Web page to
support
Preacher, K. J., &
Hayes, A. F. (2004). SPSS and SAS
procedures for estimating indirect effects
in simple mediation
models. Behavior Research Methods, Instruments, and Computers, 36, 717-731.
If you will be using this macro in published work,
please cite the paper rather than this web page.
Click
here to download the SPSS syntax
file
Click
here to get the SPSS data file from Preacher and
Hayes paper
Get
a PDF of Preacher and Hayes (2004) by clicking here
Can’t get the macro to work or have questions? Check here for some macro rules and answers to frequently asked
questions.
Join the new Statistical Mediation and Moderation
Analysis group on Facebook.
If you are interested in moderation (a.k.a.
“interaction”) rather than mediation, click here.
Can an effect that doesn’t exist be “mediated”? Click here.
NOTE: WE HAVE DEVELOPED A FAR
NOTE: Some users of SPSS 18
have reported problems with macros and scripts.
Click here for more information and a
possible solution.
Interested
in testing a moderated mediation hypothesis?
Click here.
NEW: As of
January 3, 2009, the SOBEL macro for SPSS (but not SAS) works with dichotomous
outcome variables (Y only). See the SOBEL syntax reference for details.
A script has also been produced for SPSS that
uses a Windows-style dialog box for setting up the model (see below). Download the script file here. You should open
this file within SPSS as a script file.
It will not run as a syntax file.
Here is a document on opening and running
scripts in SPSS for Windows.
SPSS SOBEL script dialog box
Instructions
for Use of SPSS Macro [PDF]
To activate the macro,
execute the SOBEL macro command set that you downloading from the link
above. Run it exactly as is. Do not modify it one bit. Once the command set is executed, a new SPSS
syntax command, SOBEL, will be available for use. This command is available
until SPSS is closed. To run the mediation analysis on a data set, execute the
following command in SPSS:
SOBEL y=yvar/x=xvar/m=mvar/boot=z.
where yvar is the name of the dependent
variable in your data file, xvar is the name of the independent variable, mvar is the name of the
proposed mediating variable, and z specifies the number of bootstrap
resamples desired, in increments of 1000 up to a maximum of 1,000,000. For
example, if z is set to 3000, then the bootstrap
estimates will be based on 3000 resamples. If z is set to 0 (or any
number less than 1000), the bootstrapping module is deactivated.
All four of these arguments
must be provided. Any cases that are system missing on any of the three
variables will be deleted from the mediation analysis (i.e., listwise
deletion), but they will remain in the active SPSS data file. If the user
desires any kind of imputation of missing values, imputation must be completed
prior to running the SOBEL command. The SPSS matrix language does not recognize
user-defined missing values, so any cases with user-defined missing values will
be treated as valid data.
There are no error checking
procedures in the macro, so the output should be examined carefully to make
sure there are no errors printed. The most likely causes of errors include
entering the command (or the original macro) incorrectly, using a variable that
is actually a constant in the data file, or requesting a bootstrapped estimate
when the original sample is very small. The latter error stems from the fact
that bootstrap resampling is done with replacement, and it is possible for a
variable resulting from a bootstrap sample to end up being a constant even
though none of the variables are actually constants. The minimum sample size
will depend on a number of factors, but in testing, the macro usually worked so
long as n was at least 25 or so. Depending upon processor speed and the
size of the sample, it may appear that SPSS has locked up or crashed once the
SOBEL command is executed. Be patient.
Because
bootstrapping is based on random sampling from the data set, each run of the
program will generate slightly different estimates of the indirect effect and
its standard error, and the upper and lower bounds of confidence intervals will
vary from run to run. The larger the number of bootstrap samples taken, the
less variable these estimates will be over consecutive runs of the program.
However, it is possible to replicate a set of bootstrap resamples by setting
the random number seed prior to executing the SOBEL
command. This is accomplished by preceding the SOBEL command
with the command SET
SEED seedval where seedval is a number between 1 and 2,000,000. If the same
seed and number of bootstrap samples are requested over multiple runs on the
same data, the output from those runs will be identical.
Instructions
for Use of SAS Macro
Right-click here to download the SAS syntax file
Click here
to download SAS data from Preacher and Hayes paper
The
procedures for using the SAS version of the macro are largely the same as for the
SPSS version. The user should first execute the SOBEL command set for SAS. This will activate a command called %sobel,
with syntax:
%sobel(data=file, y=dv, x=iv, m=med,
boot=z);
where file is the name of a SAS data file containing the
data to be analyzed, dv is the
name of the dependent variable in the data file, iv is the name of the
independent variable, med is the name of the proposed mediating
variable, and z specifies the number of bootstrap resamples desired. Except
for command format, usage is the same as for the SPSS version of the macro.
The macro will exclude all cases from the
analysis missing on any of the three variables, where missing is defined as the
period character ("."). There is no error checking in the macro, so
examine the log file carefully to look for errors. It will be obvious if an error occurs because
a line marked "ERROR" will appear in the SAS log file. The
same conditions described in Appendix A will produce errors in the SAS version
of the macro.
To
save the bootstrapped estimates of the indirect effect as a SAS data file for
later examination, the following commands should be added just before
the quiz command at the end of the macro:
create
filename from res [colname='indirect'];
append
from res;
where filename is any valid SAS file name.
As currently configured, the random
number generator will be seeded randomly. To set the seed, thus allowing you to
replicate a set of bootstrap samples, change the "0" in the line that
reads v = int(uniform(0)*n)+1 to any
positive integer less than 232 – 1.
DISCLAIMER: As with all
statistical software, all attempts are made to make sure that the computations
programmed into this macro are being performed correctly. When bugs are found and reported, I attempt
to eliminate them as quickly as possible.
I offer these macros to the research community “as is” and accept no
responsibility for any negative consequences that might result from their use. In other words, use at your own risk.