en Psicología ISSN Impreso: 0258-6444 ISSN electrónico: 2215-3535

Alternativas a las Pruebas Controladas Aleatorizadas: una revisión de tres diseños cuasi experimentales para la inferencia causal

Pavel Panko, Jacob Curtis, Brittany Gorrall, Todd Little



Los diseños de Pruebas Controladas Aleatorizadas (PCA) son típicamente vistas como el mejor diseño en la investigación en psicología. Como tal, no es siempre posible cumplir con las especificaciones de las PCA y por ello muchos estudios son realizados en un marco cuasi experimental. Aunque los diseños cuasi experimentales son considerados menos convenientes que los diseños PCA, con directrices estos pueden producir inferencias igualmente válidas. En este artículo presentamos tres diseños cuasi experimentales que son formas alternativas a los diseños PCA. Estos diseños son Regresión de Punto de Desplazamiento (RPD), Regresión Discontinua (RD), Pareamiento por Puntaje de Propensión (PPP). Adicionalmente, describimos varias mejorías metodológicas para usar con este tipo de diseños. 

Palabras clave

Psicometría; Diseños cuasi experimentales; Regresión de Punto de Desplazamiento; Regresión Discontinua; Pareamiento por Puntaje de Propensión.

Texto completo:

PDF (English) HTML (English)


Angrist, J., & Rokkanen, M. (2012). Wanna get away? RD identification away from the cutoff. Working Paper # 18662, National Bureau of Economic Research, Retrieved from w18662.

Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46, 399-424.

Battistin, E., & Rettore, E. (2008). Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs. Journal of Econometrics, 142, 715-730.

Braden, J. P. & Bryant, T. J. (1990). Regression discontinuity designs: Applications for school psychology. School Psychology Review, 19(2), 232-239.

Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22, 31- 72.

Coca-Perraillon, M. (2006). Matching with propensity scores to reduce bias in observational studies. Retrieved from da13.pdf

Coffman, D. (2012). Methodology workshop: Propensity score methods for estimating causality in the absence of random assignment: Applications for child care policy research. Presented at the annual meeting of the Child Care Policy Research Consortium, Bethesda, MD.

Cox, D.R. (1970). The analysis of binary data. London, UK: Methuen.

Cuong, N. V. (2013). Which covariates should be controlled in propensity score matching? Evidence from a simulation study. Statistica Neerlandica, 67, 169-180.

Dale, S. B., & Brown, R. S. (2007). How does cash and counseling affect costs? Health Services Research, 42, 488-509.

Greenwood, C. R., & Little, T. D. (2007). Use of regression discontinuity designs in special education research. Paper commissioned as one in a series of NCSER, IES papers devoted to special education research methodology topics. Hyattsville, MD: Optimal Solutions Group, LLC.

Hansen, B. B. (2004). Full matching in an observational study of coaching for the SAT. Journal of the American Statistical Association, 99, 609-618.

Institute of Education Sciences. (2009). Combined user’s manual for the ECLS-K eighth-grade and K-8 full sample data files and electronic codebooks. Washington, D.C.: National Center for Education Statistics, U.S. Department of Education.

Lalani, T., Cabell, C. H., Benjamin, D. K., Lasca, O., Naber, C., Fowler, V. G., & Wang, A. (2010). Analysis of the impact of early surgery on in- hospital mortality of native valve endocarditis use of propensity score and instrumental variable methods to adjust for treatment-selection bias. Circulation, 121, 1005-1013.

Lanza, S. T., Coffman, D. L., & Xu, S. (2013) Causal inference in latent class analysis, structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 20, 361-383.

Lanza, S. T., Moore, J. E., & Butera, N. M. (2013). Drawing causal inferences using propensity scores: A practical guide for community psychologists. American Journal of Community Psychology, 52, 380-392.

Linden, A., Trochim, W. K., & Adams, J. L. (2006). Evaluating program effectiveness using the regression point displacement design. Evaluation & the Health Professions, 29, 407-423.

Moss, B. G., Yeaton, W. H., & LIoyd, J. E. (2014). Evaluating the effectiveness of developmental mathematics by embedding a randomized experiment within a regression discontinuity design. Educational Evaluation and Policy Analysis, 36(2), 170-185.

Pellegrini, G., Terribile, F., Tarola, O., Muccigrosso, T., & Busillo, F. (2013). Measuring the effects of European regional policy on economic growth: A regression discontinuity approach. Papers in Regional Science, 92, 217-233.

Rosenbaum, P. and Rubin, D.B. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70(1), 4155.

Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33-38.

Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757-763.

Schulz, K. F., & Grimes, D. A. (2002). Allocation concealment in randomized trials: Defending against deciphering. The Lancet, 359, 614-618.

Shadish, W.R., Cook, T.D., & Campbell, D.T. (2002). Experimental and Quasi-experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin.

Slade, E.P., Stuart, E.A., Salkever, D.S., Karakus, M., Green, K.M., & Lalongo, N. (2008). Impacts of age of onset of substance use disorders on risk of adult incarceration among disadvantaged urban youth: Alternatives to RCT A propensity score matching approach. Drug and Alcohol Dependence, 95, 1-13.

Stuart, E. A., DuGoff, E., Abrams, M., Salkever, D., & Steinwachs, D. (2013). Estimating causal effects in observational studies using electronic health data: challenges and (some) solutions. Generating Evidence & Methods to improve patient outcomes (eGEMS), 1, 1-10.

Trochim, W.M.K. (1984). Research design for program evaluation: The regression-discontinuity approach. Beverly Hills, CA: Sage.

Trochim, W.M.K. (2006). The Research Methods Knowledge Base, 2nd Ed. Retrieved from http://

Trochim, W.M.K., & Campbell, D. T. (1996). The regression point displacement design for evaluating community-based pilot programs and demonstration projects. Unpublished manuscript. Retrieved from RPD/RPD.pdf

Trochim, W.M.K., & Campbell, D.T. (1999). Design for community-based demonstration projects. In D.T. and M.J. Russo (Eds.), Social experimentation. Thousand Oaks, CA: Sage.

Wing, C., & Cook, T.D. (2013). Strengthening the regression-discontinuity design using additional design elements: A within-study comparison. Journal of Policy Analysis and Management, 32, 853-877.

Enlaces refback

  • No hay ningún enlace refback.

© 2017 Universidad de Costa Rica. Para ver más detalles sobre la distribución de los artículos en este sitio visite el aviso legal. Este sitio es desarrollado por UCRIndex y Open Journal Systems.