Testing mediation using bootstrapping in SPSS
To fully understand the following procedures read these key papers
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium.
Communication Monographs, 76(4), 408-420. doi: 10.1080/03637750903310360
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.
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New
procedures and recommendations. Psychological Methods, 7(4), 422-445.
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison
of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1),
83-104.
This is an interactive PDF
- if you are viewing this on a computer connected to the internet: the hypertext links (blue
underlined) should take you to the relevant site. Access the relevant datafile by clicking on
the link
- if you are reading this off a hardcopy: Really??? How old school...well I guess you'll need to
boot up the old machine to run the analyses anyway so you may as well open this PDF on
screen and access the links (see instructions above)
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Testing mediation using bootstrapping in SPSS
The macros used in this tipsheet can be found at the following
website
http://www.afhayes.com/spss-sas-and-mplus-macros-and-code.html
Also see Hayes's Facebook page
(oh, as if you aren't on Facebook.....)
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Testing mediation using bootstrapping in SPSS
For this example we will use the Sobel Macro
Download the SPSS macro to your computer
You should also download the documentation (sobel.pdf) for additional information
Also note as stated on the website that another Macro "INDIRECT.SPS" will also perform the same
analysis unless you need to estimate a model with dichotomous outcome.
However, for this exercise we will use this macro.
Also note that this version of the macro does not work in SPSS ver 18 (unless you have all the
patches). The older version (sobel_SPSS) does work in ver 18 though.
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Testing mediation using bootstrapping in SPSS
Let's test this model
The dataset can be accessed HERE
Note the variable names that map onto the SPSS file
Also recall the mediation conventions (from Baron and Kenny, 1986)
Path a = IV to MedV
Path b = MedV to DV, controlling for the IV
(when multiplied together these are the INDIRECT effect)
Path c = IV to DV (aka the TOTAL effect)
Path c' = IV to DV, controlling for the MedV (aka the DIRECT effect)
Total effects = Indirect effect + Direct effect
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Testing mediation using bootstrapping in SPSS
1) Run the Macro (Sobel)
Macros are very similar to syntax files and are run as such:
Open the file via the "Open Syntax" option
Make sure to select the file "sobel.SPS"
This is the file you downloaded from the Hayes website
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Testing mediation using bootstrapping in SPSS
Run the macro
Select "Run...All"
This activates the macro (which runs in the background)
You can close the macro file if you wish - this will stay in the background until you either restart
SPSS or load a different macro
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Testing mediation using bootstrapping in SPSS
2) Set up your model in another syntax file
In this step you need to tell the macro which of your variables are the IV, DV and Mediator.
To do this you need to create a new syntax file and set up your model using this template:
SOBEL Y =DV/X = IV/M = MED/BOOT = 5000.
This is the syntax for the example model:
SOBEL Y =JOBSAT/X = POSAFFECT/M = WSE/BOOT = 5000.
Run this syntax
Note the "boot = 5000"
This is the part of the syntax that tells SPSS to draw 5000 sample indirect effects (with replacement)
using your sample data as the "population" of possible indirect effects
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The macro will now run to test your model
Note. This can take a little while as this is performing rather complex equations
To check if the program is still running you will see a little "Running MATRIX" in the bottom right
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Testing mediation using bootstrapping in SPSS
3. Interpret the output
By default the initial macro will be recorded in the output file (ignore this and scroll to the bottom of
the output file)
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Testing mediation using bootstrapping in SPSS
Direct and Total Effects output
This part of the output provides the significance tests of the:
1) "c" path (IV to DV, aka the Total effect) - in this example significant p = .0062
2) "a" path (IV to MedV) - in this example significant p =.0000
3) "b" path (MedV to DV, controlling for the IV) - in this example significant = .0000
4) "c' " path (IV to DV, controlling for the MedV, aka the Direct effect) - in this example non-significant
p = .5159
So, this model has met all the criteria for mediation according to Baron and Kenny (1986) - although
note that the IV-DV total effect criteria is no longer necessary (see earlier refs)
This coefficients are the Unstandardised B weights
In the latest macro the bivariate correlations are displayed but to obtain the Standardised Beta
weights for paths b and c' you would need to run a multiple regression in SPSS with the IV and
MedV as predictors of the DV and report the beta weights)
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Testing mediation using bootstrapping in SPSS
Indirect Effect (Sobel)
This section provides the test of the significance of the INDIRECT effect (path a X path b) using the
Sobel test
In this example path a = .7356 and path b = .3048
So the indirect effect = .2242 (see #1)
And is significant using the Sobel test (see #2)
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Testing mediation using bootstrapping in SPSS
Bootstrapped effect
Finally, the output provides the bootstrapped confidence intervals (99 and 95 percentiles are
calculated but we only want to look at the 95%)
Here we are looking to see if ZERO (0) lies within the interval range
Note that LL = Lower Limit (or the lower boundary) and UL = Upper Limit (or upper boundary) of the
Confidence interval.
Essentially we are asking whether it is possible (with 95% confidence) that the TRUE indirect effect
would be ZERO (basically, no mediation).
In this case the TRUE indirect effect is 95% likely to range from .1342 to .3232 - the estimated effect
is .2246 (lying in between these two values)
If (as in this case) zero does not occur between the LL and the UL then we can conclude that the
indirect effect is significant
An example of a non-significant indirect effect would be to have a LL of -.1342 and an UL of .3232
(since zero is a possible value between a negative number and a positive number)
For an example of reporting indirect effects using bootstrapping click here (note that this example
used AMOS but the reporting is similar)
Those wishing more information on this macro and/or mediation etc are advised to refer to the Hayes
website or the Facebook mediation website
Please note that the creator of this tipsheet is not associated with the developers of the macros and
is unable to provide additional advice on the macro or issues related with mediation or other
analyses.
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