Fichero Adicional 5. Referencias bibliográficas seleccionadas específicas

Exponemos referencias bibliográficas seleccionadas sobre aspectos específicos de la investigación Cx. (y otras investigaciones no aleatorizadas, en adelante, y otras I.NA. tb.).

Los tres primeros apartados hacen referencia al análisis de los datos para los diseños Cx. de Grupo control No Equivalente, de Discontinuidad en la Regresión y de Series Temporales respectivamente.

El cuarto apartado re refiere a la detección, estimación y control de los efectos de mediación, efectos indirectos, variables supresoras, etc, todos ellos aspectos comunes a todas las investigaciones Cx (y otras I.NA. tb.).

El quinto apartado se dedica al problema de la regresión a la media. A continuación dos apartados más de referencias en relación a dos de los

grandes problemas de los diseños Cx. (y otras I.NA. tb.), sesgo de selección (concepto, control, prevención, cómo evitarlo, etc.), y datos faltantes: abandono de sujetos, no respuesta, pérdida de datos (concepto, control, prevención, cómo evitarlo, estimación de datos, etc.). Seguido de de estos dos se exponen referencias relativas al análisis de los datos por intención de tratar y por protocolo.

Finalmente se exponen referencias bibliográficas relativas a la aplicación y evaluación de programas de intervención, a la Metodología mixta, y referencias relativas a las dos técnicas de análisis de datos más potentes y ambiciosas de cuantas existen, Modelos Multinivel y Modelos de Ecuaciones estructurales

Elenco:1.- Análisis de datos: Diseños de Grupo Control no equivalente. p.1 2.- Análisis de datos: Diseños de Discontinuidad en la Regresión. p.6 3.- Análisis de datos: Diseños de Series Temporales. p.10 4.- Mediación, efectos indirectos, variables supresoras, etc. p.15 5.- El problema de la regresión a la media. p.22 6.- Sesgo de selección (concepto, control, prevención, cómo evitarlo, etc.). p.23 7.- Datos faltantes: abandono de sujetos, no respuesta, pérdida de datos (concepto, control, prevención, cómo evitarlo, estimación de datos, etc.). p.29 8.- Análisis de los datos por intención de tratar y por protocolo. p.33 9.- Aplicación y Evaluación de programas de intervención. p.35 10.- Metodología Mixta. p.37 11.- Modelos Multinivel o Modelos jerárquicos. p.39 12.- Modelos de Ecuaciones Estructurales. p.42

Referencias:

1.- Análisis de datos: Diseños de Grupo Control no equivalente.

Altman, D.G. (2006). Practical statistics for medical research. New York: Chapman & Hall.Allison, P.D. (1990). Change scores as dependent variables in regression analysis. Sociological

Methodology, 20, 93-114. Anderson, S., Auquier, A., Hauck, W., Oakes, D., Vandaele, W., y Weisberg, H., (1980).

Statistical methods for comparative studies. New York: John Wiley & Sons.

Assmann, S.F., Pocock, S.J., Enos, L.E., y Kasten, L.E. (2000). Subgroup analysis and other (mis)uses of baseline data in clinical trials. The Lancet, 355, 1064-1069.

Ato, M. (1995). Análisis estadístico I: Diseños con variables de asignación no conocida. En M.T. Anguera, J. Arnau, M. Ato, R. Martínez, J. Pascual, J., y G. Vallejo, G. (Eds.). (1995). Métodos de investigación en psicología (Cap. 10). Madrid: Síntesis.

Ato, M. (1995). Análisis estadístico II: Diseños con variables de asignación conocida. En M.T. Anguera, J. Arnau, M. Ato, R. Martínez, J. Pascual, J., y G. Vallejo, G. (Eds.). (1995). Métodos de investigación en psicología (Cap. 11). Madrid: Síntesis.

Ato, M. y Vallejo, G. (2007). Diseños Experimentales en Psicología. Madrid: Pirámide.Berger, M., Mamdani, M., Atkins, D., y Johnson, M.L. (2009). Good research practices for

comparative effectiveness research: defining, reporting and interpreting non-randomized studies of treatment effects using secondary data sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part I. Value in Health, 12(8), 1044-1052.e_601

Bray, J.W., Schlenger, W.E., Zarkin, G.A., y Galvin, D. (2008). Analyzing Data from Nonrandomized Group Studies. RTI Press publication No. MR-0008-0811. Research Triangle Park, NC: RTI International. Disponible online en http://www.rti.org/pubs/mr-0008-0811-bray.pdf.

Cohen, J., Cohen, P., West, S.G., y Aiken, L.S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

Colditz, G.A. (2010). Overview of the epidemiology methods and applications: trengths and limitations of observational study designs. Critical Reviews in Food Science and Nutrition, 50 (Suppl 1), 10-2.

Collins, L.M. (1996). Is reliability obsolete? A commentary on 'Are simple gain scores obsolete?' Applied Psychological Measurement, 20, 289-292.

Cousens. S., Hargreaves, J., Bonell, C., Armstrong, B., Thomas, J., Kirkwood, B.R., y Hayes, R. (2011). Alternatives to randomisation in the evaluation of public-health interventions: statistical analysis and causal inference. Jounal of Epidemiology & Community Health, 65,576-581.

Cribbie, R.A., y Jamieson, J. (2000). Structural equation models and the regression bias for measuring correlates of change. Educational and Psychological Measurement, 60(6), 893-907.

Cribbie, R.A., y Jamieson, J. (2004). Decreases in posttest variance and the measurement of change. Methods of Psychological Research Online, 9(1), 37-55.

Cronbach, J.L., y Furby, L. (1970) How should we measure 'change'-or should we? Psychological Bulletin, 74(1), 68-80.

Dimitrov, D.M., y Rumrill, P.D. (2003). Pretest-posttest designs and measurement of Change. Work 20, 159-165.

Edwards, J.R. (1994). Regression analysis as an alternative to difference scores. Journal of Management, 20(3), 683-689.

Ferrando, J.P., y Angiano, C. (2011). Evaluación de las diferencias individuales en falseamiento. Psicothema, 23(4), 839-844.

Fitzmaurice, G. (2001). A conundrum in the analysis of change. Nutrition, 17(4), 360-361. Gottman, J.M., y Rushe, R.H. (1993). The analysis of change: Issues, fallacies, and new ideas.

Journal of Consulting and Clinical Psychology, 61(6), 907-910. Gupta, J.K., Srivastava, A.B.L., y Sharma, K.K. (1988). On the optimum predictive potential of

change meaures. Journal of Experimental Education, 56, 124-128.

Heckman, J. (1997). Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. The Journal of Human Resources, 32(3), 441-462.

Heckman J.J., y Hotz, V.J. (1989). Choosing among alternative nonexperimental methods for estimating the impact of social programs: The case of manpower training. Journal of the American Statistical Association, 84(408), 862-880.

Heckman, J.J., y Robb, R. (1985). Alternative methods for evaluating the impact of interventions: An overview. Journal of Economics, 30, 239-267.

Hendrix, L.J., Carter, M.W., y Hintze, J.L. (1979). A comparison of five statistical methods for analyzing pretest-posttest designs. Journal of Experimental Education, 47(2), 96-102.

Hiutema, B.E. (1980). The analysis of covariance and alternatives. New York: Wiley. Huitema, B.E. (2011). The Analysis of Covariance and Alternatives: Statistical Methods for

Experiments, Quasi-Experiments, and Single-Case Studies (2da. Ed.). New York: John Wiley.

Holland, P. 1986. Statistics and causal inference. Journal of the American Statistical Association. 81, 396, 945-960.

Holland, P.W., y Rubin, D.B. (1983). On Lord's Paradox. In H. Wainer & S. Messick (Eds.), Principles of modern psychological measurement (pp. 3-35). Hillsdale, NJ: Lawrence Erlbaum.

Holland, P., y Rubin, D. 1988. Causal inference in retrospective studies. Evaluation Review, 12, 203–231.

Hsiao, C. (2003). Analysis of panel data (2 nd. Ed.). Cambridge, MA: Cambridge University Press. Disponible online en http://bilder.buecher.de/zusatz/22/22465/22465755_lese_1.pdf.

Huck, S.W., y McLean, R.A. (1975). Using a repeated measures ANOVA to analyze the data from a pretest-posttest design: A potentially confusing task. Psychological Bulletin, 82(4), 511-518.

Jamieson, J. (1993). The law of initial values: Five factors or two? International Journal of Psychophysiology, 14, 233-239.

Jamieson, J. (1994). Correlates of reactivity: Problems with regression based methods. International Journal of Psychophysiology, 17, 73-78.

Jamieson, J. (1999). Dealing with baseline differences: Two principles and two dilemmas. International Journal of Psychophysiology, 31(2), 155-161.

Jamieson, J., y Howk, S. (1992). The law of initial values: A four factor theory. International Journal of Psychophysiology, 12, 53-61.

Johnson, M.L., Crown, W., Martin, B.C., Dormuth, C.R., y Siebert, U. (2009). Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report--Part III. Value Health, 12(8), 1062-1073.

Judd, C.M., y Kenny, D.A. (1981). Estimating the effect of social interventions. New York: Cambridge University Press.

Kenny. D.A. (1975). A quasi-experimental approach to assessing treatment effects in the nonequivalent control group design. Psychological Bulletin, 82, 345-362.

Keppel, G., y Zedeck, S. (1989). Data analysis for research designs: Analysis of variance and multiple regression/correlation approaches. New Yort: Freedman & Company.

Kim, H-J., Fay, M.P., Feuer, E.J., y Midthune, D.N. (2000). Permutation tests for joinpoint. Regression with applications to cancer rates. Statistics in Medicine, 19, 335-51.

Kutner, M.H., Nachtsheim,C., y Neter, J. (2004). Applied linear regression models (4th ed.). Boston: McGraw-Hill/Irwin.

Lai, K., y Kelley, K. (2012). Accuracy in parameter estimation for ANCOVA and ANOVA contrasts: Sample size planning via narrow confidence intervals. British Journal of Mathematical and Statistical Psychology, 65, 350–370.

Larzelere, R.E., Kuhn, B.R., y Johnson, B. (2004). The intervention selection bias: An underrecognized confound in intervention research. Psychological Bulletin, 130(2), 289-303.

Lin, H.M. y Hughes, M.D. (1997). Adjusting for regression toward the mean when variables are normally distributed. Statistical Methods in Medical Research, 6(2), 129-46.

Linden, A., Trochim, W.M.K., y Adams, J. L. (2006). Evaluating program effectiveness using the regression point displacement design. Evaluation and The Health Professions, 29(4), 407-423.

Linn, R.L., y Slinde, J.A. (1977). The determination of the significance of change between pre- and post-testing periods. Review of Educational Research, 47, 121-150.

Linn, R.L., y Werst, C.E. (1977). Analysis implications of the choice of a structural model in the nonequivalent control group design. Psychological Bulletin, 84(2), 229-234.

Lord, F.M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68(5), 304-305.

MacLehose, R.R., Reeves, B.C., Harvey, I.M., Sheldon, T.A., Russell, I.T., y Black, A.M. (2000). A systematic review of comparisons of effect sizes derived from randomised and non-randomised studies. Health Technology Assessment, 4, 1-154.

Mara, C., Cribbie, R.A., Flora, D., LaBrish, C., Mills, L., y Fiksenbaum, L. (2012). An improved model for evaluating change in randomized pretest, posttest, follow-up designs. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 8(3), 97-103.

Maris, E. (1998). Covariance adjustment versus gain scores—revisited. Psychological Methods, 3, 309-327.

Maxwell, S.E., y Delaney, H.D. (1990). Designing experiments and analyzing data: A model comparison perspective. Pacific Grove, CA: Brooks/Cole.

Milsap, R.E., y Hartog, S.B. (1988). Alpha, beta, and gamma change in evaluation research: s structural equation approach. Journal of Applied Psychology, 73(3), 574-584.

Morris, S.B. (2008). Estimating effect sizes from pretest-posttest-control group designs. Organizational Research Methods, 11, 364-386.

Murray, D.M. (2001). Statistical models appropriate for designs often used in group-randomized trials. Statistics in Medicine, 20, 1373-1385.

Oakes, J.M., y Feldman, H.A. (2001). Statistical power for nonequivalent pretest-posttest designs: The impact of change-score versus ANCOVA models. Evaluation Review, 25(1), 3-28.

Pedhazur, E.J. (1982). Multiple regression in behavioral research (2nd ed.). San Francisco: Holt, Rinehart, & Winston.

Pedhazur, E.J., y Schmelkin, L.P. (1991). Measurement, design, and analysis. Hillsdale, NJ: Lawrence Erlbaum

Petscher, Y. (2010). A simulation study on the performance of the simple difference and covariance adjusted scores in randomized experimental designs. Dissertation Abstracts International: Section B. Sciences and Engineering, 70(8-B), 5209.

Pohl, S., Steiner, P.M., Eisermann, J., Soellner, R., y Cook, T.D. (2009). Unbiased causal inference from an observational study: Results of a within-study comparison. Educational Evaluation and Policy Analysis, 31(4), 463-479.

Raudenbush, S.W., Martinez, A., y Spybrook, J. (2007). Strategies for Improving Precision in Group-Randomized Experiments. Educational Evaluation and Policy Analysis, 29(1), 5-29.

Rausch, J.R., Maxwell, S.E., y Kelley, K. (2003). Analytic Methods for Questions Pertaining to a Randomized Pretest, Posttest, Follow-Up Design. Journal of Clinical Child and Adolescent Psychology, 32 (3), 467- 486.

Rogosa, D.R., y Willett, J.B. (1983). Demonstrating the reliability of the difference score in the measurement of change. Journal of Educational Measurement, 20, 335-343.

Rosenbaum, P.R. (2002). Covariance adjustment in randomized experiments and observational studies. Statistical Science, 17(3), 286-327.

Rubin, D.B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688-701.

Rubin, D.B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics, 2(1), 1-26.

Rubin, D.B. (1990). Formal modes of statistical inference for causal effects. Journal of Statistical Planning and Inference, 25, 279-292.

Rubin, D.B. (1991). Practical implications of models of statistical inference for causal effects and the critical role of random assignment. Biometrics, 47, 1213-1234.

Schafer, W.D. (1992). Analysis of pretest-posttest designs. Measurement and Evaluation in Counselling and Development, 25, 2-4.

Schlotter, M., Schwerdt, G., y Woessmann, L. (2011). Econometric methods for causal evaluation of education policies and practices: a non-technical guide. Education Economics, 19(2), 109-137.

Shadish, W.R., Clark, M.H., y Steiner, P.M. (2008). Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random to Nonrandom Assignment. Journal of the American Statistical Association, 103, 1334-1343.

Shardell, M., Harris, A.D., El-Kamary, S.S., Furuno, J.P., Miller, R.R., y Perencevich, E.N. (2007). Statistical Analysis and Application of Quasi Experiments to Antimicrobial Resistance Intervention Studies. Clinical Infectious Diseases. 45(1), 901-907.

Singer, J.D., y Willett, J.B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrences. Oxford, UK.: Oxfird University Press.

Snyder, P.; Thompson, B.; Mclean, M.E., y Smith, B.J. (2002). Examination of Quantitative Methods Used in Early Intervention Research: Linkages With Recommended Practices. Journal of Early Intervention, 25(2), 137-150.

Spiegelman, C. (1977). A technique for analyzing a pretest-posttest nonrandomized field experiment. Tallahassee, FL: Florida State University.

Spirtes, P., Glymour, C., y Scheines, R. (2000). Causation, Prediction, and Search. MIT Press, Cambridge.

Steyer, R. (2005). Analyzing individual and average causal effects via structural equation models. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 11(1), 39-54.

Thompson, B., Diamond, K.E., McWilliam, R., Snyder, P., y Snyder, S.W. (2005). Evaluating the quality of evidence from correlational research for evidence-based practice. Exceptional Children, 71(2), 181-194.

Tisak, J., y Smith, C.S. (1994). Defending and extending difference score methods. Journal of Management, 20(3), 675-682.

van Belle, G. (2008). Statistical rules of thumb (2nd ed.). New York: John Wiley & Sons. Van Breukelen, G.J.P. (2006). ANCOVA versus change from baseline: More power in

randomized studies, more bias in nonrandomized studies. Journal of Clinical Epidemiology, 59(9), 920-925.

Varnell, S., Murray, D.M., y Baker, W.L. (2001). An evaluation of analysis options for the one-group-per-condition design: Can any of the alternatives overcome the problems inherent in this design? Evaluation Review, 25, 440-453.

Venter, A., Maxwell, S.E., y Bolig, E. (2002). Power in randomized group comparisons: The value of adding a single intermediate time point to a traditional pretest-posttest design. Psychological Methods, 7(2), 194-209.

Vooijs, M.W. (1991). Linear versus nonlinear analysis in the measurement of effects in a quasi-experimental design. Evaluation Review, 15(5), 625-638.

Wainer, H. (1991). Adjusting for differential base rates: Lord's paradox again. Psychological Bulletin, 109, 147-151.

Wagner, A.K., Soumerai, S.B., Zhang, F., y Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27, 299-309.

West, S.G., Duan, N., Pequegnat, W., Gaist, P., Des Jarlais, D.C., Holtgrave, D., Szapocznik, J., Fishbein, M., Rapkin, B., Clatts, M., y Mullen, P.D. (2008). Alternatives to the Randomized Controlled Trial. American Journal of Public Health, 98 (8), 1359-1366.

Willett, J. B. (1997). Measuring change: What individual growth modeling buys you. In E. Amsel and K. A. Renninger (Eds.), Change and development: Issues of theory, method, and application (pp. 213-243). Mahwah, NJ: Lawrence Erlbaum Associates.

Williams, M., y Vogt, P. (Eds.) (2011). The SAGE handbook of Innovation in Social Research Methods. London: SAGE.

Williams, R.H., y Zimmerman, D.W. (1996). Are simple gain scores obsolete? Applied Psychological Measurement, 20, 59-69.

Yanez, N.D., Kronmal, R.D., y Shemanski, L.R. (1998). The effects of measurement error in response variables and tests of association of explanatory variables in change models. Statistics in Medicine, 17, 2597-2606.

Yin, P., y Brennan, R.L. (2002). An investigation of difference scores for a grade-level testing program. International Journal of Testing, 2(2), 83-105.

Zimmerman, D.W., y Williams, R.H. (1982). Gain scores in research can be highly reliable. Journal of Educational Measurement, 19(2), 149-154.

2.- Análisis de datos: Diseños de Discontinuidad en la Regresión.

Aiken, L.S., West, S.G., Schwalm, D.E., Carroll, J., y Hsuing, S. (1998). Comparison of a randomized and two quasi-experimental designs in a single outcome evaluation: Efficacy of a university-level remedial writing program. Evaluation Review, 22(4), 207-244.

Bloom, H.S. (2008). Using regression discontinuity analysis. Disponible online en http://www.youtube.com/watch?v=zVKhvpDjct8

Bloom, H.S. (2009). Modern Regression Discontinuity Analysis. MDRC Working Papers on Research Methodology. Disponible online en http://www.mdrc.org/publications/539/abstract.html

Bloom, H. S. (2012). Modern regression discontinuity analysis. Journal of Research on Educational Effectiveness, 5(1), 43-82.

Bloom, H.S., Kemple, J., Gamse, B., y Jacob, R. (2005). Using regression discontinuity analysis to measure the impacts of reading first. Paper presented at the annual conference of the American Educational Research Association, Montreal, Canada. Disponible online en http://www.columbia.edu/cu/ssw/faculty/famdem/bloomdoc.pdf.

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

Bryant, D.P., Bryant, B.R., Gersten, R., Scammacca, N., y Chavez, M. M. (2008). Mathematics intervention for first- and second-grade students with mathematics difficulties. Remedial and Special Education, 29(1), 20-32.

Cappelleri, J.C., Darlington, R.B., y Trochim, W.M.K. (1994). Power analysis of cutoff-based randomized clinical trials. Evaluation Review, 18, 141-152.

Cappelleri, J.C., y Trochim, J.C. (1994). An illustrative statistical analysis of cutoff-based randomized clinical trials. Journal of Clinical Epidemiology, 47, 261-270.

Cappelleri, J.C., y Trochim, W.M.K. (1995). Ethical and scientific features of cutoff-based designs of clinical trials. Medical Decision Making, 15(4), 387-394.

Cappelleri, J.C., Trochim, W.M.K., Stanley, T.D., y Reichardt, C.S. (1991). Random measurement error does not bias the treatment effect estimate in the regression-discontinuity design: I. The case of no interaction. Evaluation Review, 15(4), 395-419.

Cook, T.D. (2008). "Waiting for life to arrive": A history of the regression-discontinuity design in psychology, statistics and economics. Journal of Econometrics, 142(2), 636-654.

Cook, T.D., y Gorard, S. (2007). Where does good evidence come from? International Journal of Research and Method in Education, 30(3), 307-323.

Cook, T.D., Shadish, W.J., y Wong, V.C. (2008). Three conditions under which observational studies produce the same results as experiments. Journal of Policy Analysis and Management, 27(4), 724-750.

Cook, T.D., y Steiner, P.M. (2009). Some empirically viable alternatives to the randomized experiment. Journal of Policy Analysis and Management, 28(1), 165-166.

Cook, T.D., y Wong, V.C. (2007). The warrant for universal pre-K: Can several thin reeds make a strong policy boat? Social Policy Report, XXI (3), 14-15.

Cook, T.D., y Wong, V.C. (2008). Empirical Tests of the Validity of the Regression Discontinuity Design. Annals of Economics and Statistics (91/92), 127-150.

Finkelstein, M., Levin, B., y Robbins, H. (1996). Clinical and prophylactic trials with assured new treatment for those at greater risk: I. A design proposal. Journal of Public Health, 86(5), 691-695.

Finkelstein, M., Levin, B., y Robbins, H. (1996). Clinical and prophylactic trials with assured new treatment for those at greater risk: II. Examples. Journal of Public Health, 86(5), 696-705.

Gersten, R., y Dimino, J. A. (2006). RTI (Response to Intervention): Rethinking special education for students with reading difficulties (yet again). Reading Research Quarterly, 41(1), 99-108.

Gormley, W.T. Jr., Gayer, T., y Phillips, D. (2005). The effects of universal pre-k on cognitive development. Developmental Psychology, 41(6), 872-884.

Green, D.P., Leong, T.I., Kern, H.L., Gerber, A.S., y Larimer, C.V. (2009). Testing the Accuracy of Regression Discontinuity Analysis Using Experimental Benchmarks. Political Analysis, 17, 400-417.

Hahn, H., P. Todd, P., y van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects With a Regression-Discontinuity Design. Econometrica, 69(3), 201-209.

Imbens, G.W., y Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.

Jacob, B.A., y Lefgren, L. (2004). Remedial education and student achievement: A regression-discontinuity analysis. The Review of Economics and Statistics, 86(1), 226-244.

Jacob, B.A., y Lefgren, L. (2004). The impact of teacher training on student achievement: Quasiexperimental evidence from school reform efforts in Chicago. Journal of Human Resources, 39(1), 50-79.

Johnston, M.V., Ottenbacher, K.J., y Reichardt, C.S. (1995). Strong quasi-experimental designs for research on the effectiveness of rehabilitation. American Journal of Physical Medicine & Rehabilitation, 74(5), 383-92.

Jungman, S. (2011). Regression Discontinuity Design: Application in Educational Research. Paper presented at the annual meeting of the Southwest Educational Research Association, San Antonio, TX. Disponible online en http://faculty.smu.edu/kyler/presen/sera2011/millerpaper.docx.

Kyriakides, L., y Luyten, H. (2009). The contribution to the cognitive development of secondary education students in Cyprus: An application of regression discontinuity with multiple cut-off points. School Effectiveness and School Improvement, 20(2), 167-186.

Lee, H.B. (2008). Analyzing Data from a Regression Discontinuity Study: A Research Note. Journal of Research Methods and Methodological Issues, 2(1), 1-9.

Lee, D., y Card, D. (2008). Regression discontinuity inference with specification error. Journal of Econometrics, 142(2), 655-674

Lesik, S. (2006). Applying the regression-discontinuity design to infer causality with non-random assignment. Review of Higher Education, 30(1), 1-19.

Lesik, S.A. (2007). Do developmental mathematics programs have a causal impact on student retention? An application of discrete-time survival and regression-discontinuity analysis. Research in Higher Education, 48(5), 583-608.

Ludwig, J., y Miller, D.L. (2007). Does Head Start improve children's life chances: Evidence from a regression discontinuity approach. Quarterly Journal of Economics, 122(1), 159-208.

Mandell, M. (2008). Having one's cake and eating it, too: Combining true experiments with regression discontinuity designs. Evaluation Review, 32(5), 415-434.

Marsh, H. (1998). Simulation study of nonequivalent group-matching and regression-discontinuity designs: Evaluations of gifted and talented programs. Journal of Experimental Education, 66(2), 163-92.

Ou, D. (2010). To leave or not to leave? A regression discontinuity analysis of the impact of failing the high school exit exam. Economics of Education Review, 29, 171-186.

Papay, J.P., Murnane, R.J., y Willett, J.B. (2010). The consequences of high school exit examinations for low-performing urban students: Evidence from Massachusetts. Educational Evaluation and Policy Analysis, 32(1), 5-23.

Reardon, S.F., y Robinson, J.P. (2012). Regression discontinuity designs with multiple rating-score variables. Journal of Research on Educational Effectiveness, 5(1), 83-104.

Reichardt, C.S., y Henry, G.T. (2012). Regression-discontinuity designs. En H. Cooper, P.M. Camic, D.L. Long, A.T. Panter, D.Rindskopf, K.J. Sher y K.J. Sher (Eds.), APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological (pp. 511-526). Washington, DC US: American Psychological Association.

Roderick, M., y Nagaoka, J. (2005). Retention under Chicago’s high-stakes testing program: Helpful, harmful, or harmless? Educational Evaluation and Policy Analysis, 27(4), 309-340.

Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics, 2(1), 1-26.

Santarossa, G. (2008). The Evaluation of Public Program Effect Using Regression Discontinuity Method : An introduction. MPRA Paper No. 11282. Disponible online en http://mpra.ub.uni-muenchen.de/11282/1/MPRA_paper_11282.pdf.

Seaver, W.B. y Quarton, R.J. (1976). Regression-discontinuity analysis of Dean's List effects. Journal of Educational Psychology, 68(4), 459-465.

Schochet, P.Z. (2008). Technical methods report: Statistical power for regression discontinuity designs in education evaluations (NCEE 2008-4026). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Disponible online en http://www.eric.ed.gov/PDFS/ED511782.pdf.

Schochet, P.Z. (2009). Statistical power of regression discontinuity designs in education evaluations. Journal of Educational and Behavioral Statistics, 34, 238-266.

Schochet, P., Cook, T., Deke, J., Imbens, G., Lockwood, J.R., Porter, J., y Smith, J. (2010). Standards for regression discontinuity designs. Washington DC: U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse. Disponible online en http://ies.ed.gov/ncee/wwc/pdf/wwc_rd.pdf.

Schlotter, M., Schwerdt, G. y Woessmann, L. (2011). Econometric methods for causal evaluation of education policies and practices: a non-technical guide. Education Economics, 19(2), 109-137.

Shadish, W.R., Galindo, R., Wong, V.C., Steiner, P.M., y Cook, T.D. (2011). A randomized experiment comparing random to cutoff-based assignment. Psychological Methods, 16(2), 179-191.

Sun, S., y Pan, W. (2012). Investigating the Accuracy of Three Estimation Methods for Regression Discontinuity Design. The Journal of Experimental Education, 81(1), 1-21.

Thistlethwaite, D.L., y Campbell, D.T. (1960). Regression discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51(6), 309-317.

Trochim, W.K . (1984). Research Design for Program Evaluation: The Regression- Discontinuity Approach. Beverly Hills: Sage.

Trochim, W.M.K. (2006). The regression-discontinuity design. Disponible online en http://www.socialresearchmethods.net/kb/quasird.php

Trochim, W.M.K. (2006). Regression-discontinuity analysis. Disponible online en http://www.socialresearchmethods.net/kb/statrd.php.

Trochim, W.M.K., y Cappelleri, J.C. (1992). Cut-off assignment strategies for enhancing randomized clinical trials. Controlled Clinical Trials, 13, 190-212.

Trochim, W.M.K., Cappelleri, J.C., y Reichardt, C.S. (1991). Random measurement error does not bias the treatment effect estimate in the regression-discontinuity design: II. When an interaction effect is present. Evaluation Review, 15(5), 571-604.

Vaughn, S., Wanzek, J., Murray, C.S., Scammacca, N., Linan-Thompson, S., y Woodruff, A.L. (2009). Response to early reading intervention: Examining higher and lower responders. Exceptional Children, 75(2), 165-183.

Wong, V.C., Cook, T.D., Barnett, S.W., y Jung, K. (2008). An effectiveness-based evaluation of five state pre-kindergarten programs. Journal of Policy Analysis and Management, 27(1), 122-154.

Zuckerman, Ll.H.; Lee, E.; Wutoh, A.K.; Xue, Z., y Stuart, B. (2006). Application of regression-discontinuity analysis in pharmaceutical health services research. Health Services Research, 41(2), 550-563.

3.- Análisis de datos: Diseños de Series Temporales.

Agarwal, R., Metiku, T., Tegegne, G.G., Light, R.P., Bunaye, Z., Bekele, D.M., y Kelley, K. (2008). Diagnosing hypertension by intradialytic blood pressure recordings. Clinical Journal of the American Society of Nephrology, 3, 1364-1372.

Alasuutar, P., Bickman and, L. y Brannen, J. (Eds.). (2008). Handbook of Social Research Method. Newbury Park, CA: Sage.

Alexander, J.M., Johnson, K.E., y Kelley, K. (2012). Longitudinal analysis of the relations between opportunities to learn about science and the development of interests related to science. Science Education, 96, 763-786.

Allison, P. D. (1994). Using panel data to estimate the effects of events. Sociological Methods and Research, 23, 174-199.

Anderson, J.A. Wright, E. R, Kelley, K., y Kooreman, H.E. (2008). Patterns of clinical functioning over time for young people served in a system of care. Journal of Emotional and Behavioral Disorders, 16, 90-104.

Arjas, E., y Parner, J. (2004). Causal reasoning from longitudinal data. Scandinavian Journal of Statistics, 31, 171-187.

Arnau, J. (Ed.) (1995). Diseños longitudinales aplicados a las ciencias sociales y del comportamiento. Mexico: Limusa.

Arnau, J. (Ed.). (2001). Diseños de series temporales: Técnicas de análisis. Barcelona. Edicions Universitat de Barcelona.

Arnau, J. (2008). Special Issue: Longitudinal Approach in Psychology and the SocialSciences. Theoretical and Analytical Perspectives. Methodology, 4, 1-3.

Arnau J., y Bono. R. (2004). Evaluating effects in short-time series: alternative models of analysis.Perceptual and Motor Skills, 98, 419-432.

Bajoga, U., Lewis, S., McNeill, A., y Szatkowski, L. (2011). Does the introduction of comprehensive smokefree legislation lead to a decrease in population smoking prevalence?. Addiction, 106, 1346-54.

Benjamin, M., Rigby, R., y Stasinopoulos, D. (2003). Generalized autoregressive moving average models. Journal of the American Statistical Association, 98, 214-23.

Bloom, H.S. (1999). Estimating program impacts on student achievement using "short" interrupted time series. Washington, DC: MDRC.

Bloom, H.S. (2003). Using "short" interrupted time-series analysis to measure the impacts of whole-school reforms with applications to a study of accelerated schools. Evaluation Review, 27(1), 3-49.

Bloom, H.S., Ham, S., Melton, L., O'Brien, J., Doolittle, F., y Kagehiro, S. (2001). Evaluating the Accelerated Schools Approach: A look at early implementation and impacts on student achievement in eight elementary schools. Washington, DC: MDRC.

Box, G.E.P., Jenkins, G.M., y Reinsel, G.C. (1994). Time series analysis: forecasting and control. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.

Brockwell, P.J., y Davis, R.A. (1991). Time series: theory and methods. 2nd ed. New York: Springer-Verlag.

Burr, J.A., y Nesselroade, J.R. (1990). Change measurement. En Von Eye, A. (Ed.), Statistical methods in longitudinal research, Vol 1: Principles and structuring change (pp. 3-35). Boston: Academic Press.

Connelly, M.., Sullivan, A.M., Peters, A.S., Clark-Chiarelli, N., Zotov, N., Martin, N., Simon, S.R., Singer, J.D., y Block, S.D. (2003). Variation in Predictors of Primary Care Career Choice by Year and Stage of Training: A National Survey. Journal of General Internal Medicine. 18 (3), 159-169.

Crosbie, J. (1993). Interrupted time-series analysis with brief single-subject data. Journal of Consulting & Clinical Psychology, 61(6), 966-974.

Crosbie, J. (1995). Interrupted time-series analysis with short series: Why it is problematic; How it can be improved. En J.M. Gottman (Ed.), The analysis of change (pp. 361-395). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Diggle, P. (1989). Time series: a biostatistical introduction. Oxford, United Kingdom: Oxford University Press.

Duncan, T.E., y Duncan, S.C. (2004). A latent growth curve modeling approach to pooled interrupted time series analyses. Journal of Psychopathology and Behavioral Assessment, 26(4), 271-278.

Escudero, J.R., y Vallejo, G. (2000). Aplicación del diseño de series temporales múltiples a un caso de intervención en dos clases de EGB. Psicothema, 12(4), 203-210.

Ferron, J., y Sentovich, C. (2002). Statistical power of randomization tests used with mulitple-baseline designs. The Journal of Experimental Education, 70(2), 165-178.

Fitzmaurice, G.M., Laird, N.M., y Ware, J. H. (2004). Applied longitudinal analysis. Hoboken, NJ: Wiley.

Francis, D.J., Fletcher, J.M., Jack, K.K., Davidson, K.C. y Thompson, N.M. (1991). Analysis of change: Modeling individual growth. Journal of Consulting and Clinical Psychology, 59(1), 27-37.

Light, R.J., Singer, J.D., y Willett, J.B. (1990). By Design: Planning Research on Higher Education. Cambridge, MA: Harvard University Press.

Friedman, S.L., y Heywood (Eds.) (1994). Developmental Follow-Up: Concepts, Genres, Domains, and Methods. Orlando, FL: Academic Press.

Glass, G., Willson, V.L. y Gottman, J.M. (1975). Design and analysis of time-series experiments. Boulder, Colo.: Colorado Associated University Press.

Graham, S.E., y Singer, J.D. (2006), Using discrete-time survival analysis to study gender differences in leaving mathematics. En S. Sawilowsky (Ed.) Real Data Analysis, (pp. 325-334). New York: Information Age Publishing.

Graham, S.E., Singer, J.D. y Willett, J.B. (2009). Longitudinal Data Analysis. En A. Maydeu-Olivares yd R. Millsap (Eds.) Handbook of Quantitative Methods in Psychology (pp. 613-675). Newbury Park, CA: Sage.

Hayes, A.M., Laurenceau, J.P., Feldman, G.C., Strauss, J.L., y Cardaciotto, L. (2007). Change is not always linear: The study of nonlinear and discontinuous patterns of change in psychotherapy. Clinical Psychology Review, 27(6), 715–723.

Heckman, J.J. ,y Robb, R. (1989). The Value of Longitudinal Data for Evaluating the Impact of Treatments on Outcomes. En D. Kasprzyk, G. Duncan, G. Kalton y M. P. Singh: Panel Surveys (pp. 512-538). New York: Wiley.

Hedeker, D., y Gibbons, R.D. (2006). Longitudinal Data Analysis. Hoboken, NJ: Wiley. Helfenstein, U. (1986). Box-Jenkins modelling of some viral infectious diseases. Statistics in

Medicine, 5, 37-47. Helfenstein U. (1996). Box-Jenkins modelling in medical research. Statistical Methods in

Medical Research, 5,3-22.Holden, J.E., Kelley, K., y Agarwal, R. (2008). Analyzing change: A primer on multilevel

models with applications to nephrology. American Journal of Nephrology, 28, 792-801.Huitema, B.E. (2004). Analysis of interrupted time-series experiments using ITSE: A critique.

Understanding Statistics, 3(1), 27-46. Huitema, B., y McKean, J., (2000). Design specification issues in time-series intervention

models. Educational and Psychological Measurement, 60(1). 38-58. Huitema, B., y McKean, J., (2007). Identifying autocorrelation generated by various error

processes in interrupted time-series regression designs: A comparison of AR1 and Portmanteau tests. Educational and Psychological Measurement, 67(3), 447-459.

Kelley, K. (2009). The average rate of change for continuous time models. Behavior Research Methods, 41, 268-278.

Kelley, K., y Maxwell, S.E. (2008). Delineating the average rate of change in longitudinal models. Journal of Educational and Behavioral Statistics, 33, 307-332.

Kelley, K., y Rausch, J.R. (2011). Sample size planning for longitudinal models: Accuracy in parameter estimation for polynomial change parameters. Psychological Methods, 16, 391-405.

Lambdin, C., y Shaffer, V.A. (2009). Are within-subjects designs transparent? Judgment and Decision Making, 4(7), 554-566.

Langley, T. (2012). The use of existing data sources to evaluate the impact of tobacco control policies on quitting behaviour. Thesis. Disponible online en http://etheses.nottingham.ac.uk/

Langley, T., Huang, Y., Lewis, S., McNeill, A., Coleman, T., y Szatkowski, L. (2011). Prescribing of nicotine replacement therapy in adolescents in England. Addiction, 106(8), 1513-1519.

Langley, T., Huang, Y., McNeill, A., Coleman, T., Szatkowski, L., y Lewis, S. (2011). Prescribing of smoking cessation medication in England since the introduction of varenicline. Addiction, 106(7), 1319–24.

Langley, T., McNeill, A., Lewis, S., Szatkowski, L., y Quinn, C. The impact of media campaigns on smoking cessation activity: a structural vector autoregression analysis. Addiction, 107(11), 2043, 2050.

Langley, T., Szatkowski, L., McNeill, A., Coleman, T., y Lewis, S. (2012). Prescribing of nicotine replacement therapy to cardiovascular disease patients in England. Addiction, 107, 1341-8.

Laurenceau, J-P., Hayes, A.M., y Feldman, G.C. (2007).Some methodological and statistical issues in the study of change processes in psychotherapy. Clinical Psychology Review, 27(6), 682–695.

Li, W. (1994). Time series models based on generalized linear models: some further results. Biometrics, 50, 506-11.

Little, T.D., Card, N.A., Preacher, K.J., y McConnell, E. (2009). Modeling longitudinal data from research on adolescence. En R.M. Lerner y L. Steinberg (Eds.), Handbook of adolescent psychology (3rd ed.) (pp. 15-54). Hoboken, NJ: Wiley.

Little, T.D., Preacher, K.J., Selig, J.P., y Card, N.A. (2007). New developments in latent variable panel analyses of longitudinal data. International Journal of Behavioral Development, 31, 357-365.

Maxwell, S.E. (1998). Longitudinal designs in randomized group comparisons: When will intermediate observations increase statistical power?. Psychological Methods, 3, 275-290.

McDowall, D., McCleary, R., Meidinger, E., y Hay, R.A. (1980). Interrupted time series analysis. Beverly Hills and London: Sage Publications.

Menard, D. (Ed.). (2008). Handbook of Longitudinal Research.. Newbury Park, CA: Sage. Moerbeek, M. (2008). Powerful and cost-efficient designs for longitudinal intervention studies

with two treatment groups. Journal of Educational and Behavioral Statistics, 33(1), 41-61.

Moskowitz, D.S., y Hershberger, S.L. (Eds.) (2002). Modeling Intraindividual Variability with Repeated Measures Data: Methods and Applications. Hillsdale, NJ: Lawrence Erlbaum Publishers.

Pan, B.A., Rowe, M.L., Singer, J.D., y Snow, C.E. (2005). Maternal correlates of growth in toddler vocabulary production in low-income families, Child Development, 76(4), 763-782.

Peer, J.E., Kupper, A., Long, J.D., Brekke, J.S., y Spaulding, W.D. (2007). Identifying mechanisms of treatment effects and recovery in rehabilitation of schizophrenia: Longitudinal analytic methods. Clinical Psychology Review, 7(6), 696-714.

Preacher, K.J., Cai, L., & MacCallum, R.C. (2007). Alternatives to traditional model comparison strategies for covariance structure models. En T. D. Little, J. A. Bovaird, y N.A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 33-62). Mahwah, NJ: Lawrence Erlbaum Associates.

Quen, H., y van den Bergh, H. (2004). On multi-level modeling of data from repeated measures designs: a tutorial. Speech Communication 43, 103-121.

Robins, J. (1989). The analysis of randomized and non-randomized aids treatment trials using a new approach to causal inference in longitudinal studies. En L. Sechrest, H. Freeman y A. Mulley (eds.). Health Service Research Methodology: A Focus on AIDS (pp. 113-159). Washington: NCHSR, U.S. Public Health Service.

Rogosa, D. (1988). Myths about longitudinal research. En K.W. Schaie, R.T. Campbell, W. Meredith, y S.C. Rawlings (Eds.), Methodological Issues in Aging Research (pp. 171-209). New York: Springer

Rogosa, D.R., Brandt, D., y Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 90, 726-748.

Rogosa, D. R., y Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50, 203-228.

Selig, J.P., y Preacher, K.J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6, 144-164.

Selig, J.P., Preacher, K.J., y Little, T.D. (2012). Modeling time-dependent association in longitudinal data: A lag as moderator approach. Multivariate Behavioral Research, 47, 697-716.

Sims, M., Maxwell, R., Bauld, L., y Gilmore, A. (2010). Short term impact of smoke-free legislation in England: retrospective analysis of hospital admissions for myocardial infarction. British Medical Journal, 340:c2161.

Singer, J.D. (2005). Review of Longitudinal Data and SAS: A Programmer’s Guide. Biometrics, 61(2), 643.

Singer, J.D., y Willett, J.B. (1996). Methodological Issues in the Design of Longitudinal Research: Principles and Recommendations for a Quantitative Study of Teachers' Careers. Educational Evaluation and Policy Analysis, 18(4), 265-283.

Singer, J.D. y Willett, J.B. (2003). Applied Longitudinal Data Analysis: Methods for Studying Change and Event Occurrence. New York: Oxford University Press.

Singer, J.D., y Willett, J.B. (2005). Improving the practice of longitudinal research. International Society for Behavioral Development Newsletter, 48(2), 13-14.

Singer, J.D., y Willett, J.B. (2005). Growth Curve Modeling. En B. Everitt y D. Howell (Eds.) Encyclopedia of Statistics in Behavioral Science, Volume 2, (pp. 772-779). Chicester, England: John Wiley & Sons.

Szatkowski, L. (2011). Can primary care data be used to evaluate the effectiveness of tobacco control policies? Data quality, method development and assessment of the impact of smokefree legislation using data from The Health Improvement Network. Thesis. University of Nottingham. Disponible online en http://etheses.nottingham.ac.uk/1902/

Szatkowski, L., Coleman, T., McNeill, A., y Lewis, S. (2011). The impact of the introduction of smoke-free legislation on prescribing of stop-smoking medications in England. Addiction, 106, 1827-34.

Touloumi, G., R. Atkinson, A. Le Tertre, et al. (2004). Analysis of health outcome time series data in epidemiological studies. EnvironMetrics, 15(2), 101-17.

Wagner, A.K., Soumerai, S.B., Zhang, F., y Ross-Degnan, D.  (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299-309.

Vallejo, G. (1995). Diseños de series temporales interrumpidas. En M. T. Anguera et al., (Eds.), Métodos de Investigación en Psicología, (pp. 290-304). España: Síntesis.

Vallejo, G. (1095). Análisis de diseños de series temporales interrumpidas. En M. T. Anguera et al., (Eds.), Métodos de Investigación en Psicología (pp. 305-323). España: Síntesis.

Vallejo, G. (1995). Problemas inferenciales asociados con el uso de diseños de series temporales interrumpidas. En M. T. Anguera et al., (Eds.), Métodos de Investigación en Psicología (pp. 324-352). España: Síntesis.

Vallejo, G. (1996). Diseños de Series Temporales Interrumpidas. España: Ariel.Vallejo, G. (2000). Análisis de series temporales interrumpidas con múltiples unidades. En A.

López, J. López y R. Moreno (Cood.), Metodología de las Ciencias Humanas y Sociales (pp. 213-225). España: Kronos.

Vallejo, G., y Escudero, J.R. (2001). Análisis de diseños de series temporales múltiples. En J. Arnau (Ed.), Diseños de Series Temporales: Técnicas de Análisis, (pp. 209-231). Barcelona: Edicions Universitat de Barcelona.

Vallejo, G., Livacic, P., y Conejo, N. (2003). Análisis de los diseños AB replicados mediante series temporales agrupadas. International Journal of Psychology and Psychological Therapy, 3, 7-26.

Vallejo, G., Arnau, J., y Bono, R. (2008). Construcción y análisis de modelos jerárquicos en contextos longitudinales y transversales. Psicothema, 20(4). 830-838.

Warren, J., Stoerger, S.M., y Kelley, K. (2012). Longitudinal gender and age bias in a prominent amateur new media community. New Media & Society, 14, 7-27.

West, S.G. y Hepworth, J.T. (1991). Statistical issues in the study of temporal data: Daily experiences. Journal of Personality, 59, 609-662.

Willett, J.B., Singer, J.D., y Martin, N.M. (1998). The Design and Analysis of Longitudinal Studies of Psychopathology and Development in Context: Statistical Models and Methodological Recommendations. Development and Psychopathology, 10, 395-426.

Windsor, R.A. (1986). The utility of time series designs and analysis in evaluating health promotion and education programs. Advances in Health Education and Promotion, 1, 435-465.

Yaffee, R.A. (2012). An Introduction to Forecasting Time Series with Stata: Taylor & Francis.Yaffee, R., y McGee, M. (2000). Introduction to Time Series Analysis and Forecasting with

Applications of SAS and SPSS. San Diego, California: Academic Press.Zeger, S. (1988). A regression model for time series of counts. Biometrika, 75, 621-9.Zeger, S.L., Irizarry, R., y Peng, R.D. (2006). On time series analysis of public health and

biomedical data. Annual Review of Public Health, 27, 57-79.Zeger, S., y Qaqish, B. (1988). Markov regression models for time series: a quasilikelihood

approach. Biometrics, 44, 1019-31.Zhang, F.,Wagner, A.K., y Ross-Degnan, D. (2011).  Simulation-based power calculation for

designing interrupted time series analyses of health policy interventions . Journal of Clinical Epidemiology, 11, 1252-1261.

Zinn, W.M., Sullivan, A.M., Zotov, N., Peters ,A.S., Connelly, M.T., Singer, J.D., y Block, S.D. (2001). The effect of medical education on primary care orientation: A national survey of student and resident perspectives. Academic Medicine, 76(1), 355-365.

4.- Mediación, efectos indirectos, variables supresoras, etc.

Aguinis, H., Beaty, J.C., Boik, R.J., y Pierce, C.A. (2005). Effect size and power in assessing moderator effects of categorical variables using multiple regression: A 30-year review. Journal of Applied Psychology, 90(1), 94-107.

Angrist, J., Imbens, G., y Rubin, D. (1996). Identification of causal effects using instrumental variables (with comments). Journal of the American Statistical Association 91, 444-472.

Albert, J.M. (2008). Mediation analysis via potential outcomes models. Statistics in Medicine, 27, 1282–1304.

Albert, J.M., y Nelson, S. (2011). Generalized causal mediation analysis. Biometrics, 67(3), 1028-1038.

Angrist, J.D., Imbens, G.W., y Rubin, D.B. (1996). Identification of causal effects using instrumental variables (with discussion). Journal of the American Statistical Association, 91, 444-455.

Arah, O. (2008). The role of causal reasoning in understanding Simpson’s paradox, Lord’s paradox, and the suppression effect: Covariate selection in the analysis of observational studies. Emerging Themes in Epidemiology 5, Art. 5. Disponible online en http://www.ete-online.com/content/pdf/1742-7622-5-5.pdf

Ato, M., y Vallejo, G. (2011). Los efectos de terceras variables en la investigación psicológica. Anales de Psicología, 27(2), 550-561.

Baron, R.M., y 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), 1173-1182.

Bauer, D.J., Preacher, K.J., y Gil, K.M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142-163.

Berzuini, C., Dawid, P., Zhang, H., y Parkes, M. (2012). Analysis of Interaction for Identifying Causal Mechanisms. En C. Berzuini, P. Dawid and L. Bernardinelli (Eds.). Causality: Statistical Perspectives and Applications. Chichester: John Wiley & Suns.

Bullock, J.G., Green, D.P., y 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.

Chalak, K., y White, H. (2011). An Extended Class of Instrumental Variables for the Estimation of Causal Effects. Canadian Journal of Economics, 44, 1-51.

Chen, A., Bengtsson, T., y Ho, T. (2009). A regression paradox for linear models: Sufficient conditions and relation to Simpson’s paradox. The American Statistician, 63, 218-225.

Cole, S., y Hernán, M. (2002). Fallibility in estimating direct effects. International Journal of Epidemiology, 31, 163-165.

Cole, D.A., y 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.

Crosby, R.A., Salazar, L.F., Di Clemente, R.J., y Lang, D.L. (2010). Balancing rigor against the inherent limitations of investigating hard-to-reach populations. Health Education Research, 25(1), 1-5.

Culpepper, S.A., y Aguinis, H. (2011). Using analysis of covariance (ANCOVA) with fallible covariates. Psychological Methods, 16(2), 166-178.

Deaton, A. (2009). Instruments of development: Randomization in the tropics, and the search for the elusive keys to economic development. Nber Working Paper Series. Disponible en http://www.nber.org/papers/w14690.pdf?new_window=1.

Dong, J. (2005). Simpson's Paradox. Encyclopedia of Biostatistics. Edwards, J.R., y Lambert, L.S. (2007). Methods for integrating moderation and mediation: A

general analytical framework using moderated path analysis. Psychological Methods, 12, 1-22.

Emsley, R., Dunn, G., y White, I.R. (2010). Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Statistical Methods in Medical Research, 19(3), 237-70.

Fairchild, A.J., y McQuillin, S.D. (2010). Evaluating mediation and moderation effects in school psychology: a presentation of methods and review of current practice. Journal of School Psychology, 48(1), 53-84.

Frazier, P.A., Tix, A.P., y Barron, K.E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51, 115-134.

Fritz, M.S., y MacKinnon, D.P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18, 233-239

Fritz, M.S., Taylor, A.B., y MacKinnon, D.P. (2012). Explanation of two anomalous results in statistical mediation analysis. Multivariate Behavioral Research, 47, 61-87.

Gallop, R., Small, D.S., Lin, J.Y., Elliot, M.R., Joffe, M., y Ten Have, T.R. (2009). Mediation analysis with principal stratification. Statistics in Medicine, 28, 1108-1130.

Gelfand, L.A., Mensinger, J.L., y Tenhave, T. (2009). Mediation analysis: a retrospective snapshot of practice and more recent directions. Journal of General Psychology, 136(2), 153-76.

Geneletti, S. (2007). Identifying direct and indirect effects in a non-counterfactual framework. Journal of the Royal Statistical Society: Series B, 69, 199-215.

Goetgeluk, S., Vansteelandt, S., y Goetghebeur, E. (2008). Estimation of controlled direct effects. Journal of the Royal Statistical Society - Series B, 70, 1049-1066.

Goodman, L.A. (1960). On the exact variance of products. Journal of the American Statistical Association, 55, 708-713.

Hafeman, D.M. (2009). "Proportion explained": a causal interpretation for standard measures of indirect effect? American Journal of Epidemiology, 170(11), 1443-1448.

Hafeman, D.M. (2011). Confounding of Indirect Effects: A Sensitivity Analysis Exploring the Range of Bias Due to a Cause Common to Both the Mediator and the Outcome. American Journal of Epidemiology, 174 (6), 710-717.

Hafeman, D., y Schwartz, S. (2009). Opening the black box: A motivation for the assessment of mediation. International Journal of Epidemiology, 3, 838-845.

Hafeman, D.M. y VanderWeele, T.J. (2011). Alternative assumptions for the identification of direct and indirect effects. Epidemiology, 22, 753-764.

Hayes, A.F., Preacher, K.J., y Myers, T.A. (2011). Mediation and the estimation of indirect effects in political communication research. En E.P. Bucy y R.L. Holbert (Eds.), The sourcebook for political communication research: Methods, measures, and analytical techniques (pp. 434-465). New York: Routledge.

Heckman, J. (1997). The Value of Quantitative Evidence on the Effect of the Past on the Present. American Economic Review, American Economic Association, 87(2), 404-08.

Heckman, J. (1997). Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations. Journal of Human Resources, 32(3), 441-462.

Heckman, J., y Navarro-Lozano, S. (2004). Using matching, instrumental variables, and control functions to estimate economic choice models. The Review of Economics and Statistics, 86, 30-57.

Heckman, J., y Robb, R. (1985). Alternative methods for evaluating the impact of interventions: An overview. Journal of Econometrics, 30(1-2), 239-267.

Holmbeck, G.N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures. Journal of Consulting and Clinical Psychology, 65, 599-610.

Howell, D. (2007). Statistical Methods for Psychology (6th Ed). Australia: Thomson.Imai, K., Keele, L., y Tingley, D. (2010). A general approach to causal mediation analysis.

Psychological Methods, 15(4), 309-334.Imai, K., Keele, L., y Yamamoto, T. (2010). Identification, inference, and sensitivity analysis

for causal mediation effects Source: Statistical Science, 25 (1), 51-71. Imai, K., Keele, L., y Tingley, D. (2010). A general approach to causal mediation analysis.

Psychological Methods, 15(4), 309-334.Imai, K., Keele, L., y Yamamoto, T. (2010). Identification, inference, and sensitivity analysis

for causal mediation effects Source: Statistical Science, 25 (1), 51-71. Imai, K. y Ratkovic, M. Estimating Treatment Effect Heterogeneity in Randomized Program

Evaluation. Annals of Applied Statistics (in press).Imai, K., Tingley, D., y Yamamoto, T. (2013). Experimental designs for identifying causal

mechanisms. Journal of the Royal Statistical Society, Series A, 176 (1) (aceptado, in press).

Imai, K., y Yamamoto, T. (2010). Causal inference with differential measurement error: Nonparametric identification and sensitivity analysis. American Journal of Political Science, 54, 543–560.

Imai, K., y Yamamoto, T. Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments. Political Analysis (aceptado, in press).

Imbens, G.W. (2003). Sensitivity to exogeneity assumptions in program evaluation. American Economic Review, 93, 126–132.

Imbens, G., y Wooldridge, J. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86.

James, L.R., y Brett, J.M. (1984).  Mediators, moderators and tests for mediation. Journal of Applied Psychology, 69, 307-321.

Jo, B. (2008). Causal inference in randomized experiments with mediational processes. Psychological Methods, 13, 314–336.

Jo, B., Stuart, E.A., Mackinnon, D.P., y Vinokur, A.D. (2011). The Use of Propensity Scores in Mediation Analysis. Multivariate Behavioral Research, 46(3), 425-452.

Joffe, M. M., Small, D., Ten Have, T., Brunelli, S., y Feldman, H. I. (2008). Extended instrumental variables estimation for overall effects. The International Journal of Biostatistics, 4(1), 1-20.

Joffe, M., Small, D., y Hsu, C.Y. (2007). Defining and estimating intervention effects for groups that will develop an auxiliary outcome. Statistical Science, 22, 74–97.

Judd, C.M., y Kenny, D.A. (1981).  Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5, 602-619.

Judd, C.M., y Kenny, D.A. (2010). Data analysis in social psychology: Recent and recurring issues. En D. Gilbert, S.T. Fiske y G. Lindzey (Eds.), The handbook of social psychology, (5th ed., pp. 115–139). Boston, MA: McGraw-Hill.

Knol, M.J., y VanderWeele, T.J. (2012). Guidelines for presenting analyses of effect modification and interaction. International Journal of Epidemiology, 41, 514-520.

Kraemer, H.C., Kiernan, M., Essex, M., y Kupfer, D.J. (2008). How and why criteria definig moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychology, 27, S101–S108.

Kraemer, H.C., Wilson, T., Fairburn, C.G., y Agras, W.S. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59, 877–883.

Kuroki, M., y Miyakawa, M. (1999). Identifiability criteria for causal effects of joint interventions. Journal of the Royal Statistical Society, 29, 105-117.

Lange, T., Vansteelandt, S., y Bekaert, M. (2012). A Simple Unified Approach for Estimating Natural Direct and Indirect Effects. American Journal of Epidemiology, 176 (3), 190-195.

le Cessie, S., Debeij, J., Rosendaal, F.R., Cannegieter, S.C., y Vandenbroucke, J.P. (2012). Quantification of bias in direct effects estimates due to different types of measurement error in the mediator. Epidemiology, 23(4), 551-60.

Lepage1, B., Dedieu1, D., Savy, N., y Lang, T. (2012). Estimating controlled direct effects in the presence of intermediate confounding of the mediator–outcome relationship: Comparison of five different methods. Statistical Methods in Medical Research, doi: 10.1177/0962280212461194.

MacKinnon, D.P. (2008). Introduction to Statistical Mediation Analysis. New York: Taylor & Francis.

MacKinnon, D.P., Fairchild, A.J., y Fritz, M.S.  (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.

MacKinnon, D.P., Lockwood, C.M., Hoffman, J.M., West, S.G., y Sheets, V.  (2002). A comparison of methods to test the significance of the mediated effect. Psychological Methods, 7, 83-104.

MacKinnon, D.P., Fairchild, A.J., y Fritz, M.S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.

MacKinnon, D.P., Krull, J.L., y Lockwood, C.M. (2000). Equivalence of the mediation, confounding, and suppression effect. Prevention Science, 1(4), 173-181.

Maracy, M., y Dunn, G. (2011). Estimating dose-response effects in psychological treatment trials: the role of instrumental variables. Statistical Methods in Medical Research, 20(3), 191-215.

Muller, D., Judd, C.M., y Yzerbyt, V.Y. (2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89, 852-863.

Pavlides, M., y Perlman, M. (2009). How likely is Simpson’s paradox? The American Statistician, 63, 226-233.

Pearl, J. (1993). Mediating instrumental variables. Tech. Rep. TR-210, http://ftp.cs.ucla.edu/pub/stat ser/R210.pdf, Department of Computer Science, University of California, Los Angeles.

Pearl, J. (2005). Direct and indirect effects. In Proceedings of the American Statistical Association, Joint Statistical Meetings. MIRA Digital Publishing, Minn., MN, 1572-1581. Disponible online en http://ftp.cs.ucla.edu/pub/stat_ser/r273-jsm05.pdf.

Pearl, J. (2009). Letter to the editor: Remarks on the method of propensity scores. Statistics in Medici Medicine, 28, 1415-1416.

Pearl, J. (2009). Myth, confusion, and science in causal analysis. Tech. Rep. R-348, University of California, Los Angeles, CA. Disponible online en http://ftp.cs.ucla.edu/ pub/stat ser/r348.pdf.

Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). New York: Cambridge University Press.

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), art.7. Pearl, J. (2011). The mediation formula: A guide to the assessment of causal pathways in

nonlinear models, Technical Report R-363. Department of Computer Science, Los Ángeles: University of California. Disponible en http://ftp.cs.ucla.edu/pub/stat ser/r363.pdf.

Pearl, J. (2011). Principal Stratification - a Goal or a Tool?. The International Journal of Biostatistics, 7(1), Art. 20.

Pearl, J. (2012). The Causal Mediation Formula – A Guide to the Assessment of Pathways and Mechanisms. Technical Report, R-379. Prevention Science. Disponible en http://ftp.cs.ucla.edu/pub/stat_ser/r379.pdf

Pearl, J. (2012). Interpretable conditions for identifying direct and indirect effects. Technical Report, R-389. Disponible en http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA564093

Pearl, J., y Paz, A. (2009). Confounding equivalence in observational studies. Tech. Rep. TR-343, University of California, Los Angeles, CA. Disponible online en http://ftp.cs.ucla.edu/pub/stat ser/r343.pdf.

Petersen, M.L., Sinisi, S.E., y van der Laan, M.J. (2006). Estimation of direct causal effects. Epidemiology, 17, 276-284.

Preacher, K.J., y Hayes, A.F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods Instruments & Computers, 36(4), 717-31.

Preacher, K.J., y Hayes, A.F. (2008). Contemporary approaches to assessing mediation in communication research. En A.F. Hayes, M.D. Slater, & L.B. Snyder (Eds.), The Sage sourcebook of advanced data analysis methods for communication research (pp. 13-54). Thousand Oaks, CA: Sage.

Preacher, K.J., Rucker, D.D., y Hayes, A.F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227.

Preacher, K.J., y Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93-115.

Rigdon, E.E., Preacher, K.J., Lee, N., Howell, R.D., Franke, G.R., y Borsboom, D. (2011). Avoiding measurement dogma: A response to Rossiter. European Journal of Marketing, 45, 1589-1600.

Robins, J.M. (1999). Testing and estimation of direct effects by reparameterizing directed acyclic graphs with structural nested models. In Glymour and G. Cooper (Eds.) Computation, Causation, and Discovery (pp. 349-405). Menlo Park, CA, Cambridge, MA: AAAI Press/The MIT Press.

Robins, J.M., y Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3, 143-155.

Robins, J.M., Hernan, M., y Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550-560.

Rosenbaum, P., y Rubin, D. (1983). The central role of propensity score in observational studies for causal effects. Biometrika, 70, 41-55.

Roy, J., Hogan, J.W., y Marcus, B.H. (2008). Principal stratification with predictors of compliance for randomized trials with 2 active treatments. Biostatistics, 9, 277-289.

Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688-701.

Rubin, D.B. (2004). Direct and indirect causal effects via potential outcomes (with discussion). Scandinavian Journal of Statistics, 31, 161-170.

Rubin, D.B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322-331.

Rubin, D. (2007). The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials. Statistics in Medicine, 26, 20-36.

Rubin, D. (2009). Author’s reply: Should observational studies be designed to allow lack of balance in covariate distributions across treatment group? Statistics in Medicine, 28, 1420-1423.

Rubin, D.B. (2010). Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010). Psychological Methods, 15, 38-46.

Rucker, D.D., Preacher, K.J., Tormala, Z.L., y Petty, R.E. (2011). Mediation Analysis in Social Psychology: Current Practices and New Recommendations. Social and Personality Psychology Compass 5/6, 359-371.

Shadish, W.R., y Sweeney, R. (1991). Mediators and moderators in meta-analysis: There’s a reason we don't let dodo birds tell us which psychotherapies should have prizes. Journal of Consulting and Clinical Psychology, 59, 883-893.

Shpitser, I., y Pearl, J. (2008). Complete Identification Methods for the Causal Hierarchy. Journal of Machine Learning Research, 9, 1941-1979.

Shpitser, I., y VanderWeele, T. (2011). A complete graphical criterion for the adjustment formula in mediation analysis. The International Journal of Biostatistics, 7(1), Art. 16.

Shrier, I. (2009). Letter to the editor: Propensity scores. Statistics in Medicine, 28, 1317-1318. Shrout, P.E., y Bolger, N.  (2002).  Mediation in experimental and nonexperimental studies:

New procedures and recommendations. Psychological Methods, 7, 422-445. Sjölander, A. (2009). Bounds on natural direct effects in the presence of confounded

intermediate variables. Statistics in Medicine, 28, 558-571.Skrabanek, P. (1994). The emptiness of the black box. Epidemiology, 5, 5553-5555.Sobel, M. E. (2008). Identification of causal parameters in randomized studies with mediating

variables. Journal of Educational and Behavioral Statistics, 33, 230-251.Tchetgen E.J., y VanderWeele, T.J. (2012). On causal inference in the presence of interference.

Statistical Methods in Medical Research - Special Issue on Causal Inference, 21, 55-75. Ten Have, T.R., y Joffe, M.M. (2012). A review of causal estimation of effects in mediation

analyses. Statatistics Methods in Medical Research, 21(1), 77-107.

Tu, Y-K., Gunnell, D., y Gilthorpe, M.S. (2008). Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox. Emerging Themes in Epidemiology. 5, 2. Disponible en http://www.ete-online.com/content/5/1/2.

Valeri, L., y VanderWeele, T.J. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, in press.

VanderLaan, M.J., y Petersen, M,L. (2008). Direct Effect Models. The International Journal of Biostatistics, 4, Article 23.

VanderWeele, T.J. (2008). Simple relations between principal stratification and direct and indirect effects. Statistics and Probability Letters, 78, 2957-2962.

VanderWeele, T.J. (2009). Mediation and mechanism. European Journal of Epidemiology, 24, 217-224.

VanderWeele, T.J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20, 863-871.

VanderWeele, T.J. (2009). Sufficient cause interactions and statistical interactions. Epidemiology, 20, 6-13.

VanderWeele, T. J. (2010). Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology, 21(4), 540-551.

VanderWeele, T.J. (2010). Response to "On the definition of effect modification," by E. Shahar and D.J. Shahar. Epidemiology, 21, 587-588.

VanderWeele, T.J. (2011). Subtleties of explanatory language: what is meant by "mediation"? European Journal of Epidemiology, 26, 343-346.

VanderWeele, T.J. (2011). A word and that to which it once referred: assessing "biologic" interaction. Epidemiology, 22, 612-613.

VanderWeele, T.J. (2011). Controlled direct and mediated effects: definition, identification and bounds. Scandinavian Journal of Statistics, 38, 551-563.

VanderWeele, T.J. (2011). Principal stratification: uses and limitations. International Journal of Biostatistics, 7, Art. 28, 1-14.

VanderWeele, T.J. (2012). Mediation analysis with multiple versions of the mediator. Epidemiology, 23(3), 454-463.

VanderWeele, T.J. (in press). A three-way decomposition of a total effect into direct, indirect, and interactive effects. Epidemiology . Disponible en http://projects.iq.harvard.edu/causalmechanisms/files/threeway.pdf.

VanderWeele, T.J., y Arah, O.A. (2011). Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology, 22(1), 42-52.

VanderWeele, T.J., y Knol, M.J. (2011). The interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions. Annals of Internal Medicine, 154, 680-683.

VanderWeele, T.J., Mukherjee, B., y Chen, J. (2012). Sensitivity analysis for interactions under unmeasured confounding. Statistics in Medicine, 31, 2552-2564.

VanderWeele, T.J., y Robins, J.M. (2008). Empirical and counterfactual conditions for sufficient cause interactions. Biometrika, 95, 9-61.

VanderWeele, T.J., Valeri, L., y Ogburn, E.L. (2012). The role of misclassification and measurement error in mediation analyses. Epidemiology, 23, 561-564.

Vansteelandt, S., y VanderWeele, T.J. Natural direct and indirect effects on the exposed: effect decomposition under weaker assumptions. Biometrics, in press.

VanderWeele, T., y Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and Its Interface, 2, 457-468.

VanderWeele, T.J. y Vansteelandt, S. (2010). Odds Ratios for Mediation Analysis for a Dichotomous Outcome - with discussion. American Journal of Epidemiology, 172, 1339-1348.

Vansteelandt, S. (2009). Estimating direct effects in cohort and case-control studies. Epidemiology, 20, 851-860.

Vansteelandt, S. (2010). Estimation of controlled direct effects on a dichotomous outcome using logistic structural direct effect models. Biometrika, 97, 921-934.

Vansteelandt, S., Goetgeluk, S., Lutz, S., Waldman, I., Lyon, H., Schadt, E.E., Weiss, S.T., y Lange, C. (2009). On the adjustment for covariates in genetic association analysis: A novel, simple principle to infer causality. Genetic Epidemiology, 33, 394-405.

Wooldridge, J. (2009). Should instrumental variables be used as matching variables? Tech. Rep. Michigan State University, MI. Disponible online en https://www.msu.edu/∼ec/faculty/wooldridge/current%20research/treat1r6.pdf.

Yu, L.M., Chan, A.W., Hopewell, S., Deeks, J.J., y Altman, D.G (2010). Reporting on covariate adjustment in randomised controlled trials before and after revision of the 2001 CONSORT statement: a literature review. Trials, 18, 11-59.

Zigler, C.M., y Belin, T.R. (2011). The potential for bias in principal causal effect estimation when treatment received depends on a key covariate. Annals Applied Statistics, 5(3), 1876-1892.

5.- El problema de la regresión a la media.

Bland, J.M., y Altman, D.G. (1994). Statistic Notes: Regression towards the mean. British Medical Journal, 308, 1499.

Bland, J.M., y Altman, D.G. (1994). Some examples of regression towards the mean. British Medical Journal, 309, 780.

Dallal, G.E. (2000). The regression effect - The regression fallacy. Disponible online en http://www.jerrydallal.com/LHSP/regeff.htm

House, E.R. (2007). Regression to the mean: a novel of evaluation politics. USA: Information Age Publishing.

Morton, V., y Torgerson, D.J. (2003), Effect of regression to the mean on decision making in health care. British Medical Journal, 326:1083.

Lohman, D.F., y Korb, K.A. (2006). Gifted today but not tomorrow? Longitudinal changes in ability and achievement during elementary school. Journal for the Education of the Gifted, 29(4) 451-484.

Ostermann, T., Willich, S.N., y Lüdtke, R. (2008). Regression toward the mean – A detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology, 8(52).

Poulton, E.C. (1994). Behavioral decision theory: A new approach. Cambridge, UK & New York, NY: Cambridge University Press.

Smith, G., y Smith, J. (2005). Regression to the mean in average test scores. Educational Assessment, 10(4) 377-399.

Trochim, W.M.K. (2006). Research methods knowledge base: Regression toward the mean. Disponible online en http://www.socialresearchmethods.net/kb/regrmean.php

Trochim, W.M.K. (2006). Regression to the mean. Disponible online en http://www.socialresearchmethods.net/kb/regrmean.php.

6.- Sesgo de selección (concepto, control, prevención, cómo evitarlo, etc.).

Austin, P.C. (2008). A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Statistics in Medicine, 27(12), 2037-49.

Austin, P.C. (2008). Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score. Pharmacoepidemiology and Drug Safety, 17(12), 1202-17.

Austin, P.C. (2008). Assessing balance in measured baseline covariates when using many-to-one matching on the propensity-score. Pharmacoepidemiology and Drug Safety, 17(12), 1218-25.

Austin, P.C. (2011). A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of In-Hospital Smoking Cessation Counseling on Mortality. Multivariate Behavioral Research, 46(1), 119-151.

Bai, H. (2011). A comparison of propensity score matching methods for reducing selection bias. International Journal of Research & Method in Education, 34(1), 81-107.

Bareinboim, E., y Pearl, J. (2011). Controlling selection bias in causal inference (Tech. Rep. No. R-381), University of California, Los Angeles, CA: Department of Computer Science. Disponible en http://ftp.cs.ucla.edu/pub/stat ser/r381.pdf

Berger, V., y Rose, S. (2004). Ensuring the comparability of comparison groups: is randomization enough? Controlled Clinical Trials, 25(5), 515–524.

Bifulco, R. (2010). Can Nonexperimental Estimates Replicate Estimates Based on Random Assignment in Evaluations of School Choice?. A Within-Study Comparison. Journal of Policy Analysis and Management, 31(3), 729-751.

Bloom, H.S., Michalopoulos, C., y Hill, C.J. (2005). Using Experiments to Assess Nonexperimental Comparison-Groups Methods for Measuring Program Effects. En H. S. Bloom (Ed.), Learning More From Social Experiments: Evolving Analytic Approaches (pp. 173-235). New York: Russell Sage Foundation.

Bloom, H. S., Michalopoulos, C., Hill, C. J., y Lei, Y. (2002). Can nonexperimental comparison group methods match the findings from a random assignment evaluation of mandatory welfare-to-work programs? MDRC Working Papers on Research Methodology. Disponible en http://www.mdrc.org/publications/66/full.pdf.

Bloom, H.S., Richburg-Hayes, L., y Black, A.R. (2005). Using Covariates to Improve Precision Empirical Guidance for Studies That Randomize Schools to Measure the Impacts of Educational Interventions. MDRC Working Papers on Research Methodology . Disponible en http://www.mdrc.org/publications/417/abstract.html.

Bloom, H. S., Richburg-Hayes, L., y Black, A. R. (2007). Using covariates to improve precision for studies that randomize schools to evaluate educational interventions. Educational Evaluation & Policy Analysis, 29 (1), 30-59.

Borm, G.F., Fransen, J., y Lemmens, W.A.J.G. (2007). A simple sample size formula for analysis of covariance in randomized clinical trials. Journal of Clinical Epidemiology, 60(12), 1234-1238

Burton, A., y Altman, D.G. (2004). Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines. British Journal of Cancer, 91(1), 4-8.

Choi, B.C.K., y Pak, A.V.P. (2005). A catalog of biases in questionnaires. Preventing Chronic Disease. Public Health Research, Practice and Policy, 2(1), 1-13

Coalition for Evidence-Based Policy (2012). Which Comparison-Group (“Quasi-Experimental”) Study Designs Are Most Likely to Produce Valid Estimates of a Program’s Impact?: A Brief Overview and Sample Review Form. Disponible en http://coalition4evidence.org/wordpress/wp-content/uploads/Validity-of-comparison-group-designs-updated-Feb-2012.pdf.

Cochran, W.G. (1957). Analysis of covariance: Its nature and uses. Biometrics, 13, 261-281.Collier, D., y Mahoney, J. (1996). Insights and Pitfalls: Selection Bias in Qualitative Research.

World Politics, 49(1), 56-91.Cook, T. D. (2004). Recent empirical research on selection bias. Newsletter of the Institut fuer

Hoehere Studien, Vienna, 12(3), 8-9. Disponible online en http://www.ihs.ac.at/publications/lib/nl1220043.pdf

Cook, L., Cook, B.G., Landrum, T.J., y Tankersley, M. (2008). Examining the role of group experimental research in establishing evidenced-based practices. Intervention in School and Clinic, 44(2), 76-82.

Cox, E., Martin, B., van Staa, T., Garbe, E., Siebert, U., y Johnson, M.L. (2009). Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of non-randomized studies of treatment effects using secondary data sources: The International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report—Part II. Value in Health, 12(8), 1053-1061.e_601

Cook, T., Shadish, W.R., y Wong, V.C. (2008). Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons. Journal of Policy Analysis and Management, 27, 724-750.

Cook, T.D., y Steiner, P.M. (2010). Case matching and the reduction of selection bias in quasi-experiments: The relative importance of pretest measures of outcome, of unreliable measurement, and of mode of data analysis. Psychological methods, 15(1), 56-68.

Cook, T.D., Steiner, P.M., y Pohl, S. (2009). Assessing how bias reduction is influenced by covariate choice, unreliability and data analytic mode: an analysis of different kinds of within-study comparisons in different substantive domains. Multivariate Behavioral Research. 44, 828-847.

Datta, L.E. (2007). What are we, chopped liver? Or why it matters if the comparisons are active and what to do. The Evaluation Center, The Evaluation Café. Disponible en http://www.wmich.edu/evalctr/wp-content/uploads/2010/05/chopped-liver.pdf

Dehejia, R.H., y Wahba, S. (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053–1062.

Dehejia, R.H., y Wahba, S. (2002). Propensity Score Matching Methods for Non-Experimental Causal Studies, Review of Economics and Statistics, 84 (1), 151-161.

Delgado-Rodriguez, M., y Llorca, J. (2004). Bias. Journal of Epidemiololy and Community Health, 58, 635-641.

Diaz, J.J., y Handa, S. (2006). As assessment of propensity score matching as a nonexperimental impact estimator: Evidence from Mexico’s PROGRESA program. The Journal of Human Resources, XLI, 2, 319-345.

Fleiss, J.L. (1999) Analysis of Covariance and the Study of Change, En J.L. Fleiss (Ed.). The Design and Analysis of Clinical Experiments. Hoboken, NJ, USA: John Wiley & Sons, Inc.

Fraker, T., y Maynard, R. (1987). The adequacy of comparison group designs for evaluations of employment-related programs. Journal of Human Resources, 22, 2, 194-227

Funatogawa, T., Funatogawa, I., y Shyr, Y. (2011), Analysis of covariance with pre-treatment measurements in randomized trials under the cases that covariances and post-treatment variances differ between groups. Biometrical Journal, 53, 512-524.

Gerstein, D.R., y Johnson, R.A. (2000). Nonresponse and selection bias in treatment follow-up studies. Substance Use & Misuse, 35(6-8), 971-1014.

Gersten, R., Fuchs, L.S., Compton, D., Coyne, M. Greenwood, C., y Innocenti, M.S. (2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional Children, 71(2), 149-164.

Goldberger, A. S. (1972). Selection bias in evaluating treatment effects: Some formal illustrations (Discussion Paper 129-172). Madison, WI: University of Wisconsin, Institute for Research on Poverty. También en Tom Fomby, R. Carter Hill, Daniel L. Millimet, Jeffrey A. Smith y Edward J. Vytlacil (eds.). (2008). Modelling and Evaluating Treatment Effects in Econometrics (Advances in Econometrics, Volume 21), (pp. 1-31). Emerald Group Publishing Limited. Disponible en http://www.irp.wisc.edu/publications/dps/pdfs/dp12372.pdf

Goldberger, A.S. (1972). Selection bias in evaluating treatment effects: The case of interaction. Madison, WI: University of Wisconsin, Institute for Research on Poverty.

Goldberger, A.S. (1979). Methods for eliminating selection bias. Department of Economics, Madison: University of Wisconsin.

Goldberger, A.S. (1980). Abnormal selection bias. Social Systems Research Institute, Madison: University of Wisconsin, Madison.

Golinelli, D., Ridgeway, G., Rhoades, H., Tucker, J., y Wenzel, S. (2012). Bias and variance trade-offs when combining propensity score weighting and regression: with an application to HIV status and homeless men. Health Services and Outcomes Research Methodology, 12(1-2), 104-118.

Harwell, M. (2003). Summarizing Monte Carlo Results in Methodological Research: The Single-Factor, Fixed-Effects ANCOVA Case. Journal of Educational and Behavioral statistics, 28(1), 45-70.

Heckman, J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153-161.Heckman, J., y Hotz, V.J. (1989). Choosing among Alternative Nonexperimental Methods for

Estimating the Impact of Social Programs: The Case of Manpower Training. Journal of the American Statistical Association, 84(408), 862-874.

Heisman, D.T., y Shadish, W.R. (1996). Assignment methods in experimentation: When do nonrandomized experiments approximate answers from randomized experiments? Psychological Methods, 1(2), 154-169.

Huitema, B. E. (1980). The analysis of covariance and alternatives. New York: Wiley.Iacus, S.M., King, G., y Porro, G. (2012). Causal Inference without Balance Checking:

Coarsened Exact Matching. Political Analysis, 20(1), 1-24. Jo, B., y Stuart, E.A. (2009). On the use of propensity scores in principal causal effect

estimation. Statistics in Medicine, 28(23), 2857-75.Lash, T.L., Fox, M.P., y Fink, A.K. (2009). Applying Quantitative Bias Analysis to

Epidemiologic Data. New York: Springer.Luellen, J.K., Shadish, W.R., y Clark, M-H. (2005). Propensity Scores An Introduction and

Experimental Test. Evaluation Review, 29(6), 530-558.Magidson,J. (1977). Toward a causal model approach for adjusting for preexisting differences

in the nonequivalent control group situation. A general alternative to ANCOVA. Evaluation Review, 1(3), 399-420.

Mara, C., y Cribbie, R. A. (2012). Paired-samples tests of equivalence. Communications in Statistics: Simulation and Computation, 41(10), 1928-1943

Marcus, S.M., Stuart, E.A., Wang, P., Shadish, W.R., y Steiner, P.M. (2012). Estimating the causal effect of randomization versus treatment preference in a doubly randomized preference trial. Psychological Methods, 17(2), 244-254.

Marshall, A., Altman, D.G., Royston, P., y Holder, R.L. (2010). Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study. BMC Medical Research Methodololy,19, 10-7.

Maxwell, S.E., y Delaney, H.D. (1990). Designing experiments and analyzing data: A model comparison perspective. Belmont, CA: Wadsworth.

Maxwell, S.E., Delaney, H.D., y Dill, C.A. (1984). Another look at ANCOVA versus blocking. Psychological Bulletin, 95,136-147.

Maxwell, S.E., O’Callaghan, M.F., y Delaney, H.D. (1993). Analysis of covariance. En L. K. Edwards (Ed.), Applied analysis of variance in behavioral science (pp. 63–104). New York: Marcel Dekker.

McCaffery, K.J., Turner, R., Macaskill, P., Walter, S.D., Chan, S.F., y Irwig L. (2011). Determining the impact of informed choice: separating treatment effects from the effects of choice and selection in randomized trials. Medical Decision Making, 31(2), 229-236.

Mervis, C.B., y Klein-Tasman, B.P. (2004). Methodological issues in group-matching designs: α levels for control variable comparisons and measurement characteristics of control and target variables. Journal of Autism and Developmental Disorders, 34(1), 7-17.

Mervis, C.B., y Robinson, B.F. (1999). Methodological issues in cross-syndrome comparisons: Matching procedures, sensitivity (Se), and specificity (Sp). Commentary on M. Sigman y E. Ruskin, Continuity and change in the social competence of children with autism, Down syndrome, and developmental delays. Monographs of the Society for Research in Child Development, 64(256), 115-130.

Mervis, C.B., y Robinson, B.F. (2003). Methodological issues in cross-group comparisons of language and/or cognitive development. En Y. Levy y J. Schaeffer (Eds.), Language competence across populations: Toward a definition of specific language impairment (pp. 233-258). Mahwah, NJ: Lawrence Erlbaum.

Myers, J.A., y Louis, T.A. (2012). Comparing treatments via the propensity score: stratification or modeling? Health Services and Outcomes Research Methodology, 12 (1), 29-43.

Orsini, N., Bellocco, R., Bottai, M., Wolk, A., y Greenland, S. (2008). A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies. The Stata Jourrnal, 8(1), 29-48.

Ozminkowski, R.J., Wortman, P.M., y Roloff, D.W. (1989). Inborn/outborn status and neonatal survival: A meta-analysis of non-randomized studies. Statistics in Medicine, 7, 1207-1221.

Phillips, C.V. (2003). Quantifying and Reporting Uncertainty from Systematic Errors. Epidemiology, 14(4), 459-466.

Pirog, M.A., Buffardi, A.L., Chrisinger, C.K., Singh, P., y Briney, J. (2009). Are alternatives to randomized assignment nearly as good? Statistical corrections to nonrandomized evaluations. Journal of Policy Analysis and Management, 28, 169-172.

Pohl, S., Steiner, P. M., Eisermann, J., Soellner, R., y Cook, T. D. (2009). Unbiased causal inference from an observational study: Results of a within-study comparison. Educational Evaluation and Policy Analysis, 31(4), 463-479.

Porter, A.C., y Raudenbush, S.W. (1987). Analysis of covariance: its model and use in psychological research. Journal of Counseling Psychology, 34(4), 383-392.

Ramsahai, R.R., Grieve, R., y Sekhon, J.S. (2011). Extending iterative matching methods: an approach to improving covariate balance that allows prioritisation. Health Services and Outcomes Research Methodology, 11(4-5), 95-114.

Raudenbush, S.W., Martinez, A., y Spybrook J. (2007). Strategies for improving precision in group-randomized experiments.  Educational Evaluation and Policy Analysis, 29(1), 5-29.

Roberts, M.H., y Dalal, A.A. (2012). Clinical and economic outcomes in an observational study of COPD maintenance therapies: multivariable regression versus propensity score matching. International Journal of Chronic Obstructive Pulmononary, 7, 221-33.

Rogosa, D. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88, 307-321.

Rose, S., y van der Laan, M.J. (2009). Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation. The International Journal of Biostatistics, 5(1):1.

Rossi, P.H., y Freeman, H.E. (2004). Evaluation: A systematic approach (7th ed.). Newbury Park, CA: Sage Publications, Inc.

Sackett, D.L. (1979). Bias in analytic research. Journal of Chronic Diseases, 32, 51-63.Seeger, J.D., Kurth, T., y Walker, AM. Use of propensity score technique to account for

exposure-related covariates: an example and lesson. Medical Care, 45(10 Suppl. 2), S143-8.

Shadish, W.R., Matt, G.E., Navaro, A.M., y Phillips, G. (2000). The effects of psychological therapies under clinically representative conditions: A meta-analysis. Psychological Bulletin, 126, 512-529.

Shadish, W.R., y Ragsdale, K. (1996). Randomized versus quasi-experimental designs: A metaanalysis investigating the relationship between random assignment and effect size. Journal of Consulting and Clinical Psychology, 64, 1290-1305.

Smith, J., y Todd, P., 2004. Does matching overcome Lalonde’s critique of nonexperimental estimators. Journal of Econometrics, 125, 305-353.

Shadish, W.R., y Steiner, P.M. (2010). A Primer on Propensity Score Analysis. Newborn and Infant Nursing Reviews, 10, 19-26.

Steiner, P.M., Cook, T.D., y Shadish, W.R. (2011). On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores. Journal of Educational and Behavioral Statistics, 36, 213-236.

Steiner, P.M., Cook; T.D., Shadish, W.R., y Clark M.H. (2010). The importance of covariate selection in controlling for selection bias in observational studies. Psychological Methods. 15(3), 250-67.

Stuart, E.A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25(1), 1-21.

Stuart, E.A., y Jo, B. (2013). Assessing the sensitivity of methods for estimating principal causal effects. Statistical Methods in Medical Research, 4(3).

Sullivan, L.M., y D'Agostino, R.B. (2002). Robustness and Power of Analysis of Covariance Applied to Data Distorted from Normality by Floor Effects: Non-Homogeneous Regression Slopes. Journal of Statistical Computation and Simulation, 72 (2), 141-165.

Tchetgen E.J., y VanderWeele, T.J. (2012). On Identification of Natural Direct Effects when a Confounder of the Mediator is Directly Affected by Exposure. Collection of Biostatistics Research Archive (COBRA) Preprint Series. Artículo 93. Disponible en http://biostats.bepress.com/cobra/art93

Vach W. (1997). Some issues in estimating the effect of prognostic factors from incomplete covariate data. Statistics in Medicine, 16(1-3), 57-72.

VanderWeele, T.J. (2006). The use of propensity score methods in psychiatric research. International Journal of Methods in Psychiatric Research, 15, 95-103.

VanderWeele, T.J. (2008). The sign of the bias of unmeasured confounding. Biometrics, 64, 702-706.

VanderWeele, T.J. (2009). On the relative nature of over-adjustment and unnecessary adjustment. Epidemiology, 20, 496-499.

VanderWeele, T.J. (2012). Confounding and effect modification: distribution and measure. Epidemiologic Methods, 1, 55-82.

VanderWeele, T.J., y Arah, O.A. (2011). Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments and confounders. Epidemiology, 22, 42-52.

VanderWeele, T.J., y Shpitser, I. (2011). A new criterion for confounder selection. Biometrics, 67, 1406-1413.

VanderWeele, T.J., y Shpitser, I. (2011). On the definition of a confounder. Collection of Biostatistics Research Archive (COBRA) Preprint Series. Artículo 84. Disponible en http://biostats.bepress.com/cobra/art84/

Walter, S.D., Turner, R.M., Macaskill, P., McCaffery, K.J., y Irwig, L. (2012). Optimal allocation of participants for the estimation of selection, preference and treatment effects in the two-stage randomised trial design. Statistics in Medicine, 31(13), 1307-1322.

Wilde, E.T., y Hollister, R. (2007). How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment. Journal of Policy Analysis and Management, 26 (3), 455-477.

Willson, W.L. (2012). Examining Use of Covariate Interaction Models in AERJ Over 25 Years. Paper presented at the annual meeting of the Southwest Educational Research Association, San Antonio, TX. Disponible en http://sera-edresearch.org/wp-content/uploads/2011/08/2011ProgramFinal.pdf.

Winship, C., y Marc, R. D. (1992). Models for sample selection bias. Annual Review of Sociology, 18, 327-350.

Wortman, P.M. y Bryant, F.B. (1985). School desegregation and black achievement: An integrative review. Sociological Methods & Research, 13, 289-324.

Wunsch, H., Linde-Zwirble, W.T,, y Angus, D.C. (2006). Methods to adjust for bias and confounding in critical care health services research involving observational data. Journal of Critical Care, 21(1), 1-7.

7.- Datos faltantes: abandono de sujetos, no respuesta, pérdida de datos (concepto, control, prevención, cómo evitarlo, estimación de datos, etc.).

Abikoff, H., Arnold, L. E., Newcorn, J. H., Elliott, G. R., Hechtman, L., Severe, J. B., et al. (2002). Emergency/adjunct services and attrition prevention for randomized clinical trials in children: The MTA manual-based solution. Journal of the American Academy of Child & Adolescent Psychiatry, 41(5), 498-504.

Agodini, R., y Dynarski, M. (2001). Are experiments the only option? A look at dropout prevention programs. Document No. PR01-71. Princeton, NJ: Mathematica Policy Research, Inc. Disponible en http://www.mathematica-mpr.com/PDFs/experonly.pdf

Ahern, K., y Le Brocque, R. (2005). Methodological Issues in the Effects of Attrition: Simple Solutions for Social Scientists. Field Methods, 17(1), 53-69.

Akl, E.K., Briel, M., You, J.J., Lamontagne, F., Gangji, A., Cukierman, T. et al., (2009). LOST to follow-up Information in Trials (LOST-IT): a protocol on. Trials, 10:40.

Akl, E.K., Briel, M., You, J.J., Sun, X., Johnston, B.C., Busse, J.W., et al., (2012). Potential impact on estimated treatment effects of information lost to follow-up in randomised controlled trials (LOST-IT): systematic review. British Medical Journal, 344: e2809.

Altman, D.G., y Bland, J.M. (2007). Missing data. British Medical Journal, 334:424 Allison, P.D. (2000). Multiple imputation for missing data: a cautionary tale. Sociological

methods and Research, 28, 301-309.Allison, P.D. (2002). Missing Data. Thousand Oaks, CA: Sage.Amico, K.R. (2009). Percent Total Attrition: A Poor Metric for Study Rigor in Hosted

Intervention Designs. American Journal of Public Health, 99(9), 1567-1575. Amico, K.R., Harman, J.J., y O'Grady, M.A (2008). Attrition and related trends in scientific

rigor: a score card for ART adherence intervention research and recommendations for future directions. Current HIV/AIDS Reports, 5(4), 172-85.

Andersen, E. (2007). Participant retention in randomized, controlled trials: The value of relational engagement: International Journal for Human Caring, 11(4), 46-51.

Anstey, K. J., Hofer, S. M., y Luszcz, M. A. (2003). Cross-sectional and longitudinal patterns of dedifferentiation in late-life cognitive and sensory function: The effects of age, ability, attrition, and occasion of measurement. Journal of Experimental Psychology: General, 132(3), 470-487.

Armistead, L. P., Clark, H., Barber, C. N., Dorsey, S., Hughley, J., Favors, M., et al. (2004). Participant retention in the Parents Matter! Program: Strategies and outcome: Journal of Child and Family Studies, 13(1), 67-80.

Burton, A., y Altman, D.G. (2004). Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines. British Journal of Cancer, 91(1), 4-8.

Burton, A., Billingham, L.J., y Bryan, S. (2007). Cost-effectiveness in clinical trials: using multiple imputation to deal with incomplete cost data. Clinical Trials, 4, 154-161.

Callahan, J. L., Aubuchon-Endsley, N., Borja, S. E., y Swift, J. K. (2009). Pretreatment expectancies and premature termination in a training clinic environment. Training and Education in Professional Psychology, 3(2), 111-119.

Carpenter, J.R. (2009). Statistical modelling with missing data using multiple imputation. London School of Hygiene & Tropical Medicine. Support for JRC from ESRC, MRC, & German Research Foundation. Disponible en http://www.restore.ac.uk/Longitudinal/surveynetwork/documents/lecture1.pdf

Carpenter, J.R., Goldstein, H., y Kenward, M.G. (2011). REALCOM-IMPUTE software for multilevel multiple imputation with mixed response types. Journal of Statistical Software, 45(5), 1-14.

Carpenter, J. R., y Plewis, I. (2011). Coming to terms with non-response in longitudinal studies. En Malcolm Williams y Paul Vogt (Eds.), The SAGE handbook of Innovation in Social Research Methods. London

Cassidy, E. L., Baird, E., y Sheikh, J. I. (2001). Recruitment and retention of elderly patients in clinical trials: Issues and strategies. American Journal of Geriatric Psychiatry, 9(2), 136-140.

Christensen, H., y Mackinnon, A. (2006). The law of attrition revisited. Journal of Medical Internet Research, 8(3), 70-72.

Cotter, R. B., Burke, J. D., Loeber, R., y Mutchka, J. (2005). Predictors of contact difficulty and refusal in a longitudinal study. Criminal Behaviour and Mental Health, 15(2), 126-137.

Cotter, R. B., Burke, J. D., Stouthamer-Loeber, M., y Loeber, R. (2005). Contacting participants for follow-up: How much effort is required to retain participants in longitudinal studies?. Evaluation and Program Planning, 28(1), 15-21.

Crosby, R.A., Salazar, L.F., Di Clemente, R.J. y Lang, D.L. (2010). Balancing rigor against the inherent limitations of investigating hard-to-reach populations. Health Education Research, 25(1), 1-5.

Davis, M.J., y Addis, M.E. (1999). Predictors of attrition from behavioral medicine treatments. Annals of Behavioral Medicine, 21(4), 339-49.

Delucchi, K., y Bostrom, A. (1999). Small sample longitudinal clinical trial with missing data: A comparison of analytic methods. Psychological Methods, 4, 158–172.

Donders, A.R., van der Heijden, G.J., Stijnen, T. y Moons, K.G. (2006). Review: a gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59(10), 1087-91.

Eysenbach G. (2005). The law of attrition. Journal of Medical Internet Research, 7(1):e11.Eysenbach, G. (2006). The law of attrition revisited--Author's reply. Journal of Medical

Internet Research, 8(3), 73-74. Fielding, S., Fayers, P., y Ramsay, C. (2010). Predicting missing quality of life data that were

later recovered: an empirical comparison of approaches. Clinical Trials, 7(4):333-42. Fielding, S., Maclennan, G., Cook, J.A., y Ramsay, C.R. (2008). A review of RCTs in four

medical journals to assess the use of imputation to overcome missing data in quality of life outcomes. Trials, 11, 9:51.

Foster, E. M., y Fang, G. Y. (2004). Alternative Methods for Handling Attrition: An Illustration Using Data From the Fast Track Evaluation. Evaluation Review, 28(5), 434-464.

Frick, J. R., Goebel, J., Schechtman, E., Wagner, G. G., y Yitzhaki, S. (2006). Using analysis of Gini (ANOGI) for detecting whether two subsamples represent the same universe. Sociological Methods & Research, 34(4), 427-468.

Gerstein, D.R., y Johnson, R.A. (2000). Nonresponse and selection bias in treatment follow-up studies. Substance Use & Misuse, 35(6-8), 971-1014.

Grant, J. S., Raper, J. L., Kang, D.-H., y Weaver, M. T. (2008). Research participant recruitment and retention. New York, NY: Oxford University Press.

Grittner, U., Gmel, G., Ripatti, S., Bloomfield, K., y Wicki, M. (2011). Missing value imputation in longitudinal measures of alcohol consumption. International Journal of Methods in Psychiatric Research, 20(1), 50-61.

Gust, D.A., Mosimaneotsile, B., Mathebula, U., Chingapane, B., Gaul, Z., Pals, S.L., y Samandari, T. (2011). Risk factors for non-adherence and loss to follow-up in a three-year clinical trial in Botswana. PLoS One, 6(4):e18435.

Haapea, M., Miettunen, J., Veijola, J., Lauronen, E., Tanskanen, P., y Isohanni, M. (2007). Non-participation may bias the results of a psychiatric survey: An analysis from the survey including magnetic resonance imaging within the Northern Finland 1966 Birth Cohort. Social Psychiatry and Psychiatric Epidemiology, 42(5), 403-409.

Harel, O., Hofer, S. M., Hoffman, L., Pedersen, N., y Johansson, B. (2007). Population inference with mortality and attrition in longitudinal studies on aging: A two-stage multiple imputation method. Experimental Aging Research, 33(2), 187-203.

Harris, P. M. (1998). Attrition revisited. American Journal of Evaluation, 19(3), 293-305. Hofler, M., Pfister, H., Lieb, R., y Wittchen, H.-U. (2005). The use of weights to account for

non-response and drop-out. Social Psychiatry and Psychiatric Epidemiology, 40(4), 291-299.

Horton, N.J., y Kleinman, K.P. (2007). Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. The American Statistician, 61(1), 79–90.

Horton, N.J., y Lipsitz, S.R. (2001). Multiple imputation in practice: comparison of software packages for regression models with missing variables. The American Statistician, 55(3), 244–254.

Karahalios, A., Baglietto, L., Carlin, J.B., English, D.R., y Simpson, J.A. (2012). A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures. BMC Medical Research Methodology, 12: 96.

Kenward, M.G., y Carpenter, J. (2007). Multiple imputation: current perspectives. Statistical Methods in Medical Research, 16(3), 199-218.

Klebanoff, M.A., y Cole, S.R. (2008). Use of multiple imputation in the epidemiologic literature. American Journal of Epidemiology, 168, 355-357.

Knol, M.J., Janssen, K.J., Donders, A.R., Egberts, A.C., Heerdink, E.R., Grobbee, E., Moons, K.G., y Geerlings, M.I. (2010). Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example. Journal of Clinical Epidemiogy, 63(7), 728-36.

Leon, A. C., Mallinckrodt, C. H., Chuang-Stein, C., Archibald, D. G., Archer, G. E., y Chartier, K. (2006). Attrition in Randomized Controlled Clinical Trials: Methodological Issues in Psychopharmacology. Biological Psychiatry, 59(11), 1001-1005.

Levkoff, S., y Sanchez, H. (2003). Lessons Learned About Minority Recruitment and Retention From the Centers on Minority Aging and Health Promotion. The Gerontologist, 43(1), 18-26.

Lyons, K. S., Carter, J. H., Carter, E. H., Rush, K. N., Stewart, B. J., y Archbold, P. G. (2004). Locating and Retaining Research Participants for Follow-up Studies. Research in Nursing & Health, 27(1), 63-68.

Marcellus, L. (2004). Are We Missing Anything? Pursuing Research on Attrition. Canadian Journal of Nursing Research, 36(3), 83-98.

Marshall, A., Altman, D.G., Royston, P., y Holder, R.L. (2010). Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study. BMC Medical Research Methodololy, 19, 10-7.

Mazumdar, S., Tang, G., Houck, P. R., Dew, M. A., Begley, A. E., Scott, J., Mulsant, B.H., y Reynolds, C.F. (2007). Statistical analysis of longitudinal psychiatric data with dropouts. Journal of Psychiatric Research, 41(12), 1032-1041.

McFarlane, J. (2007). Strategies for successful recruitment and retention of abused women for longitudinal studies. Issues in Mental Health Nursing, 28(8), 883-897.

Nur, U., Shack, L.G., Rachet, B., Carpenter, J.R., y Coleman, M.P. (2010). Modelling relative survival in the presence of incomplete data: a tutorial. International Journal of Epidemiology, 39(1), 118-128.

O'Neil, K. M., Penrod, S. D., y Bornstein, B. H. (2003). Web-based research: Methodological variables' effects on dropout and sample characteristics. Behavior Research Methods, Instruments & Computers, 35(2), 217-226.

Overall, J. E., Tonidandel, S., y Starbuck, R. R. (2006). Rule-of-thumb adjustment of sample sizes to accommodate dropouts in two-stage analysis of repeated measurements. International Journal of Methods in Psychiatric Research, 15(1), 1-11.

Patrician, P.A. (2002). Multiple imputation for missing data. Research in Nursing & Health, 25(1), 76-84.

Perez, R. G., Ezpeleta, L., y Domenech, J. M. (2007). Features associated with the non-participation and drop out by socially-at-risk children and adolescents in mental-health epidemiological studies. Social Psychiatry and Psychiatric Epidemiology, 42(3), 251-258.

Rubin, D.B. (1996). Multiple imputation after 18 years. Journal of the American Statistical Association, 91, 473-490.

Rubin, D.B. (1987). Multiple imputation for non response in surveys. New York: Wiley.Schafer, J.L. (1997). Analysis of incomplete multivariate data. London: Chapman and Hall.Schafer, J.L., y Graham, J.W. (2002). Missing data: our view of the state of the art.

Psychological Methods, 7, 147-177.Siddiqi, A.E., Sikorskii, A., Given, C.W., y Given, B. (2008). Early participant attrition from

clinical trials: role of trial design and logistics. Clinical Trials, 5(4), 328-35.Simon, E.G., Fouché, C.J., y Perrotin F. (2012). Introduction to randomized trials: attrition bias.

Gynecologie Obstetrique & Fertilite, 40(1), 64-5. Sinharay, S., y Russell, D. (2001). The use of multiple imputation for the analysis of missing

data. Psychological Methods, 6, 317–329.Solberg, L. I., Plane, M. B., Brown, R. L., Underbakke, G., y McBride, P. E. (2002).

Nonresponse bias: Does it affect measurement of clinician behavior?. Medical Care, 40(4), 347-352.

Sonawalla, S. B., Farabaugh, A. H., Leslie, V. M., Pava, J. A., Matthews, J. D., y Fava, M. (2002). Early drop-outs, late drop-outs and completers: Differences in the continuation phase of a clinical trial. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 26(3), 415-419.

Sonawalla, S. B., Farabaugh, A. H., Leslie, V. M., Pava, J. A., Matthews, J. D., y Fava, M. (2002). Early drop-outs, late drop-outs and completers: Differences in the continuation phase of a clinical trial: Erratum. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 26(7-8), 1415-1419.

Spratt, M., Carpenter, J.R., Sterne, J.A.C., Carlin, J.B., Heron, J., Henderson, J., y Tilling, K. (2010). Strategies for Multiple Imputation in Longitudinal Studies. American Journal of Epidemiology, 172(4), 478-487.

Sterne, J.A.C., White, I.R., Carlin, J.B., Spratt, M., Royston, P., Kenward, M.G., Wood, A.M., y Carpenter, (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. British Medical Journal, 339, 157-160.

Taljaard, M., Donner, A., y Klar, N. (2008). Imputation strategies for missing continuous outcomes in cluster randomized trials. Biometrical Journal, 50(3), 29-45.

Vach W. (1997). Some issues in estimating the effect of prognostic factors from incomplete covariate data. Statistics in Medicine, 16(1-3), 57-72.

Valentine, J.C., y McHugh, C.M. (2007). The effects of attrition on baseline comparability in randomized experiments in education: a meta-analysis. Psychological Methods, 12(3), 268-82.

Von Hippel, P.T. (2009). How to impute interactions, squares and other transformed variables. Sociological Methodology, 39, 265-291.

Weiss, D. J. (1999). An analysis of variance test for random attrition. Journal of Social Behavior & Personality, 14(3), 433-438.

White, I.R., y Royston, P. (2009). Imputing missing covariate values for the cox model. Statistics in Medicine, 28(15), 1982-1998.

Wisniewski, S.R., Leon, A.C., Otto, M.W., y Trivedi, M.H. (2006). Prevention of missing data in clinical research studies. Biological Psychiatry, 59(11), 997-1000.

Wood, A.M., White, I.R., Hillsdon, M., y Carpenter, J. (2005). Comparison of imputation and modelling methods in the analysis of a physical activity trial with missing outcomes. International Journal of Epidemiology, 34(1), 89-99.

Wood, A.M., White, I.R., y Thompson, S.G. (2004). Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clinical Trials, 1(4), 368-76.

Yucel, R.M. (2011). State of the Multiple Imputation Software. Journal of Statistcical Software, 45(1), pii: v45/i01.

8.- Análisis de los datos por intención de tratar y por protocolo.

Anderson, R.A. (2012). Reply: Intention-to-treat and per-protocol analyses. Human Reproduction, 27 (10), 3118-3119.

Boutis, K., Willan, A., Babyn P., Goeree, R., y Howard, A. (2010). Cast versus splint in children with minimally angulated fractures of the distal radius: a randomized controlled trial. Canadian Medial Association Journal, 182, 1507-12.

Chakraborty, H., y Gu, H. (2009). A Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials with Missing Values. RTI Press publication No. MR-0009-0903. Research Triangle Park, NC: RTI International. Disponible en http://www.rti.org/pubs/mr-0009-0904-chakraborty.pdf

Chêne, G., Morlat, P., Leport, C., Hafner, R., Dequae, L., Charreau, I., Aboulker, J.P., Luft, B., Aubertin, J., Vildé, J.L., y Salamon, R. (2008). Intention-to-treat vs. on-treatment analyses of clinical trial data: experience from a study of pyrimethaminein the primary prophylaxis of toxoplasmosis in HIV-infected patients. Controlled Clinical Trials, 19, 233-48.

Fisher, L.D., Dixon, D.O., Hersonm,J., Frankowski, R.K., Hearron, M.S., y Peace, K.E.(1990). Intention-to-Treat in clinical trials, en K.E Peace (Ed.), Statistical Issues in Drug Research and Development. Marcel Dekker.

Gupta, S.K. (2011). Intention-to-treat concept: A review. Perspectives in Clinical Research, 2(3), 109–112.

Guyatt, G.H., Rennie, D., Meade, M.O., y Cook, D.J. (Eds.). (2008). User’s Guide to The Medical Literature. (2 nd. Ed.). New York: McGraw Hill. A manual for evidence-based Clinical Practice. Desponible Online en http://jmvertiz.posgrado.unam.mx/pmdcmos02/convocatorias/Users_guide_medical_literature.pdf

Guyatt, G.H., y Rennie, D. (2002). The Principle of Intention to Treat. In User’s Guide to The Medical Literature. Chicago, IL: American Medical Association Press.

Hollis, S., y Campbell, F. (1999). What is meant by intention to treat analysis? Survey of published randomised controlled trials. British Medical Journal, 319, 671-674.

Hyun Ja Lim, H.J., y Hoffmann, R.G. (2007). Study Design: The Basics. Methods in Molecular Biology, 40, 1-17.  

Hsieh, P., Acee, T., Chung, WH., Hsieh, YP., Kim, H., Thomas, G.D., You, Ji., Levin, J.R., y Robinson, D.H. (2005). Is Educational Intervention Research on the Decline?. Journal of Educational Psychology, 97(4), 523–529.

Lachin, J.M. (2000). Statistical Considerations in the Intent-to-Treat Principle. Controlled Clinical Trials, 21 (3), 167-189.

Mazumdar, S., Houck, P. R., Lui, K. S., Mulsant, B. H., Pollock, B. G., Dew, M. A., y Reynolds, C.F. (2002). Intent-to-treat analysis for clinical trials: Use of data collected after termination of treatment protocol. Journal of Psychiatric Research, 36(3), 153-164

Moher, D., Schulz, K.F., Altman, D.G., y CONSORT GROUP (Consolidated Standards of Reporting Trials). (2001). The CONSORT statement: Revised recommendations for improving the quality of reports of parallel-group randomized trials. Annals of Internal Medicine, 134, 657-62.

Montedori, A., Bonacini, M.I., Casazza, G., Luchetta, M.L., Duca, P., Cozzolino, F., y Abraha, I. (2011). Modified versus standard intention-to-treat reporting: are there differences in methodological quality, sponsorship, and findings in randomized trials? A cross-sectional study. Trials 28(12), 58.

Polit, D. F., y Gillespie, B. M. (2010). Intention-to-treat in randomized controlled trials: Recommendations for a total trial strategy. Research in Nursing & Health, 3(4), 355 - 368.

Schultz, K.F., y Grimes, D.A. (2002). Sample size slippages in randomized trials: exclusions and the lost and wayward. The Lancet, 359, 781-785.

Sedgwick, P. (2011). Analysis by per protocol. British Medical Journal, 342:d2330 Shah, P.B. (2011). Intention-to-treat and per-protocol analysis. Canadian Medial Association

Journal, 183(6): 696. White, I.R., Horton, N.J., Carpenter, J., y Pocock, S.J. (2011). Strategy for intention to treat

analysis in randomised trials with missing outcome data. British Medical Journal, 342:d40.

White, I.R., Carpenter, J., y Horton, N.J. (2012). Including all individuals is not enough: Lessons for intention-to-treat analysis. Clinical Trials, 9(4), 396-407.

West, S.G., Duan, N., Pequegnat, W., Gaist, P., Des Jarlais, D.C., Holtgrave, D., Szapocznik, J., Fishbein, M., Rapkin, B., Clatts, M., y Mullen, P.D. (2008). Alternatives to the Randomized Controlled Trial. American Journal of Public Health, 98(8), 1359-1366.

8.- Aplicación y Evaluación de programas de intervención

Altschuld, J.W., y Kumar, D.D. (2010). Needs assessment. An overview. Thousand Oaks: Sage.Anguera, M.T., y Chacón, S. (2008). Aproximación conceptual en evaluación de programas. En

M.T. Anguera, S. Chacón y A. Blanco (Eds.), Evaluación de programas sociales y sanitarios: un abordaje metodológico (pp. 17-36). Madrid: Síntesis.

Avellar, S., y Paulsell, D. (2011). Lessons Learned from the Home Visiting Evidence of Effectiveness Review Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. Washington, DC. Disponible online en http://homvee.acf.hhs.gov/Lessons_Learned.pdf.

Bandy, T., y Moore, K.A. (2011). What Works for Promoting and Enhancing Positive Social Skills: Lessons from Experimental Evaluations of Programs and Interventions. (Research-to-Results Brief). Washington, DC: Child Trends. Disponible en http://www.childtrends.org/Files//Child_Trends_2011_03_02_RB_WWSocialSkills.pdf

Bickman, L. (2000). Summing up program theory. New Directions for Evaluation, 87, 103-112. Bloom, H.S. (2006). Learning More from Social Experiments: Evolving Analytic Approaches.

New York: Russell Sage Foundation.Bloom, H.S., Michalopoulos, C., Hill, C.J., y Lei, Y. (2002). Can Nonexperimental

Comparison Group Methods Match the Findings from a Random Assignment Evaluation of Mandatory Welfare-to-Work Programs?. MDRC Working Papers on Research Methodology. Disponible online en http://www.mdrc.org/publications/66/abstract.html.

Blunkett, D. (2000). Influence or Irrelevance? Can Social Science Improve Government? Secretary of States ESRC Lecture 2. Londres: DFEE.

Bozeman, W., y Addair, J. (2010). Strategic planning for change and improvement in career and technical education. Techniques, 85(1), 10-12.

Castro, F.G., Barrera, M., y Holleran Steiker, L.K. (2010). Issues and challenges in the design of culturally adapted evidence-based interventions. Annual Review of Clinical Psychology, 6, 213-239.

Cook, T.D. (2000). The false choice between theory-based evaluation and experimentation. New Directions for Evaluation, 87, 27-34.

Cook, T.D. (2007). ¿Por qué los investigadores que realizan evaluación de programas y acciones educativas eligen no usar experimentos aleatorizados? Paedagogium, 35.

Cook, T.D., Scriven, M., Coryn, C.L.S., y Evergreen, S.D.H. (2010). Contemporary Thinking About Causation in Evaluation: A Dialogue With Tom Cook and Michael Scriven. American Journal of Evaluation, 31(1), 105-117.

Eastmond, N. (1998). Commentary: When Funders Want to Compromise Your Design. American Journal of Evaluation, 19(3), 392-395.

Ellis, T.J., y Levy, Y. (2009). Towards a guide for novice researchers on research methodology: Review and proposed methods. Issues in Informing Science and Information Technology, 6, 323-337.

Fernández-Ballesteros, R. (1995). El proceso de evaluación de programas. En R. Fernández-Ballesteros (Ed.), Evaluación de programas. Una guía práctica en ámbitos sociales, educativos y de salud (pp. 75-113). Madrid: Síntesis.

Fitz-Gibbon, C.T., y Morris, L.L. (1978). How to design a program evaluation. Beverly Hills, CA: Sage Publications.

Funnell, S.C., y Rogers, P.J. (2011). Purposeful program theory: Effective use of theories of change and logic models (Research Methods for the Social Sciences). San Francisco: John Wiley & Sons.

Gysbers, N.C., y Henderson, P. (2012). Developing & Managing Your School Guidance & Counseling Program (5th Ed.). Virginia: American Counseling Association.

Hacsi, T.A. (2000). Using program theory to replicate successful programs. New Directions for Evaluation, 87, 71-78.

Hamilton, J., y Bronte-Tinkew, J. (2007). Logic models in out-of-school time programs. (Research-to-Results Brief). Washington, DC: Child Trends. Disponible en http://www.childtrends.org/Files//Child_Trends-2007_01_05_RB_LogicModels.pdf.

Hansen, M., Alkin, M.C., y Wallace, T.L. (2013). Depicting the logic of three evaluation theories. Evaluation and Program Planning, 38, 34-43.

Heckman, J.J. (1989). Causal Inference and Nonrandom Samples. Journal of Educational Statistics, 14(2), 159-68.

Heckman, J.J., y Hotz, V.J. (1989). Choosing among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training. Journal of the American Statistical Association, 84(408), 862-74.

Heckman, J.J., Moon, S.H., Pinto, R., Savalyev, P., y Yavitz, A. (2010). Analyzing Social Experiments as Implemented: A Reexamination of the Evidence from the Highscope Perry Preschool Program. Quantitative Economics, 1(1), 1-46.

Heckman, J.J. , y Robb, R. (1989). The Value of Longitudinal Data for Evaluating the Impact of Treatments on Outcomes. En D. Kasprzyk, G. Duncan, G. Kalton y M.P. Singh, Panel Surveys (pp. 512-538). New York: Wiley.

Hunter, D.E.K. (2006). Daniel and the rhinoceros. Evaluation and Program Planning, 29, 180-185.

Lamont, M., y White, P. (2005). Interdisciplinary Standards for Systematic Qualitative Research. Cultural Anthropology, Law and Social Science, Political Science, and Sociology Programs. National Science Foundation. Disponible online en http://www.nsf.gov/sbe/ses/soc/ISSQR_rpt.pdf#page=70.

Leedy, P.D., y Ormrod, J.E. (2010). Practical research: Planning and design (9th ed.). Upper Saddle River, NJ: Prentice Hall.

Nadler, G. (1981). The planning and design approach. Hoboken, NJ: John Wiley & Sons.Nadler, G., y Chandon, W.J. (2004). Smart questions: Learn to ask the right questions for

powerful results. San Francisco: Jossey-Bass. Reichardt, C.S. (2011). Evaluating Methods for Estimating Program Effects. American Journal

of Evaluation, 32( 2), 246-272.Rogers, P.J. (2000). Causal models in program theory evaluation. New Directions for

Evaluation, 87, 47-55.Rogers, P.J. (2008). Using programme theory to evaluate complicated and complex aspects of

interventions. Evaluation 14 (1), 29-48. Rosas, S.R., y Kane, M. (2012). Quality and rigor of the concept mapping ethodology: A

pooled study analysis. Evaluation and Program Planning, 35, 236-245. Rossi, P.H., Lipsey, M.W., y Freeman, H.E. (2004). Evaluation: A systematic approach (7th

ed.). Thousand Oaks, CA: SAGE Publications.Scheirer, M.A. (1998). Commentary: Evaluation Planning is The Heart of the Matter. American

Journal of Evaluation, 19(3), 385-391.Shadish, W.R., Newman, D.L., Scheirer, M.A.,y Wye, C. (Eds.), (1995). Guiding principles for

evaluators (New Directions for Program Evaluation, San Francisco: Jossey- Bass.

Slavin, R.E. (2008). What works? Issues in synthesizing educational program evaluations. Educational Researcher, 37, 5-14.

Sloane, F. (2008). Comments on Slavin: Through the Looking Glass: Experiments, Quasi-Experiments, and the Medical Model. Educational Researcher, 37, 41-46.

Society for Prevention Research. (2010). Standards of evidence: Criteria for efficacy, effectiveness, and dissemination. Disponible online en http://www.preventionresearch.org/StandardsofEvidencebook.pdf.

Song, M., y Herman, R. (2010). Critical Issues and Common Pitfalls in Designing and Conducting Impact Studies in Education: Lessons Learned From the What Works Clearinghouse (Phase I). Educational Evaluation and Policy Analysis, 32, 351-371.

Sridharan, A., y Nakaima, A. (2011). Then steps to making evaluation matter. Evaluation and Program Panning, 34(2), 135-146.

Stufflebeam, D.L., y Shinkfield, A.J. (2007). Evaluation theory, models, and applications. San Francisco: Jossey-Bass.

Subhrendu, P. (2009). Rough guide to impact evaluation of environmental and development programs. SANDEE working paper / South Asian Network for Development and Environmental Economics (SANDEE), no. 40-09. Disponible online en http://idl-bnc.idrc.ca/dspace/bitstream/10625/41844/1/129483.pdf.

Trochim, W.M.K. (1984). Research designs for program evaluation: The regression discontinuity approach. Newbury Park, CA: Sage.

Valentine, J.C., y Cooper, H. (2008). A systematic and transparent approach for assessing the methodological quality of intervention effectiveness research: the Study Design and Implementation Assessment Device (Study DIAD). Psychologial Methods, 13, 130-49.

World Health Organization, WHO. (1981). Evaluación de los programas de salud. Normas fundamentales. Serie Salud para todos, 6. Geneva: WHO.

10.- Metodología Mixta

Brannen, J. (2005). Mixing Methods: The Entry of Qualitative and Quantitative Approaches into the Research Process. International Journal of Social Research Methodology, 8, 173-184.

Caracelli, V.J., y Cooksy, L.J. (2013). Incorporating Qualitative Evidence in Systematic Reviews: Strategies and Challenges, New Directions for Evaluation, 138, 97-108.

Collins, K.M.T., y Onwuegbuzie, A.J. (2013). Establishing Interpretive Consistency When Mixing Approaches: Role of Sampling Designs in Evaluations. New Directions for Evaluation, 138, 85-95.

Creswell, J. (2004). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. New York: Prentice Hall.

Greene, J. C. (2007). Mixed methods in social inquiry. San Francisco: Jossey-Bass.Greene, J.C. (2013). Reflections and Ruminations. New Directions for Evaluation, 138, 109-

119.Greene, J.C. (2008). Is mixed methods social inquiry a distinctive methodology? Journal of

Mixed Methods Research, 2(1), 7-22.Greene, J., Benjamin, L., y Goodyear, L. (2001). The merits of mixing methods in evaluation.

Evaluation, 7(1), 25-44.

Greene, J.C., Caracelli, V.J., y Graham, W.F. (1989). Toward a conceptual framework for mixed-method evaluation design. Educational Evaluation and Policy Analysis, 11(3), 255-274.

Greene, J. C., Kreider, H., y Mayer, E. (2005). Combining qualitative and quantitative methods in social inquiry. Enn B. Somekh y C. Lewin (Eds.), Research methods in the social sciences (pp. 274-281). London: Sage.

Hall, J.N. (2013). Pragmatism, Evidence, and Mixed Methods Evaluation. New Directions for Evaluation, 138, 15-26.

Hesse-Biber, S. (2013). Thinking Outside the Randomized Controlled Trials Experimental Box: Strategies for Enhancing Credibility and Social Justice. New Directions for Evaluation, 138, 49-60.

Johnson, R.B., Onwuegbuzie, A.J., y Turner, L.A. (2007). Toward a Definition of Mixed Methods Research. Journal of Mixed Methods Research, 1, 112-133.

Lesley, A. (2012). Designing and Doing Survey Research. London: Sage. Lieber, E., y Weisner T. S. (2010). Meeting the Practical Challenges of Mixed Methods

Research. In A. Tashakkori & C. Teddlie (Eds.), Mixed Methods in Social & Behavioral Research (2nd Ed.), (pp. 559-611). Thousand Oaks, CA; SAGE Publications.

Lowenthal, P.R., y Leech, N. (2009). Mixed research and online learning: Strategies for improvement. In T. T. Kidd (Ed.), Online education and adult learning: New frontiers for teaching practices (pp. 202-211). Hershey, PA: IGI Global.

Maxcy, S.J. (2003). Pragmatic threads in mixed methods research in the social sciences: The search for multiple modes of inquiry and the end of the philosophy of formalism. In A.Tashakkori y C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 51-89). Thousand Oaks, CA: Sage.

Mertens, D.M. (2013). What Does a Transformative Lens Bring to Credible Evidence in Mixed Methods Evaluations?. New Directions for Evaluation, 138, 27-35.

Mertens, D.M., y Hesse-Biber, S. (2013). Mixed Methods and Credibility of Evidence in Evaluation. New Directions for Evaluation, 138, 5-13.

Mingers J., y Brocklesby J. (1997). Multimethodology: Towards a Framework for Mixing Methodologies, Omega, 25, 489-509.

Onwuegbuzie, A.J. y Johnson, R.B. (2006). The Validity Issue in Mixed Research. Research in the Schools, 13 (1), 48-63.

Onwuegbuzie, A., y Leech, N. (2005). Taking the “Q” Out of Research: Teaching Research Methodology Courses Without the Divide Between Quantitative and Qualitative Paradigms. Quality and Quantity, 39, 267-296.

Sandelowski, M., Voils, C. I., y Barroso, J. (2006). Defining and designing mixed research synthesis studies. Research in the Schools, 13(1), 29-40.

Schram, S. F. y Brian, C. (Eds.) (2006). Making Political Science Matter: Debating Knowledge, Research, and Method. New York: New York University Press.

White, H. (2013). The Use of Mixed Methods in Randomized Control Trials. New Directions for Evaluation, 138, 61-73.

11.- Modelos Multinivel o Modelos jerárquicos

Aitkin, M., y Longford, N. (1986). Statistical Modelling Issues in School Effectiveness Studies. Journal of the Royal Statistical Society, Ser A, 149, 1-43.

Andreu, J. (2011). El Análisis Multinivel: Una Revisión Actualizada en el Ámbito Sociológico. Metodología de Encuestas, 13, 161-176.

Aparicio, A., y Morera, M. (2007). La Conveniencia del Análisis Multinivel para La Investigación en Salud: Una Aplicación para Costa Rica. Población y Salud en Mesoamérica, 4(2), 1-23.

Ato, M., Losilla, J.M., Navarro, J.B., Palmer, A.L., y Rodrigo, M.F. (2000). Modelo Lineal Generalizado. Terrassa: CBS.

Baker, J., Clark, T., Maier, K.S., y Viger, S. (2008). The differential influence of instructional context on the academic engagement of students with behavior problems. Teaching and Teacher Education, 24(7), 1876-1883.

Bauer, D.J. (2003). Estimating Multilevel Linear Models as Structural Equation Models. Journal of Educational and Behavioral Statistics, 28, 135-167.

Bauer, D.J., Preacher, K.J., y Gil, K.M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142-163.

Beretvas, S.N. (2008). Cross-classified random effects models. In A.A. O’Connell and D.B. McCoach (Eds.), Multilevel modeling of educational data (pp. 161-197). Charlotte, NC: Information Age Publishing.

Beretvas, S.N., Meyers, J.L., y Rodriguez, R.A. (2005). The cross-classified multilevel measurement model: An explanation and demonstration. Journal of Applied Measurement, 6(3), 322-341.

Bickel, R. (2007). Multilevel Analysis for Applied Research: It´s just Regression. New York: Guilford Press.

Borich, G. (2009). Effective Teaching Methods. Upper Saddle River, NJ: Merril Pub Co.Bryk, A.S., y Raudenbush, S.W. (1987). Application of hierarchical linear models to assessing

change. Psychological Bulletin, 101, 147-158.Bryk, A.S., y Raudenbush, S. W. (1988). Heterogeneity of variance in experimental studies: A

challenge to conventional interpretations. Psychological Bulletin 104 (3), 396-404. Bryk, S.W., y Raudenbush, A.S. (2002). Hierarchical linear models : applications and data

analysis methods (3 rd. ed.). California, Sage Publications.Burnham, K.P., y Anderson, D.R. (2002).Model Selection and Multimodel Inference: A

Practical Information-Theoretic Approach. New York: Springer Verlag. Claeskens, G., y Hjort, N.L. (2008).Model Selection and Model Averaging. New York:

Cambridge University Press.Cnaan, A., Laird, N.M., Slasor, P. (2007).Using the General Linear Mixed Model to Analyse

Unbalanced Repeated Measures and Longitudinal Data. Statistics in Medicine, 16, 2349-2380.

Cox, R.B., Blow, A.J., Maier, K.S., y Parra Cardona, J.R. (2010). Covariates of substance-use initiation for Venezuelan Youth: Using a multilevel approach to guide prevention programs. Journal of Studies on Alcohol and Drugs, 71(3), 424-433.

Curran, P.J. (2003). Have Multilevel Models been Structural Equation Models all along?. Multivariate Behavioral Research, 38, 529-569.

Curran, P.J., y Bauer, D.J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583-619.

Curran, P.J., y Hussong, A.M. (2003). The use of latent trajectory models in psychopathology research. Journal of Abnormal Psychology, 112, 526-544.

Curran, P.J., y Wirth, R.J. (2004). Inter-individual differences in intra-individual variation: Balancing internal and external validity. Measurement: Interdisciplinary Research and Perspectives, 2, 219-227.

De Leeuw, J. y Meijer, E. (2008). Introduction to Multilevel Analysis. En J. De Leeuw and E. Meijer (Eds.), Handbook of Multilevel Analysis (pp.1-75). New York: Springer.

Fidell, B.G., y Tabachnick, L.S. (2007). Using multivariate statistics (5th ed. ed.). Boston; Pearson.

Fitzmaurice, G.M., Laird, N.M., y Ware, J.H. (2004). Applied Longitudinal Analysis. New York: John Wiley & Sons.

Garson, G.D. (Ed),. Hierarchical linear modeling : guide and applications. Thousand Oaks, CA: Sage Publications.

Gelman A., y Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press.

Goldstein, H. (1987). Multilevel Models in Educational and Social Research. New York: Oxford University Press.

Goldstein, H. (1989). Models for Multilevel Response Variables With An Application To Growth Curves. In R. D. Bock (Ed.), Multilevel Analysis of Educational Data. San Diego, CA: Academic Press.

Goldstein, H. (2011). Multilevel Statistical Models (4th ed). London: Wiley.Gueorguieva, R (2001). A multivariate generalized linear mixed model for joint modelling of

clustered outcomes in the exponential family. Statistical modeling, 1(3), 177-193.Heck, R.H. y Thomas, S.L. (2000). An Introduction to Multilevel Modeling Techniques.

Mahwah, NJ: Lawrence Erlbaum Associates.Heck, R.H., Thomas, S.L., y Tabata, L.N. (2010). Multilevel and longitudinal modeling with

IBM SPSS. New York: Routledge. Hox, J.J. (2010). Multilevel analysis: Techniques and applications (2nd ed). Hogrefe and

Huber.Hox, J.J., y Kreft, I.G. (1994). Multilevel Analysis Methods. Sociological Methods and

Research, 22(3), 238-299.Hsieh, C.A., y Maier, K.S. (2009). An international comparative study of educational

inequality: A preliminary Bayesian analysis of longitudinal data from a small sample. International Journal of Research and Method in Education, 32(1), 103-125.

Kim, J. S. (2009). Multilevel Analysis: An Overview and some Contemporary Issues. In R.E. Millsap and A. Maydeu-Olivares (Eds.), The SAGE Handbook of Quantitative Methods in Psychology (pp. 337-361).London: Sage.

Kozlowski1, S.W.J., Chao, G.T., Grand, J.A., Braun, M.T., y Kuljanin, G. (2013). Advancing Multilevel Research Design: Capturing the Dynamics of Emergence Organizational Research Methods, doi: 10.1177/1094428113493119

Leeuw, I.K.J. (1998). Introducing multilevel modeling. London: Sage Publications Ltd. Littell, R.C., Pendergast, J., y Natarajan, R. (2000). Modelling Covariance Structure in the

Analysis of Repeated Measures Data. Statistics in Medicine, 19, 1793-1819.Luke, D.A. (2004). Multilevel modeling (3 rd. ed.). Thousand Oaks, CA: Sage Publications. Luo, W., y Kwok, O.M. (2009). The impacts of ignoring a crossed factor in analyzing cross-

classified data. Multivariate Behavioral Research, 44(2), 182-212.Mathieu, J.E. y Chen, G. (2011). The Etiology of the Multilevel Paradigm in Management

Research Journal of Management, 37, 610-641.

Meyers, J.L., y Beretvas, S.N. (2006). The impact of inappropriate modeling of cross-classified data structures. Multivariate Behavioral Research, 41(4), 473-497.

Molenberghs, G. (2007). Random-effects models for multivariate repeated measures. Statistical Methods in Medical Research, 16(5), 387-397.

Raudenbush, S.W., y Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). (Advanced Quantitative Techniques in the Social Sciences Series, No. 1). Newbury Park, CA: Sage.

Raudenbush, S.W., Bryk, A.S., Cheong, Y.F., Congdon, R., y Du Toit, M. (2011). HLM 7: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International.

Reise, S.P., y Duan, N. (2003). Multilevel Modeling: Methodological Advances, Issues, and Applications. Hillsdale, NJ: Lawrence Erlbaum Associates.

Sammel, M., Lin, X., y Ryan, L. (1999). Multivariate linear mixed models for multiple outcomes. Statistics in Medicine, 18(17-18), 2479-2492.

Schulz, W., y Maas, I. (2010). Studying historical occupational careers with multilevel growth models. Demographic Research, 23(24), 669-696.

Singer, J.D. (2002). Fitting Individual Growth Models Using SAS PROG MIXED. In D.S. Moskowitz & S.L. Hershberger (Eds.), Modeling Intraindividual Variability With Repeated Measures Data: Methods and Applications (pp. 135-170). Mahwah, NJ: Lawrence Erlbaum Associates.

Singer, J.D., y Willet, J.B. (2003).Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press.

Snijders, T.A B., y Bosker, R.T. (2011).Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelling.(2nd. Ed.). London: Sage.

Vallejo, G., Fernández, J.R., y Secades, R. (2003). Análisis Estadístico y Consideraciones de Potencia en la Evaluación de Programas Mediante Diseños de Muestreo de Dos Etapas. Psicothema, 15(2), 300-308.

Vallejo, G., Fernández, J. R., y Secades, R. (2004). Application of A Mixed Model Approach for Assessment of Interventions and Evaluation Programs. Psychological Reports, 95, 1095-1118.

Vallejo, G., Arnau, J., Bono, R., Fernández, P., y Tuero, E. (2010). Selección de Modelos Anidados para Datos Longitudinales Usando Criterios de Información y la Estrategia de Ajuste Condicional. Psicothema, 22(2), 323-333.

Villarroel, L. (2009). Cluster analysis using multivariate mixed effects models. Statistics in Medicine, 28(20), 2552-2565.

Wallace, D., y Green, B. S. (2002). Analysis of Repeated Measures Designs With Linear Mixed Models. In D.S. Moskowitz and S.L. Hershberger (Eds.), Modeling Intraindividual Variability With Repeated Measures Data: Methods and Applications (pp. 135-170). Mahwah NJ: Lawrence Erlbaum Associates.

Vaughn, B.K., y Wang, Q. (2009). Classification based on hierarchical linear models: The need for incorporation of social contexts in classification analysis. Journal on Educational Psychology, 3(1), 34-42.

Wright, S.P. (1998). Multivariate analysis using the MIXED procedure. Paper presented at the Twenty-Third Annual Meeting of SASUsers’ Group International, Nashville, TN(Paper 229). Disponible en http://www2.sas.com/proceedings/sugi23/Stats/p229.pdf

Wu, Y.W., Clopper, R. R., y Wooldridge, P.J.(1999).A Comparison of Traditional Approaches to Hierarchical Linear Modeling when Analyzing Longitudinal Data.Research in Nursing & Healt, 22, 421-432.

12.- Modelos de Ecuaciones Estructurales

Bagozzi, R., y Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40 (1), 8-34.

Bergsma, W., Croon, M.A., y Hagenaars, J.A. (2009). Causal Analyses: Structural Equation Models and (Quasi-)Experimental Designs. En W. Bergsma, M. Croon y J.A. Bollen, K. (1989). Structural Equations with Latent Variables. New York: John Wiley & Sons.

Bergsma, W., Croon, M.A., y Hagenaars, J.A. (2009). Marginal Models Marginal Models: For Dependent, Clustered, and Longitudinal Categorical Data (Statistics for Social and Behavioral Sciences). New York: Springer.

Cooper, H.M.; Hedges, L.V. y Valentine, J.C. (2009). The Handbook of Research Synthesis and Meta-Analysis, (2nd ed). New York: Russell Sage Foundation.

Goldberger, A.S., y Duncan, O.D. (1973). Structural equation models in the social sciences. New York: Seminar Press.

Goldstein, H., Bonnet, G., y Rocher, T. (2007). Multilevel Structural Equation Models for the Analysis of Comparative Data on Educational Performance. Journal of Educational and Behavioral Statistics, 32, (3), 252-86.

Hair, J.F.G., Hult, T.M., Ringle, C.M., y Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks: Sage.

Hayes, A.F., y Preacher, K.J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45, 627-660.

Heckman, J., y Vytlacil, E. (2005). Structural equations, treatment effects and econometric policy evaluation. Econometrica, 73, 669-738.

Hershberger, S.L. (2003). The growth of structural equation modeling: 1994-2001. Structural Equation Modeling, 10(1), 35-46.

Holbert, R.L., y Stephenson, M.T. (2002). Structural equation modeling in the communication sciences, 1995-2000. Human Communication Research, 28, 531-551.

Holland, P. (1988). Causal inference, path analysis, and recursive structural equations models. En C. Clogg (ed.). Sociological Methodology (pp. 449-484). Washington: American Sociological Association.

Holland, P.W. (1988). Causal Inference, Path Analysis, and Recursive Structural Equations Models. Sociological Methodology, 18, 449-484.

Hoyle, R.H (1995). Structural Equation Modeling: Concepts, Issues, and Applications. Thousand Oaks, California: SAGE.

Jackson, D.L. (2010). Reporting results of latent growth modeling and multilevel modeling analyses: some recommendations for rehabilitation psychology. Rehabilitation Psychology, 55, 272-85.

Kahn, J.H. (2011). Multilevel modeling: overview and applications to research in counseling psychology. Journal of Counseling Psychology, 58(2), 257-71.

Kaplan, D (2000). Structural Equation Modeling: Foundations and Extensions. Thousand Oaks, California : SAGE .

Kline, R.B. (2010). Principles and Practice of Structural Equation Modeling (3rd Edition). New York: The Guilford Press.

Lauritzen, S. (1996). Graphical Models. Oxford: Clarendon Press. Lauritzen, S. (2001). Causal inference from graphical models. En D. Cox y C. Kluppelberg

(eds.) In Complex Stochastic Systems. Boca Raton: Chapman and Hall/CRC Press.

Little, T.D., Card, N.A., Bovaird, J.A., Preacher, K.J., y Crandall, C.S. (2007). Structural equation modeling of mediation and moderation with contextual factors. In T.D. Little, J.A. Bovaird, y N.A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 207-230). Mahwah, NJ: Lawrence Erlbaum Associates.

Loehlin, J.C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis (4th ed.). Mahwah, NJ: Lawrence Erlbaum.

McArdle, J.J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60, 577-605.

MacCallum, R.C., y Austin, J.T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201-222.

Martens, M.P. (2005). The use of structural equation modeling in counseling psychology research. The Counseling Psychologist, 33, 269-298.

Myers, N.D., Wolfe, E.W., Maier, K.S., Feltz, D.L., y Reckase, M.D. (2006). Extending validity evidence for multidimensional measures of coaching competency. Research Quarterly for Exercise and Sport, 77(4), 451-463.

Nokelainen, P., y Ruohotie, P. (2009). Non-linear Modeling of Growth Prerequisites in a Finnish Polytechnic Institution of Higher Education. Journal of Workplace Learning, 21(1), 36-57.

Peugh, J.L. (2010). A practical guide to multilevel modeling. The Journal of School Psychology, 48(1), 85-112.

Preacher, K.J. (2006). Quantifying parsimony in structural equation modeling. Multivariate Behavioral Research, 41, 227-259.

Preacher, K.J. (2006). Testing complex correlational hypotheses using structural equation modeling. Structural Equation Modeling, 13, 520-543.

Preacher, K.J. (2011). Multilevel SEM strategies for evaluating mediation in three-level data. Multivariate Behavioral Research, 46, 691-731.

Preacher, K.J., Curran, P.J., y Bauer, D.J. (2006). Computational tools for probing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437-448.

Preacher, K.J., Zhang, Z., y Zyphur, M.J. (2011). Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM. Structural Equation Modeling, 18, 161-182.

Preacher, K.J., Zyphur, M.J., y Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209-33.

Pui-Wa, L., y Qiong W. (2007). Introduction to Structural Equation Modeling: Issues and Practical Considerations. Educational Measurement: Issues and Practice, 26(3), 33-43.

Raykov, T., y Marcoulides, G. (2000). A First Course in Structural Equation Modeling. Mahwah, NJ: Lawrence Erlbaum Associates.

Shah, R., y Goldstein, S.M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 24(1), 148-169.

Steyer, R., Eid, M., y Schwenkmezger, P. (1997). Modeling true intraindividual change: True change as a latent variable. Methods of Psychological Research, 2, 21-34.

Teo, T (2011). Using structural equation modeling (SEM) in educational research: Practices and Issues. International Journal of Applied Educational Studies, 10(1), 49-65.

Teo, T., y Khine, M.S. (2009). Structural Equation Modeling in Educational Research. Concepts and Applications. Boston: Sense publishers.

Tomarken, A.J, Waller, N.G. (2005). Structural equation modeling: strengths, limitations, and misconceptions. Annual Review of Clinical Psychology , 1, 31-65.

Schreiber, J.B. (2008). Core reporting practices in structural equation modeling. Research in Social and Administrative Pharmacy, 4(2), 83-97.

Schumacker, R.E., y Lomax, R.G. (2004). A Beginner's Guide to Structural Equation Modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

Ulf Kröhne, J. (2010). Estimation of Average Total Effects in Quasi-Experimental Designs: Nonlinear Constraints in Structural EquationModels. Tesis doctoral Disponible online en http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-23269/Kr%C3%B6hne/Dissertation.pdf.

VanderWeele, T. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology, 20, 18-26.

Violato, C., y Hecker, K. G. (2007). How to use structural equation modeling in medical education research: a brief guide. Teaching and Learning in medicine, 19(4), 362-371.

von Oertzen, T. (2010). Power equivalence in structural equation modelling. British Journal of Mathematical and Statistical Psychology, 62(2), 257,272.

Werts, C.E., y Linn, R.L. (1970). Path analysis: Psychological examples. Psychological Bulletin, 74, 193-212.

Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9, 476-487.

Zhang, Z., Zyphur, M.J., y Preacher, K.J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12, 695-719.