Quasi experiments and Small N Designs PDF

Title Quasi experiments and Small N Designs
Course Psychological Methods: Research Procedures
Institution University of California Riverside
Pages 1
File Size 49.8 KB
File Type PDF
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Quasi experiments: researchers identify an independent variable but do not manipulate it or randomly assign participants to conditions o “acts of nature” o cannot/should not be manipulated (independent) o types:  Nonequivalent groups design: has at least 2 groups that are between subjects  Nonequivalent groups pretest – posttest design: a between subjects design that assess the dependent variable before and after the groups “diverge”  Repeated – measure quasi experiment  Interrupted time series design: measures the dependent variable repeatedly before, during, and after the study  Interrupted time series with reversal: an interrupted time series design in which measurements continues after things have gone back to their original state  Nonequivalent control group interrupted time series: includes a control group for which the key events does not occur Internal validity is ALWAYS lower in quasi experiments than in true experiments o Selection effect: the major potential confounds in nonequivalent group designs  “Solutions”:  Matched groups: a particularly useful strategy  Wait-list design: all participants experience an even or treatment, but one group experiences it later than the other Quasi experiments typically improve on correlational studies by attempting to rule out certain threats to internal validity and by comparing 2 groups Small-N design: gathering a lot of info from a small sample Single – N Design: a study of a single person or animals experience (case study) Advantages of small/single N designs: o Allows researchers to study rare people or events o Provides rich data about a narrow span of experience o Avoids problems associated with averaging across participants Disadvantages: o Findings may not generalize Stable – baseline design: assess dependent variable repeatedly, then introduce treatment or event Multiple – baseline design: introducing a treatment or even repeatedly with “baseline” measure in between Reversal design: baseline measures, treatment/event, removal of treatment/event...


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