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help for sgmediation2
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Sobel-Goodman mediation tests
sgmediation2 depvar [if exp] [in range] , iv(focal_iv) mv(mediator_var) [options]
Description
Sobel-Goodman tests provide a statistical test of mediation in linear regression models. See
www.trentonmize.com/software/sgmediation2 for full details, summarized briefly below.
The commonly used approach to mediation based on Baron and Kenny (1986) suggests that a variable may be
considered a mediator to the extent to which it carries the influence of a focal independent variable
(IV) to a given dependent variable (DV). In this framework, mediation can be said to occur when (1) the
IV significantly affects the mediator, (2) the IV significantly affects the DV in the absence of the
mediator, (3) the mediator has a significant unique effect on the DV, and (4) the effect of the IV on
the DV shrinks upon the addition of the mediator to the model.
Others (e.g. Preacher and Hayes 2004) suggest that only two requirements need be met: (1) the IV has a
significant effect before the mediator is added to the model, and (2) the effect of the IV shrinks upon
the addition of the mediator to the model (i.e. same requirement as #4 above). Simplifying even
further, many now suggest (e.g. Zhao, Lynch, and Chen 2010) that the only needed requirement is that
the effect of the IV shrinks upon the addition of the mediator to the model (AKA there is a significant
indirect effect; see below for details) because mediation can occur even in the absence of a direct
effect of the IV.
sgmediation2 provides tests of all of the various requirements discussed above to facilitate most any
test desired. I personally agree that the test that the effect of the IV shrinks upon the addition of
the mediator to the model (i.e. the indirect effect) is of most central interest. But as Zhao et al.
(2010) detail -- the individual tests outlined by Baron and Kenny (1986) are still quite useful to
determine the specific nature of mediation found.
Some limitations of this general approach to mediation are discussed below along with one alternative
approach.
Required options
iv(var) The focal independent variable (IV). Factor syntax is not allowed on the focal IV. This
limits the focal IV to continuous or binary variables.
mv(var) The mediator variable (MV). Factor syntax is not allowed on the mediator variable. This
limits the mediator to continuous or binary variables.
Optional options
cv(varlist) Optional list of covariate (control) variables. Factor variables are allowed in the list.
prefix( ) Allows the user to specify survey weights and/or multiple imputation estimates by
requesting the relevant prefix you would use with regress. Specify svy: for the survey
weights defined by svyset to be used. Specify mi est: for multiple imputation estimates to
be used as defined in mi set. Specify mi est: svy: for both survey weights and multiple
imputation estimates as defined in mi svyset.
vce( ) Allows the user to specify a variance estimator other than the default ols (see regress for
options). For example, users may wish to specify robust for robust variance estimates or
cluster clustvar for cluster robust variance estimates.
options( ) Allows the user to specify any other options that are allowed with regress.
quietly Suppresses the individual regression output and only shows the summary tables.
decimals(#) changes the number of decimal places reported in the final tables of statistics. The
default is 3. Any integer between 1 - 8 is allowed.
Examples
use "https://tdmize.github.io/data/data/cda_ah4"
drop if missing(health, edyrs, income, race, woman, age)
sgmediation2 health, iv(edyrs) mv(income)
*Add control variables
sgmediation2 health, iv(edyrs) mv(income) cv(i.race i.woman age)
*Add survey weights already set with svyset
sgmediation2 health, iv(edyrs) mv(income) cv(i.race i.woman age) prefix(svy:)
*Obtain cluster robust variance estimates for clustering on occcat
sgmediation2 health, iv(edyrs) mv(income) cv(i.race i.woman age) vce(cluster occcat)
*Use bootstrapping to obtain standard errors and confidence intervals
bootstrap r(ind_eff) r(dir_eff) r(tot_eff), reps(1000): sgmediation2 health, iv(edyrs) mv(income)
cv(i.race i.woman age)
*Obtain bias-corrected and percentile confidence intervals based on the bootstrapped samples
estat bootstrap, bc percentile
Author
sgmediation2 is an adaptation (with permission) of the sgmediation command. sgmediation2 is written and
maintained by Trenton D. Mize. Please send any requests for help or suggestions for additions to the
command to tmize@purdue.edu
The original sgmediation command was written by Phil Ender of the UCLA Statistical Consulting Group.
Stored results
sgmediation2 returns the table of Sobel-Goodman tests, the tests of effects, and several scalars as:
r(sgtests) Matrix of the table of Sobel-Goodman tests of mediation.
r(effects) Matrix of the table of indirect, direct, and total effects.
r(ar_zstat) z-statistic on Aroian test.
r(g_zstat) z-statistic on Goodman test.
r(s_zstat) z-statistic on Sobel test.
r(tot2dir) Ratio of total to direct effect.
r(ind2dir) Ratio of indirect to direct effect.
r(ind2tot) Ratio of indirect to total effect.
r(b_coef) Coefficient on b path.
r(a_coef) Coefficient on a path.
r(tot_eff) Total effect.
r(dir_eff) Direct effect.
r(ind_eff) Indirect effect (a X b)
Limitations of the Sobel-Goodman approach to mediation
There are many limitations to this approach to mediation (more than I discuss here). A few of note:
1. Only continuous or binary focal independent variables (IV) can be examined.
2. Only continuous or binary mediating variables (MV) can be examined.
3. Multiple mediating variables (MVs) cannot be easily incorporated.
4. Limited to tests of a single coefficient. E.g. There is no clear way to test if the effect of age is
mediated if both age and age^2 coefficients are included in the models.
5. Limited to linear regression models.
6. A specialized approach appropriate only for mediation and not other cross-model comparisons.
These limitations (and some others) were the motivation of my article A General Framework for Comparing
Predictions and Marginal Effects (Mize, Doan, and Long 2019). See that article and the associated Stata
files if you are interested.
References
Aroian, L. A. (1944). The probability function of the product of two normally distributed variables.
Annals of Mathematical Statistics, 18, 265-271.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations. Journal of
Personality and Social Psychology, 51(6), 1173.
Goodman, L. A. (1960). On the exact variance of products. Journal of the American Statistical
Association, 55, 708–713.
MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation
Review, 17, 144-158.
MacKinnon, D. P., Warsi, G., & Dwyer, J. H. (1995). A simulation study of mediated effect measures.
Multivariate Behavioral Research, 30(1), 41-62.
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.
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in
simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717-731.
Mize, T. D., Doan, L., & Long, J. S. (2019). A general framework for comparing predictions and marginal
effects across models. Sociological Methodology, 49(1), 152-189.
Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about
mediation analysis. Journal of Consumer Research, 37(2), 197-206.