squares or generalized least-squares (Finkel, JoP, 1993: 8). In this case, the regression coefficient can be used directly to estimate the impact of a change in the LEVEL of X on the change in the LEVEL of Y.
(2) To use the (quite demanding) approach proposed by Denk and Finkel in the 1992 AJPS piece.
Could Clarify perform a similar estimation with logistic regressions?
Many thanks for your help,
Thomas Didier
McGill University
