It is not very difficult to perform path analysis using Stata’s **regress** command, but it
does require the use of a **regress** command for each stage in the path analysis model. **pathreg**
is a convenience command that can compute the path analysis with a single command.
You can download **pathreg **over the internet by typing **search
pathreg** (see
How can I used the search command to search for programs and get additional
help? for more information about using **search**).

Let’s say that we want to estimate the following path model using the **hsb2** dataset.

We will begin by downloading the data over the internet and getting the correlation between
the two exogenous variables, **read** and **write**.

use http://www.ats.ucla.edu/stat/data/hsb2, clearcorr read write(obs=200) | read write -------------+------------------ read | 1.0000 write | 0.5968 1.0000

This path analysis is really just two regression models. The first model is
**math = _cons + read + write** while
the second model is **science = _cons + math + read + write**. Using **pathreg** we just
place each model inside parentheses (leaving off the equal signs, plus signs and constants).

pathreg (math read write)(science math read write)------------------------------------------------------------------------------ math | Coef. Std. Err. t P>|t| Beta -------------+---------------------------------------------------------------- read | .4169486 .0564838 7.38 0.000 .4563134 write | .3411219 .0610982 5.58 0.000 .3451322 _cons | 12.86507 2.82162 4.56 0.000 . ------------------------------------------------------------------------------ n = 200 R2 = 0.5153 sqrt(1 - R2) = 0.6962 ------------------------------------------------------------------------------ science | Coef. Std. Err. t P>|t| Beta -------------+---------------------------------------------------------------- math | .3190094 .0766753 4.16 0.000 .301854 read | .3015317 .0686815 4.39 0.000 .3122533 write | .2065257 .0707644 2.92 0.004 .1977167 _cons | 8.407353 3.192799 2.63 0.009 . ------------------------------------------------------------------------------ n = 200 R2 = 0.4999 sqrt(1 - R2) = 0.7071

We will use the standardized regression coefficients (Beta) as our path coefficients. Now we can
add the path coefficients and errors,
sqrt(1 – R^{2}) to the path diagram as shown below.