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st: intervening variables and path analysis
Richard Goldstein and Yan Cheng--
Any discussion of intervening variables or path analysis could touch
on hundreds of different sets of terminology from numerous
disciplines, but a seach on "structural equations models" should
encompass much of the relevant literature [in Stata, the first hit
should be on -reg3- probably, and see gllamm.org for complicated
models]. This thread is certainly a long one in economics, and no
short answer is likely to satisfy. However, I like the quote below
Everitt and Dunn (1991) note, "However convincing, respectable and
reasonable a path diagram... may appear, any causal inferences
extracted are rarely more than a form of statistical fantasy". The
authors are referring to the fact that ultimately path analysis deals
with correlation, not causation of variables. The arrows in path
models do indeed reflect hypotheses about causation. However, many
models may be consistent with a given dataset. Path analysis merely
illuminates which of two or more competing models, derived from
theory, is most consistent with the pattern of correlations found in
the data. The competing theories may be represented in separate path
models with separate path analyses, or may be combined in a single
path diagram, in which case the researcher is concerned with comparing
the relative importance of different paths within the diagram.
Everitt, B. S., and G. Dunn, G. (1991). Applied multivariate data
analysis. London: Edward Arnold.
On 3/5/07, Sergiy Radyakin <Radyakin@aoek.uni-hannover.de> wrote:
Your "diameter of blood vessel" seems to be quite a real variable though.
I am not a doctor, but I thought it works the other way around:
one smokes --> diameter decreases --> pressure increases --> heart rate
increases --> one dies ?
I wonder how do you establish causality in this case?
----- Original Message -----
From: "Richard Goldstein" <email@example.com>
To: "statalist" <firstname.lastname@example.org>
Sent: Monday, March 05, 2007 3:57 PM
Subject: st: advice re: "intervening" variable
> This is more a stat question that a Stata question.
> I have a system of 3 variables where one variable is in between,
> in time and physiology, the other two variables:
> blood pressure -> diameter of blood vessel -> heart rate
> That is, a change in blood pressure "causes" some change in
> the diameter of the blood vessel which in turn "causes"
> some change in heart rate (actually in "RRI" which is,
> basically, the inverse of heart rate).
> I have never come across this situation before, but I believe
> that several substantive disciplines do have such situations.
On 3/5/07, Yan Cheng <email@example.com> wrote:
I have some questions about path analysis, I tried the command 'pathreg' in the stata to condut it, however,
1. Are there any fitting indice to identify which model is better?
2. If the dependent variable is category, such as binary or multinomial variables, can I still use the same command 'pathreg'?
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