Psychology 6073 Experimental Design Course Outline
Instructor: Jorge L. Mendoza
Office: 736 Dale Hall, Psychology
Phone: 325-4568, Home 321- 4676
Office Hours: TR 11-12 am
Website: http://www.ou.edu/faculty/M/Jorge.L.Mendoza-1
email: Jmendoza@ou.edu
one-way random & two-way mixed examples
groups-by-trials repeated measures design examples
Multilevel data set from the British Study
Questions for the British Study
Textbooks:
Reasonable Accommodations: Any student who has a disability that may prevent him or her from fully displaying his or her abilities in this course should contact me as soon as possible to discuss accommodations necessary to ensure full participation and facilitate your educational opportunities.
Basic Course Description: This is a course about experimental designs, requiring previous coverage of ANOVA from a models perspective. Also, you should be comfortable with matrix notation and have a basic understanding of matrix algebra. The course has two major components: major designs and hierarchical linear models. We will also discuss the logic of experimentation and causation in non-experimental designs. In addition, we will touch on the analysis of time depended data.
The major designs considered are the completely randomized design, the randomized block design, the factorial design, the mixed design and the repeated measures design. You will be prepare for each design by reading, writing notes, and finding research examples. You will find an example of each design and bring them to class for class discussion. Make a copy of the article and bring it to class. As you consider each design you should keep in mind the description, layout, model, assumptions, efficiency, as well as advantages and disadvantages. Also, you will be required to turn in a written two-page summary and evaluation of the article. The evaluation should focus on the statistics and experimental design aspects of the research. In addition to these evaluations, you will be asked to run a number of data analyses using SAS and write a proposal involving a technique or design studied in class.
Grade: In class mid-term (100), class projects (100), proposal and presentation (100), take-home Final (100).
List of Topics
Bases for experimentation, experimental design and modeling causation
References:
Timed Events:
Traditional ANOVA Models
References:
Mixed factor designs
References:
Repeated measures
References:
Hierarchical Linear Models (Mixed Models)
References:
Advanced Issues in HLM
References:
Reference List
1. Behrens, J. (1997) Principles and procedures of exploratory data analysis. Psychological Methods, pp 131-160.
2. Jex, S. M. & Bliese, P. D. (1999) Efficacy beliefs as a moderator of the impact of work-related stressors: A multilevel study. Journal of Applied Psychology, ,84, pp.349-361.
3. Cook, C. D., & Campbell, D. T. Quasi-Experimentation: Design & Analysis Issues for Field Settings. Rand McNally, 1979.
4. Kirk, R. E. Experimental Design, 3rd Ed., Brooks-Cole, 1995.
5. Myers, J.L. Fundamentals of Experimental Design. Allyn and Bacon, 1979
6. Keppel, G. Design & Analysis: A Researcher's Handbook. 2nd Ed. Prentice-Hall, 1982
7. Singer, J. D. (1998) Using SAS Proc Mixed to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, pp. 291-322.
8. Rogan, J. C., Keselman, H. J., & Mendoza, J. L.,(1979) Analysis of repeated measurements. British Journal of Mathematical and Statistical Psychology 32, pp. 269-286.
9. Mueller, K. E. and Barton, C. N. (1989) Approximate power for repeated-measures ANOVA lacking sphericity. Journal of the American Statistical Association, 84, pp. 549-555.
10. Lecoutre, B. (1991) A correction for the approximate test in repeated measures designs with two or more independent groups. Journal of Educational Statistics, 16, 371-372.
11. Raudenbush, S. W., Rowan, B, & kang, S. J. (1991) A multilevel, multivariate model for studying school climate with estimation via the EM algorithm and application to U. S. high-school data. Journal of Educational Statistics, 16, 295-230.
12. Timm, N. H. and Mieczkowski. Univariate & Multivariate General Linear Models: Theory and Applications Using SAS Software. SAS Institute, 1997
13. Chen, R. S., & Dunlap, W. P. (1994) A monte carlo study of a performance on a corrected formula for , suggested by Lecoutre. Journal of Educational and Behavioral Statistics, 19, pp. 119-126.
14. Keselman, H. J. (1994) Stepwise and simultaneous multiple comparison procedures of repeated measures’ means. Journal of Educational and Behavioral Statistics, 19, pp. 127-162.
15. Rasbash, J and Goldstein, H. (1994) Efficient analysis of mixed hierarchical and cross-classified random structures using a multilevel model. Journal of Educational and Behavioral Statistics, 19, pp. 337-350
16. Goldstein, H. Multilevel Statistical Models, 2nd Ed., John Wiley & Sons, 1995.
17. Journal of Educational and Behavioral Statistics (1995) Special Issue on Hierarchical Linear Models: Problems and Prospects.20, pp. 109-240. This is a whole issue on HLM.
18. Raudenbush, S. W. & Willms, J. D. (1995) The estimation of school effects. Journal of Educational and Behavioral Statistics, 20, pp. 307-335.
19. Barton, S. (1994) Chaos, self-organization, and psychology. American Psychologist, 49,pp.5-14
20. Halasz, M. (1995) Nonlinear dynamics in behavioral systems. American Psychologist, 50,pp.107-108.
21. Mandel, D. R. (1995) Chaos theory, sensitive dependence, and the logistic equation. American Psychologist, 50,pp.106-107.
22. Wilkinson, L. & Task Force (1999) Statistical methods in psychology journals: guidelines and explanations. American Psychologist, 54, pp. 594-604.
23. Rosenthal, R. & Rubin, D. (1982) A simple, general purpose display of magnitude of experimental effect. Journal of Educational Psychology, 74, 166-169.
24. Rosenthal, R. & Rubin, D. (1994) The counternull value of an effect size: A new statistic. Psychological Science, 5, 329-334.
25. Hanges, Paul J.; Braverman, Eric P.; Rentsch, Joan R. Changes in raters' perceptions of subordinates: A catastrophe model. Journal of Applied Psychology. 1991 Dec Vol 76(6) 878-888
26. Schafer, J. L. & Olsen, M. K. Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective. Multivariate Behavioral Research, 33, 545-571.
27. Greenhouse, J. B., Stangl, D. & Bromberg, J. An introduction to survival analysis: Statistical methods for analysis of clinical trial data. Journal of Consulting & Clinical Psychology. 1989 Aug Vol 57(4) 536-544.
28. Morita, June G.; Lee, Thomas W.; Mowday,
Richard T. Introducing survival analysis to organizational researchers: A
selected application to turnover research. Journal of Applied Psychology.
1989 Apr Vol 74(2) 280-292.