Analytic methods for questions pertaining to a randomized pretest, posttest, follow-up design

Journal of Clinical Child and Adolescent Psychology

Volumn 32, Issue 3, 2003, Pages 467-486

  Rausch, Joseph R ^a   Maxwell, Scott E ^a   Kelley, Ken ^a  

^a UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF VARIANCE; ARTICLE; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; FOLLOW UP; HUMAN; METHODOLOGY; RANDOMIZED CONTROLLED TRIAL; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ANALYSIS OF VARIANCE; DATA INTERPRETATION, STATISTICAL; FOLLOW-UP STUDIES; HUMANS; LINEAR MODELS; MODELS, STATISTICAL; RANDOMIZED CONTROLLED TRIALS; RESEARCH DESIGN;

EID: 0043231294     PISSN: 15374416     EISSN: None     Source Type: Journal    
DOI: 10.1207/S15374424JCCP3203_15     Document Type: Article

References (24)

1. Bryant, J. L., & Paulson, A. S. (1976). An extension of Tukey's method of multiple comparisons to experimental designs with concomitant variables. Biometrika, 63, 631-638.
2. Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147-158.
3. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago: Rand McNally.
4. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
5. Delucchi, K., & Bostrom, A. (1999). Small sample longitudinal clinical trial with missing data: A comparison of analytic methods. Psychological Methods, 4, 158-172.
6. Friedman, L. M., Furberg, C., & DeMets, D. L. (1998). Fundamentals of clinical trials (3rd ed.). New York: Springer-Verlag.
7. Gatsonis, C., & Sampson, A. R. (1989). Multiple correlation: Exact power and sample size calculations. Psychological Bulletin, 106, 516-524.
8. Hendrix, L. J., Carter, M. W., & Hintze, J. L. (1979). A comparison of five statistical methods for analyzing pretest-posttest designs. Journal of Experimental Education, 47, 96-102.
9. Huck, S. W., & 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, 511-518.
10. Huitema, B. E. (1980). The analysis of covariance and alternatives. New York: Wiley.
11. Kenny, D. A. (1979). Correlation and causality. New York: Wiley-Interscience.
12. Levin, J. R., Serlin, R. C., & Seaman, M. A. (1994). A controlled, powerful multiple-comparison strategy for several situations. Psychological Bulletin, 115, 153-159.
13. Lord, F. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68, 304-305.
14. Matthews, J. N. S. (2000). An introduction to randomized controlled clinical trials. London: Arnold.
15. Maxwell, S. E., & Delaney, H. D. (1990). Designing experiments and analyzing data: A model comparison perspective. Belmont, CA: Wadsworth.
16. Maxwell, S. E., Delaney, H. D., & Dill, C.A. (1984). Another look at ANCOVA versus blocking. Psychological Bulletin, 95, 136-147.
17. Maxwell, S. E., O'Callaghan, M. F., & Delaney, H. D. (1993). Analysis of covariance. In L. K. Edwards (Ed.), Applied analysis of variance in behavioral science (pp. 63-104). New York: Marcel Dekker.
18. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.
19. Rausch, J. R., & Maxwell, S. E. (2003). Longitudinal designs in randomized group comparisons: Optimizing power when the latent individual growth trajectories follow straight-lines. Manuscript in preparation.
20. Rogosa, D. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88, 307-321.
21. Rogosa, D., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92, 726-748.
22. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
23. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton-Mifflin.
24. Sinharay, S., & Russell, D. (2001). The use of multiple imputation for the analysis of missing data. Psychological Methods, 6, 317-329.