**sgmediation2** help file

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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.