Abstract
Mediational analyses have been recognized as useful in answering two broad questions that arise in HIV/AIDS research, those of theoretical model testing and of the effectiveness of multicomponent interventions. This article serves as a primer for those wishing to use mediation techniques in their own research, with a specific focus on mediation applied in the context of path analysis within a structural equation modeling (SEM) framework. Mediational analyses and the SEM framework are reviewed at a general level, followed by a discussion of the techniques as applied to complex research designs, such as models with multiple mediators, multilevel or longitudinal data, categorical outcomes, and problematic data (e.g., missing data, nonnormally distributed variables). Issues of statistical power and of testing the significance of the mediated effect are also discussed. Concrete examples that include computer syntax and output are provided to demonstrate the application of these techniques to testing a theoretical model and to the evaluation of a multicomponent intervention.
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Notes
A key advantage of using latent variables instead of measured variables is that latent variables account for unreliability of measurement (Jaccard & Wan, 1995). When measures are unreliable, the regression coefficients/path coefficients are biased. For more information on tests of mediation within a latent variable framework, see Hoyle and Kenny (1999).
It is also possible to use the EM algorithm to obtain maximum likelihood estimates for missing data in EQS. This procedure makes the EQS programming slightly more complex, so in this example we chose to use listwise deletion as it keeps the programming language more simplified. For sample programs using the EM algorithm for missing data in EQS, we refer the reader to the EQS program manual (Bentler, 1995).
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Acknowledgement
The authors would like to thank Dr. David Kenny for serving as a reviewer on this paper. His contributions were invaluable. Preparation of this paper was supported by grants from the National Institute on Alcoholism and Alcohol Abuse (AA013844-01) and the National Institute on Drug Abuse (DA019139-01) awarded to the first author.
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Appendix
Appendix
Appendix A
EQS Syntax
/TITLE
Theory-testing model in EQS for Bryan, Schmiege, & Broaddus
/SPECIFICATIONS
DATA = ‘c:\eqs files\data\psex.ess’; VARIABLES = 6; CASES = 300;
METHOD = ML; ANALYSIS = COVARIANCE; MATRIX = RAW;
/LABELS
v1 = caseid; v2 = attitude; v3 = norms; v4 = selfeff; v5 = intent; v6 = behavior;
/EQUATIONS
v6 = *v4 + *v5 + *v2 + e6;
v5 = *v2 + *v3 + *v4 + e5;
/VARIANCES
e6 = *; e5 = *;
/COVARIANCES
v2,v3 = *; v2,v4 = *; v3,v4 = *;
/TEC
iter = 500;
fit = all;
correlations = yes;
effects = yes;
/WTEST;
/LMTEST;
/END
Appendix B
Mplus Syntax
Title: Intervention model for Bryan, Schmiege, & Broaddus;
Data:
File is c:\intex;
Format is 1f7.0 8f7.2;
Variable:
Names are
case condition attitude selfeff norms intent intalc t3intent behavior;
Usevariables are
condition attitude selfeff norms intent behavior;
Missing are all .;
Analysis:
Type = general missing h1;
Estimator is ML;
Model:
attitude on condition;
selfeff on condition;
norms on condition;
intent on attitude;
intent on selfeff;
intent on norms;
intent on condition;
attitude with selfeff;
attitude with norms;
selfeff with norms;
behavior on intent;
Model indirect:
intent ind attitude condition;
intent ind selfeff condition;
intent ind norms condition;
behavior ind intent selfeff condition;
Output:
Standardized Sampstat Residual Modindices(4) Patterns H1se Cinterval;
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Bryan, A., Schmiege, S.J. & Broaddus, M.R. Mediational Analysis in HIV/AIDS Research: Estimating Multivariate Path Analytic Models in a Structural Equation Modeling Framework. AIDS Behav 11, 365–383 (2007). https://doi.org/10.1007/s10461-006-9150-2
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DOI: https://doi.org/10.1007/s10461-006-9150-2