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Lecture Notes

by: Valerie Ho

Lecture Notes 209

Valerie Ho
GPA 3.53
research methods
Jacqueline Pickrell

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research methods
Jacqueline Pickrell
Class Notes
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This 6 page Class Notes was uploaded by Valerie Ho on Thursday October 8, 2015. The Class Notes belongs to 209 at University of Washington taught by Jacqueline Pickrell in Spring 2015. Since its upload, it has received 17 views. For similar materials see research methods in Psychlogy at University of Washington.


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Date Created: 10/08/15
Psych209 Lecture 5272815 QuasiExperimental Designs Designs with a nonequivalent control group 0 Selection emerges as a major threat to internal validity 0 Selection may interact with other threats ie selection interaction Ethics 0 Sometimes selling the results business and understanding the effect science con ict 0 Even if nothing is known about the pretest differences the company will want to sell the results Design problems in Applied Research Problems unique to BSD Nonequivalent groups selection problems Even matching can lead to regression problems Problems unique to WSD May not be able to counterbalance properly Attrition over a long period of time Contamination Problem that olccurs if there is communication about an experiment between groups of participants assigned to different conditions May lead to Demoralization Compensation 0 Diffusion of Treatment Interrupted Time Series Researchers examine a series of observations both before and after a treatment Simple interrupted timeseries design DV repeatedly measures Simple interrupted timeseries with nonequivalent control group A series of preand posttreatment scores are obtained for a treatment group and a nonequivalent control group 28515 From last lecture QuasiExperiments Psych209 Lecture 5272815 Nonequivalent Control Group design Little League Coaching Smoll et al 1993 Interrupted Times Series desidn New York example Small N Designs and Case Studies Review of Case Studies Detailed description and analysis of a single individual 0 quotCase Historyquot 0 Most common in clinical work and neuropsychology Considered qualitative narratives Famous Case Studies Darwin observations and notes on his son Freud l Theory of personality developed from his observations of his patients Piaget Child development and its stages Phineas Gage l role of the frontal lobe Strengths and Limitations of Case Studies Strengths IZl Provides a highly detailed analysis IZl Rare cases shed light on normal behavior IZl Theorybuilding source for hypotheses IZl Document extraordinary peopleevents Limitations No control groupslack control over extraneous variables Results may not generalize low external validity Susceptible to experimenter bias Memory of Ps may be limited x XXX SingleCase experimental Designs Researchers systematically examine how an IV in uences the behavior of an individual case looking at a manipulation Key Features 0 Each participant serves as hisher own control or comparison Treatment phase vs baseline phase 0 DV is measured over a course of the trials Rely on visual analysis of the data Example BF Skinner employed this research method on a single rat Psych209 Lecture 5272815 Goal of Singlesubject Designs Behvaiour of a single P must be shown to change as a result of the treatment Three components 1 Target behavior must be operationally de ned 2 Baseline level of responding must be established 3 Begin treatment and monitor behavior IndividualSubject Validity Summarizing data from large groups sometimes yields results that fail to characterize the behaviors of the individual that represents that group 0 Individualsubject validity The extent to which a general research result applies to the individual subjects in the study Advantages to SingleSubject Designs IZl Doesn t distort the behavior which can happen when examining group means IZl Clinical signi cance yet might not achieve statistical signi cance IZl Practical and ethical problems can eb addressed IZl Flexibility in Design Disadvantages to SingleSubject Design x Reasons for using SmallN designs Used when Procedures are costlytie consuming Procedures are invasive Procedures require intensive training ie training animals to learn sign language Special Ps that are rarehard to nd Terminologies of SmallN Designs 0 Designs that involve a very small number of PS 0 Summary statistics usually not needed conclusions can be drawn from visual inspection of the data 0 Focus on observations of individual behaviors Baseline Measures 0 Control Strategy 0 Two purposes Descriptive and Predictive Psych209 Lecture 5272815 The two baseline should be stable before beginning experiment or show a trend that is opposite to the predictive baseline 0 Trend Variability that is systematic Indicates a distinctive direction in the DV Ascending or Descending Collected baseline gt Intervention Repeated measures of behavior DV Behavior measures under a treatment lV AB Design aka Comparison Designs Baseline followed by Treatment Simplest Singlesubject design A Baseline B Treatment 0 Want to see behavior change when we move from A to B Potential confounds to consider 0 History 0 Maturation esp experiments on child they grow and mature wiser Regression to the mean Withdrawal Designs ABA Design reversal design 0 After treatment B has been in effect for awhile it is withdrawn back to A 0 Should see behavior return to baseline once treatment is removed 0 Baseline Treatment Removal of Treatment Problems with this Design x Treatment may not be reversible eg learning x You may want to leave the learner in the new condition Thus we look at ABAB Design aka Repeated Treatments Replication Design 0 Increase con dence if behavior changes again when treatment is reintroduced second B Ethical Advantage Finish experiment with treatment in place 0 Process Baseline Treatment Removal of Treatment Treatment Repeated 0 Leaves the learner with the bene t of treatment Problems with ABAB Design x Ethical concerns surrounding withdrawal of treatment that seems to be effective 0 Psych209 Lecture 5272815 x If target behavior does not revert to baseline levels during a withdrawal phase it is difficult to draw conclusions x Multiplebaseline designs are more useful if we have reason to believe a target behavior will not revert to baseline levels Alternating Treatments Design Employ multiple treatments without violating the quotonly change once variable at a timequot rule 0 Baseline Treatment 1 Treatment 2 Treatment 1 Treatment 2 etc looking at each treatment individual and then 0 Used it assess which treatment is most effective 0 This allows the comparison of two different treatments or IV Multiple Baseline Design Baseline established and treatment introduced at different times Three variables 1 Across Individuals Changing same behavior in different individuals 2 Across Behaviors Changing several behaviors in one individuals 3 Across Settings Changing same behavior in several different settings eg changing behavior at home in school with friends etc Tries to see if behavior change corresponds to onset of treatment 0 Behavior measured repeatedly during a baseline period and then during a treatment period 0 Some sort of replication is conducted 0 Timing of treatment is varied to rule out alternative explanations Subiects Two participants exposed to the same treatment gt Example Wagaman with 1 type of behavior in 2 individuals in 1 setting There were 8 school children age 610 IV Regulated breathing treatment DV Stuttering during the treatment and after it Setting home Behaviors 0 Same treatment applied to two or more behaviors of the same individual 0 Introduction of treatment is staggered across behaviors in order to create gt Example Doss with 2 types of behavior in 1 individual in 1 setting 9yrs old child with pervasive developmental disorder Psych209 Lecture 5272815 IV Treatment to improve social skills by reading stories DV Settings Same treatment applied to the same behavior in two or more settings Changing Criterion Designs Behavior shaped reinforce small approximations until the nal behavior is reached By establishing a baseline l introduce a treatment continue until initial criterion is met criterion made harder l continued until target behavior is met Strengths and Limitations of Small N designs Strengths IZl Contribute to treatment research IZl Strong individualsubject validity IZl Detailed analysis of a few people provides researchers with a lot of unique info Limitations x Lack external validity cannot generalize to all when it s only used on one x Need replication x Lack inferential statistical analyses x Cannot test for interaction effects


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