Classical and causal inference approaches to statistical mediation analysis

  1. Ato García, Manuel
  2. Vallejo Seco, Guillermo 1
  3. Ato Lozano, Ester
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Psicothema

ISSN: 0214-9915

Año de publicación: 2014

Volumen: 26

Número: 2

Páginas: 252-259

Tipo: Artículo

Otras publicaciones en: Psicothema

Resumen

Antecedentes: aunque existe un amplio consenso en el uso de los procedimientos estadísticos para el análisis de la mediación en la investigación psicológica, la interpretación del efecto de mediación resulta muy controvertida debido al potencial incumplimiento de los supuestos que requiere su aplicación, la mayoría de los cuales son ignorados en la práctica. Método: se resumen los procedimientos actualmente vigentes para el análisis de mediación desde los enfoques clásico y de la inferencia causal, junto con los supuestos estadísticos para estimar efectos de mediación no sesgados, en particular la existencia de variables omitidas o confundidores, y se utiliza un estudio de simulación para determinar si la violación de los supuestos puede cambiar la estimación del efecto de mediación. Resultados: el estudio de simulación mostró una sobreestimación importante del efecto de mediación en presencia de confundidores latentes. Conclusiones: se recomienda complementar el enfoque clásico con el enfoque de la inferencia causal, que generaliza los resultados del primer enfoque al análisis de la mediación e incorpora nuevas herramientas para evaluar sus supuestos estadísticos. Para alcanzar tal objetivo se comparan las características distintivas de los programas de software recientemente desarrollados en R, SAS, SPSS y Mplus.

Referencias bibliográficas

  • Anderson, S., & Hunter, S.C. (2012). Cognitive appraisals, emotional reactions and their associations with three forms of peer victimization. Psicothema,24, 621-627.
  • Angrist, J.D., Imbens, G.W., & Rubin, D.B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association,91, 444-455.
  • Ato, M., & Vallejo, G. (2011). Los efectos de terceras variables en la investigación psicológica. Anales de Psicología, 27, 550-561.
  • Baron, J., & 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, 1173-1182.
  • Bullock, J.G., & Ha, S.E. (2011). Mediation analysis is harder than it looks. In J.N. Drukman, D.P. Green, J.H. Kuklinski & A. Lupia (Eds.), Cambridge Handbook of Experimental Political Science (pp. 508-521). New York, NY: Cambridge University Press.
  • Bullock, J.G., Green, D.P., & Ha, S.E. (2010). Yes, but what's the mechanism? (don't expect an easy answer). Journal of Personality and Social Psychology,98, 550-558.
  • Cava, M.J., Musitu, G., & Murgui, S. (2006). Familia y violencia escolar: el rol mediador de la autoestima y la actitud hacia la autoridad institucional [Family and school violence: The mediator role of self-esteem and attitudes towards institutional authority]. Psicothema, 18, 367-373.
  • Coffman, D.L. (2011). Estimating causal effects in mediation analysis using propensity scores. Structural Equation Modeling, 18, 357-369.
  • Cole, D.A., & Maxwell, S.E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology,112, 558-577.
  • García, J.F., Pascual, J., Frías, M.D., Van Krunckelsven, D., & Murgui, S. (2008). Diseño y análisis de la potencia: n y los intervalos de confianza de las medias [Design and power analysis: n and confidence intervals of means]. Psicothema, 20, 933-938.
  • Hayes, A.F. (2013). Introduction to mediation, moderation and conditional process analysis: A regression-based approach. New York, NY: The Guilford Press.
  • Hayes, A.F., & Scharkow, M. (2013). The relative trustworthiness of tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science,24, 1918-1927.
  • Hicks, R., & Tingley, D. (2011). Causal mediation analysis. The Stata Journal, 11, 1-15.
  • Holland, P.W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945-960.
  • Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 1, 51-71.
  • Imai, K., Keele, L., & Tingley. D. (2010). A general approach to causal mediation analysis. Psychological Methods,15, 309-344.
  • Imai, K., Keele, L., & Yamamoto, T. (2013). Experimental designs for identifying causal mechanisms. Journal of the Royal Statistical Society, 176, Part I, 5-51.
  • Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2011). Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review, 105, 765-789.
  • Jo, B., Stuart, E.A., MacKinnon, D.P., & Vinokur, A.D. (2011). The use of propensity scores in mediation analysis. Multivariate Behavioral Resarch, 46, 425-452.
  • Judd, C.M., & Kenny, D.A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review,5, 602-619.
  • MacKinnon, D.P. (2007). Introduction to mediation analysis. Mahwah, NJ: Erlbaum.
  • MacKinnon, D.P., Cheong, J., & Pirlott, A.G. (2012). Statistical mediation analysis. In H. Cooper, P.M. Camic, D. Long, A.T. Panter, D. Rindskopf, & K.J. Sher (Eds.). APA Handbook of Research Methods in Psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological (pp. 313-331). Washington, DC: American Psychological Association.
  • MacKinnon, D.P. (2008). Introduction to statistical mediation analysis. New York, NY: Lawrence Erlbaum.
  • MacKinnon, D.P., Lockwood, C.M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99-128.
  • Maxwell, S.E., & Cole, D.A. (2007). Bias in cross-sectional analysis of longitudinal mediation. Psychological Methods,12, 23-44.
  • Maxwell, S.E., Cole, D.A., & Mitchell, M.A. (2011). Bias in cross-sectional analysis of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research,46, 816-841.
  • Muthen, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus. Unpublished Mplus paper. URL: http://www.statmodel.com/download/causalmediation.pdf. Accessed: 2013-06-21 (Archived by WebCite® at http://www.webcitation. org/6LIEfxWaJ).
  • Pardo, A., & Román, M. (2013). Reflections on the Baron and Kenny model of statistical mediation. Anales de Psicología,29, 614-623.
  • Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys,3, 96-146.
  • Pearl, J. (2010). An introduction to causal inference. The International Journal of Biostatistics, 6(2), Article 7 (DOI: 10.2202/1557-4679. 1203).
  • Pearl, J. (2012). The mediation formula: A guide to the assessment of pathways and mechanisms. Prevention Science, 13, 426-436.
  • Preacher, K.J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93-115.
  • Preacher, K.J., & Selig, J.P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures,6, 77-98.
  • Robins, J.M., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3, 143-155.
  • Roe, R.A. (2012). What is wrong with mediators and moderators? The European Health Psychologist, 14, 4-10.
  • Rubin, D.B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322-331.
  • Sánchez-Manzanares, M., Rico, R., Gil, F., & San Martín, R. (2006). Memoria transactiva en equipos de toma de decisiones: implicaciones para la efectividad de equipo [Transactive memory in decision-making teams: Implications for team effectiveness]. Psicothema, 18, 750-756.
  • Shadish, W.R. (2010). Campbell and Rubin: A primer and comparison of their approaches to causal inference in field settings. Psychological Methods, 15, 3-17.
  • Sobel, M.E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 12, 290-312.
  • Spencer, S.J., Zanna, M.P., & Fong, G.T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89, 845-851.
  • Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2013). Mediation: R package for causal mediation analysis. Journal of Statistical Sofware (in press).
  • Valeri, L. (2012). Statistical methods for causal mediation analysis. Doctoral dissertation. Cambridge, MA: Harvard University DASH Repository.
  • Valeri, L., & VanderWeele, T.J. (2013). Mediation analysis allowing for exposure-mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods,18, 137-150.
  • Vallejo, G., Ato, M., Fernández, P., & Livacic-Rojas, P. (2013). Multilevel bootstrap analysis with assumptions violated. Psicothema, 25, 520-528.
  • VanderWeele, T.J., Valeri, L., & Ogburn, E.L. (2012). The role of measurement error and misclassification in mediation analysis. Epidemiology, 23, 561-564.
  • Zhao, X., Lynch, J.G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research,37, 197-206.