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Random Effects Repeated Measures

Questions. What is the difference between fixed- and random-effects in terms of treatments? How are F tests with random effects different than with fixed effects? Describe a concrete example of a randomized block design. You should have 1 factor as the blocking factor and one other factor as the

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Random Effects Repeated Measures

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    1. Random Effects & Repeated Measures Alternatives to Fixed Effects Analyses

    2. Questions What is the difference between fixed- and random-effects in terms of treatments? How are F tests with random effects different than with fixed effects? Describe a concrete example of a randomized block design. You should have 1 factor as the blocking factor and one other factor as the factor of main interest.

    3. Questions (2) How is a repeated measures design different from a totally between subjects design in the collection of the data? How does the significance testing change from the totally between to a design to one in which one or more factors are repeated measures (just the general idea, you don’t need to show actual F ratios or computations)? Describe one argument for using repeated measures designs and one argument against using such designs (or describe when you would and would not want to use repeated measures).

    4. Fixed Effects Designs All treatment conditions of interest are included in the study All in cell get identical stimulus (treatment, IV combination) Interest is in specific means Expected mean squares are (relatively) simple; F tests are all based on common error term.

    5. Random Effects Designs Treatment conditions are sampled; not all conditions of interest are included. Replications of the experiment would get different treatments Interest in the variance produced by an IV rather than means Expected mean squares relatively complex; the denominator for F changes depending on the effect being tested.

    6. Fixed vs. Random

    7. Single Factor Random The expected mean squares and F-test for the single random factor are the same as those for the single factor fixed-effects design.

    8. Experimenter effects (Hays Table 13.4.1)

    10. Random Effects Significance Tests (A & B random/within)

    11. Why the Funky MS? Treatment effects for A, B, & AxB are the same for fixed & random in the population of treatments. In fixed, we have the population, in random, we just have a sample. Therefore, in a given (random) study, the interaction effects need not sum to zero. The AxB effects appear in the main effects.

    12. Applications of Random Effects Reliability and Generalizability How many judges do I need to get a reliability of .8? How well does this score generalize to a particular universe of scores? Intraclass correlations (ICCs) Estimated variance components Meta-analysis Control (Randomized Blocks and Repeated Measures)

    13. Review What is the difference between fixed- and random-effects in terms of treatments? How are F tests with random effects different than with fixed effects?

    14. Randomized Blocks Designs A block is a matched group of participants who are similar or identical on a nuisance variable Suppose we want to study effect of a workbook on scores on a test in research methods. A major source of nuisance variance is cognitive ability We can block students on cognitive ability.

    15. Randomized Blocks (2) Say 3 blocks (slow, average, fast learners) Within each block, randomly assign to workbook or control. Resulting design looks like ordinary factorial (3x2), but people are not assigned to blocks. The block factor is sampled, i.e., random. The F test for workbook is more powerful because we subtract nuisance variance. Unless blocks are truly categorical, a better design is analysis of covariance, described after we introduce regression.

    16. Randomized Blocks (3)

    17. Review Describe a concrete example of a randomized block design. You should have 1 factor as the blocking factor and one other factor as the factor of main interest. Describe a study in which Depression is a blocking factor.

    18. Repeated Measures Designs In a repeated measures design, participants appear in more than one cell. Painfree study Sports instruction Commonly used in psychology

    19. Pros & Cons of RM

    20. RM – Participant ‘Factor’

    21. Drugs on Reaction Time

    22. Total SS

    23. Drug SS

    24. Person SS

    25. Summary

    26. SAS

    27. 2 Factor, 1 Repeated

    28. Summary

    29. SAS & Post Hoc Tests

    30. Assumptions of RM

    31. Review How is a repeated measures design different from a totally between subjects design in the collection of the data? How does the significance testing change from the totally between to a design to one in which one or more factors are repeated measures (just the general idea, you don’t need to show actual F ratios or computations)? Describe one argument for using repeated measures designs and one argument against using such designs (or describe when you would and would not want to use repeated measures).

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