30 pts

1. Revised Grading Scheme 30 pts 20 pts Assignments vMWM Drop lowest test score 130 105 235

2. Chapters 12 & 15 And so much more

3. Large and Small N designs Small N • one or a few subjects Large N • Greater than a few subjects (often multiple groups) • most common technique used in research design

4. Large N Designs • Gained in popularity after Sir Ronald Fisher invented the analysis of variance in the 1930s • Easier to generalize with more than one subject (greater external validity)

5. Why even use small N? • Precision – pooling or combining data can obscure the results of individual subjects • You may miss effects by pooling data across individuals. Subject 1 Subject 2 Combined

6. Why even use small N? • Another example where pooling data led to a misinterpretation of what subjects had or had not learned? • Hint: a series of water maze studies on the effects of partial reinforcement (PR) • How many subjects in the PR group? • What data was pooled? • What was discovered by de-aggregating the data? • What’s the big picture lesson?

7. The BIG PICTURE lesson • Large N’s aggregate over subjects. • Smaller N studies sometimes aggregate over time. • Both have the potential to loose fidelity Mirriam-Webster Online a: the quality or state of being faithful b: accuracy in details :exactness 2: the degree to which an electronic device (as a record player, radio, or television) accurately reproduces its effect (as sound or picture) From Wikipedia, the free encyclopedia High fidelity (disambiguation) High fidelity or hi-fi is most commonly a term for the high-quality reproduction of sound or images

8. Small N Designs • Also used for practical reasons • Only a few patients in clinical research for a rare disease, plenty with common ones • Animals may be expensive (especially those fancy rats) • So, it’s ideal for poor researchers with restricted or limited access to human patients and/or those that may lack motivation to collect acceptable amounts of data in order to do a real study deemed credible by other scientific peers! Just the crowd I want to hang around and get advice from

9. Small N Designs Popular in: • Clinical and animal research • Laboratory and field studies • Psychophysics • Studies of learning • Used most extensively in operant conditioning research

11. ABA Design • The return to baseline in the ABA design tests whether B had an effect or whether another extraneous variable confounded the study. • Thus, the effect of B, the experimental treatment, must be reversible • it is also called a reversal design

12. Variations of the ABA Design • ABABA – two treatments and two returns to baseline – can detect cumulative effects of the treatment • ABACADA – multiple experimental conditions - B, C and D represent different treatments • AB design – sacrifice the return to baseline if it would harm the subject (e.g., behavior modification worked in reducing self-injurious behavior)

13. Variations of the ABA Design A Swedish design that only made sense in the drug-induced haze of the 70s disco era.

14. Variations of the ABA Design • Multiple baseline design – a series of baselines and treatments are compared, but once a treatment is established it is not withdrawn (e.g. AAABBB no more As) • Discrete trials design – does not rely on baselines at all, but compares performance across treatment conditions (e.g. BCDE) a BC design would be analogous to what large N design?

15. Variations of the ABA Design AC/DC – a.k.a, the “Indiscrete trials design” • After “A”, never return to baseline • skip all the boring B condition stuff and go right for the CDC conditions that put you on a fast track to the land down-under… • Apply thunderbolt between C and D.

16. B. F. Skinner • Studied changes in the rate of behavior (e.g., a rat lever pressing for food) • by careful,continuous measurement of a single subject over time. The control and experimental conditions are given to the same subject at different times A Baseline B Experimental A Baseline

17. Evaluating the Experiment • Internal validity – was the experiment free of confounding? • Manipulation check – assesses how successfully the experimenter manipulated the situation she or he intended to produce. • Pact of ignorance – subjects who have guessed the hypothesis might try to hide the fact because they know that their data might be discarded.

18. Statistical problems • Statistical conclusion validity – are conclusions about the statistical results valid? • Did you use an appropriate test? • Too many a priori tests – increases the chance of making a Type 1 error. • Small effect size – the results can be significant but not very meaningful if the effect size is small.

19. External validity • Two requirements: • The experiment is internally valid • And can be replicated What form of validity is a prerequisite for another form of validity?

20. Research significance • Are the results consistent with prior studies? • Do the results extend our knowledge of the problem? • Are there any implications for broader theoretical issues?

21. Multivariate Designs • Involve multiple variables studied concurrently • MANOVA (multiple DVs) • Multiple correlation • Factor analysis

22. Unobtrusive measures • Specific procedures for measuring a subjects behavior without them knowing that their behavior is being measured • Greater external validity because the behavioral data is similar to behavior occurring outside the experiment • E.g., a field experiment • Manipulate antecedent conditions • Observe outcomes in natural setting

23. Nonsignificant results You should reconsider: • The experimental hypothesis • The procedures used in the study • The possibility that a Type 2 error occurred