Education 255C - Statistical Methods for
School-Based Intervention Studies
Syllabus, Handouts & Suggested Readings
Week 1 (1/9, 1/11):
Lecture 1: Overview of course website. Introductory example.
Lecture 2: Regression and
path analysis. ANCOVA.
Suggested Readings:
MacKinnon and Dwyer (1993). Estimating mediated effects in prevention studies.
Evaluation Review, 17, 144-158.
Shrout, P.E. & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422-445.
Week 2 (1/16, 1/18): Growth modeling
Monday: Holiday
Lecture 3: How should the baseline be used? Introductory growth modeling.
Suggested Readings:
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.
Hedeker, D. (2004). An introduction to growth modeling. D. Kaplan (Ed.), Quantitative Methodology for the Social Sciences. Thousand Oaks CA: Sage Publications.
Week 3 (1/23, 1/25): Growth modeling
Lecture 4: Introductory growth modeling continued
Lecture 5: Further practical issues in growth modeling
Suggested Readings:
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.
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.
Week 4 (1/30, 2/1): Growth modeling & logistic regression
Lecture 6: Advanced growth models
Lecture 7: Logistic regression
Suggested Readings:
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.
Books on logistic regression.
Week 5 (2/6, 2/8): Logistic regression and growth mixture analysis.
Lecture 8: Logistic regression continued
Lecture 9: For Whom Is An Intervention Effective?. Growth mixture analysis
Suggested Readings:
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.
van Lier, P. A. C., Muthén, B. O., van der Sar, R. M., & Crijnen, A. A. M. (2004). Preventing disruptive behavior in elementary schoolchildren: Impact of a universal, classroom based intervention. Journal of Consulting and Clinical Psychology, 72, 467-478.
Week 6 (2/13, 2/15):
Lecture 10: Growth mixture modeling continued.
Lecture 11: Mixtures and local solutions. Growth mixture modeling continued: LCGA vs GMM. Randomized trials with non-compliance.
Suggested Readings:
Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.
Little, R.J. & Yau, L.H.Y. (1998). Statistical techniques for analyzing data from prevention trials: treatment of no-shows using Rubin’s causal model. Psychological Methods, 3, 147-159.
Week 7 (2/20, 2/22):
Monday (2/20): Holiday
Lecture 12 (2/22): "Poduska (AIR) presentation: Going to scale with the Whole Day First Grade Program". Abstract. Presentation.
Suggested Readings:
Flay, B. R., et. al. (2005). Standards of evidence: Criteria for efficacy,effectiveness and dissemination. Prevention Science, 6, 151-175.
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
Week 8 (2/27, 3/1): Multilevel analysis
Lecture 13 (2/27): Analysis with multilevel data. Multilevel regression.
Lecture 14 (3/1): Myers (Mathematica) presentation: Randomization designs in school settings.
Suggested Readings:
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.
Week 9 (3/6, 3/8):
Lecture 15 (3/6): Multilevel ANCOVA. Multilevel growth modeling.
Lecture 16( 3/8): Survival analysis. Overview of mixture modeling. Overview of Mplus. Overview of models and software for intervention analysis.
Suggested Readings:
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.
Week 10 (3/13, 3/15): Causal inference. Power Estimation.
Lecture 17(3/13): Causal inference. Video of Rubin's causal inference course. Power estimation.
Lecture 18 (3/15): Propensity score. Solution to Assignment 6. Missing data.
Suggested Readings:
Blackstone, E. H (2002). Comparing apples and oranges. J. of Thoracic and Cardiovascular Surgery, 123, 8-15.
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.
Brown, C. H (2004). Research design, measurement, and data analytic issues.
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.
Hong & Raudenbush (2006). Evaluating kindergarten retention policy: a case study of causal inference for multi-level observation data.
Kraemer, H. C., et. al. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Arch Gen Psychiatry, 59, 877-883.
Raudenbush, S. W., Hong G. & Rowan B. (2002). Studying the causal effects of instruction with application to primary-school mathematics.
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.
Rubin, D. B. (2004). Direct and indirect causal effects via potential outcomes. Scand. J. Statistics, 31, 161-170.
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