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Published byNaomi Goodman Modified over 9 years ago
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1 30 pts 20 pts Assignments vMWM Drop lowest test score 105 130 235 Revised Grading Scheme
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2 Chapters 12 & 15 And so much more
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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
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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)
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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
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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?
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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
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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!
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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
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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)
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13 Variations of the ABA Design A Swedish design that only made sense in the drug-induced haze of the 70s disco era.
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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?
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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”
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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
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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.
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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.
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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?
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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?
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21 Multivariate Designs Involve multiple variables studied concurrently –MANOVA (multiple DVs) –Multiple correlation –Factor analysis
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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
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23 Nonsignificant results You should reconsider: The experimental hypothesis The procedures used in the study The possibility that a Type 2 error occurred
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