References:

Categorical Data Analysis

Agresti, A. (2002). Categorical data analysis. Second edition. New York: John Wiley & Sons.

Agresti, A. (1996). An introduction to categorical data analysis. New York: Wiley.

Hosmer, D. W. & Lemeshow, S. (2000).  Applied logistic regression.  Second edition.  New York: John Wiley & Sons.

Long, S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage.



Causal Analysis
Brown C. H. (2003).  Design principles and their application in preventive field trials.  In WJ Bukoski and Z Sloboda, Handbook of Drug Abuse Prevention: Theory, Science, and Practice.  New York: Plenum Press, pp. 523-540.

Holland, P. W. (1986).  Statistics and causal inference. Journal of the American Statistical Association, 81, 945-970.

Hong & Raudenbush (2006). Evaluating kindergarten retention policy: a case study of causal inference for multi-level observation data.

Raudenbush, S. W., Hong G. & Rowan B. (2002). Studying the causal effects of instruction with application to primary-school mathematics.

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

Rubin, D. B. (2002). Basic concepts of statistical inference for causal effects in experiments and observational studies.

Rubin, D. B. (2004). Direct and indirect causal effects via potential outcomes. Scand. J. Statistics, 31, 161-170.





Criteria for Efficacy, Effectiveness and Dissemination

Flay, B. R., et. al. (2005). Standards of evidence: Criteria for efficacy,effectiveness and dissemination. Prevention Science, 6, 151-175.




Designs
Brown, C.H. & Liao, J. (1999). Principles for designing randomized preventive trials in mental health: An emerging development epidemiologic perspective. American Journal of Community Psychology, special issue on prevention science, 27, 673-709.

David Murray (1998). Design and Analysis of Group-Randomized Trials. New York, NY: Oxford University Press.



Genetic Analysis

Kendler, K.S. (2005). Psychiatric genetics: a methodologic critique. Am. J. Psychiatry, 162, 3-11.

Growth and Growth Mixture Analysis

Brown, E.C., Catalano, C.B., Fleming, C.B., Haggerty, K.P. & Abbot, R.D. (2005). Adolescent substance use outcomes in the Raising Healthy Children Project: A two-part latent growth curve analysis. Journal of Consulting and Clinical Psychology, 73, 699-710.

Clark, D. B., et. al. (2005). Fluoxetine for the treatment of childhood anxiety disorders: Open-label, long-term extension to a controlled trail. J. Am. Acad. child. Adolesc. Psychiatry, 44, 1263-1270.

Muthén, B., Brown, C.H., Masyn, K., Jo, B., Khoo, S.T., Yang, C.C., Wang, C.P., Kellam, S., Carlin, J., & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475.

Muthén, B. & Curran, P. (1997). General longitudinal modeling of individual differences in experimental designs: a latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402.

Olsen, M. K. and Schafer, J. L. (2001). A two-part random-effects model for semi-continuous longitudinal data. Journal of the American Statistical Association, 96, 730-745.



Mediation Analysis
Kraemer, H. C., et. al. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Arch Gen Psychiatry, 59, 877-883.

MacKinnon and Dwyer (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144-158.

MacKinnon, Lockwood, et. al. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.

Orlando, M., et. al. (2005). Mediation analysis of a school-based drug prevention program: Effects of project alert. Prenvention Science, 6, 35-46.

Shrout, P.E. & Bolger, N. (2002).  Mediation in experimental and nonexperimental studies: New procedures and recommendations.  Psychological Methods, 7, 422-445.



Multilevel Analysis

Hedeker, D. (2004). An introduction to growth modeling. D. Kaplan (Ed.), Quantitative Methodology for the Social Sciences. Thousand Oaks CA: Sage Publications.

Kellam, S.G. (1998). The effect of the level of aggression in the first grade classroom on the course and malleability of aggressive behavior into middle school. Development and Psychopathology, 10, 165-185.

Murray, D. (1998). Design and Analysis of Group-Randomized Trials. New York, NY: Oxford, University Press.

Muthén, B. & Khoo, S.T. (1998). Longitudinal studies of achievement growth using latent variable modeling. Learning and Individual Differences, Special issue: Latent growth curve analysis, 10, 73-101.

Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods. Second edition. Newbury Park, CA: Sage Publications.

Rumberger, R. W. & Palardy, G. J. (2004). Multilevel models for school effectiveness research. D. Kaplan (ed.), Handbook of quantitative methodology for the social sciences.
Newbury Park, CA: Sage Publications.

Seltzer, M. (2004). The use of hierarchical models in analyzing data from experiments and quasi-experiments conducted in field settings. D. Kaplan (ed.), Handbook of quantitative methodology for the social sciences, 345-368. Newbury Park, CA: Sage Publications.

Snijders, T. & Bosker, R. (1999). Multilevel analysis. An introduction to basic and advanced multilevel modeling. Thousand Oakes, CA: Sage Publications.



Propensity Score Analysis

Blackstone, E. H (2002). Comparing apples and oranges. J. of Thoracic and Cardiovascular Surgery, 123, 8-15.

D'Agostino, R. B., Jr.(1998). Tutorial in biostatistics: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265-2281.

Rosenbaum, P. R. (1986). Dropping out of high school in the United States: An observational study. Jounal of Educational Statistics, 11, 207-224.

Rubin, D.B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Service & Outcomes Research Methodology, 2, 169-188.



Quasi-Experimental Designs

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



Survival Analysis

Clark, T.G., Bradburn, M. J., Love, S.B. and Altman, D.G. (2003). Survival Analysis Part I: Basic concepts and first analyses. Bri. J. Cancer, 89, 232-238.

Bradburn, M. J., Clark, T.G., Love, S.B. and Altman, D.G. (2003). Survival Analysis Part II: Multivariate data analysis - an introduction to concepts and methods. Bri. J. Cancer, 89, 431-436.

Bradburn, M. J., Clark, T.G., Love, S.B. and Altman, D.G. (2003). Survival Analysis Part III: Multivariate data analysis - choosing a model and assessing its adequacy and fit. Bri. J. Cancer, 89, 605-611.

Clark, T.G., Bradburn, M. J., Love, S.B. and Altman, D.G. (2003). Survival Analysis Part IV: further concepts and methods in survival analysis. Bri. J. Cancer, 89, 781-786.

Muthén, B. & Masyn, K. (2005). Discrete-time survival mixture analysis. Journal of Educational and Behavioral Statistics, 30, 27-28.

Singer, J.D. and Willet, J.B. (1993). It's about time: Using discrete- time survival analysis to study duration and the timing of events. J. of Educational Statistics, 18, 155-195.