1 30 pts 20 pts Assignments vMWM Drop lowest test score Revised Grading Scheme

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 1Subject 2Combined

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) exactness From Wikipedia, the free encyclopedia High fidelity (disambiguation) High fidelityHigh 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) Just the crowd I want to hang around and get advice from 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!

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

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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 After “A”, never return to baselineAfter “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…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.Apply thunderbolt between C and D. AC/DC – a.k.a, the “Indiscrete trials design”

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