quasi-experimental designs. intermediate between correlational study and true experiment. more...
Post on 19-Dec-2015
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Quasi-Experimental Designs
Quasi-Experimental Designs
Intermediate between correlational study and true experiment. More than a relationship between variables. Low internal validity = cannot determine
causality.
In true experiment, IV is manipulated and subjects are randomly assigned to conditions.
In quasi-experiments, IV is “manipulated”, but subjects are already part of a group based on pre-existing characteristics.
Nonmanipulated IV
IV occurs naturally Participants are not randomly assigned to
conditions.
Compares performance between 2 or more groups based on pre-existing characteristics. Ex: gender; religion; age; smokers vs. nonsmokers;
high, medium or low cholesterol levels. Groups are not equivalent before treatment. Low internal validity – we cannot conclude causality
Nonmanipulated independent variable and measure a particular dependent variable.
Control group & Nonequivalent group
True experimental designs have an experimental group (treatment) and a control group (no treatment). Participants are randomly assigned to either
condition.
Quasi-experimental designs do not have a control group because there is no random assignment of participants to the conditions. The nonequivalent group serves as the comparison to the treatment group
Typical quasi-experimental design
Select 2 groups based on pre-existing characteristics.
Divide each group in half: half of the participants in each group get the treatment and half do not.
Compare performance with and without IV within each group and across groups.
Disadvantage Pre-existing differences can confound results.
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18 28
Age Males Females
DV: # of anagrams solved
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Y
es
NO
Nonequivalent group design
88 86
60 42
Age Young Old
DV: % of words remembered
Mem
ory
Tes
t
Rec
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Nonequivalent group design
Single-Case Experimental Designs
Single case experimental designs
Involves the study of only 1 participant (single case designs) or 2 or 3 participants (small- n designs)
Often used in clinical settings.
Do not allow for generalization.
Allow for replications with different IV on the same participant or small-n designs.
Do not compare means nor run statistical analyses. Assess how performance changes from one condition to
another by graphing it.
Baseline measurement
A measurement of behavior made under normal conditions (e.g., no IV is present); a control condition.
Serves to compare the behavior as affected by the IV.
Collect enough measures to achieve a stable pattern.
Representative Single-Case Experimental Designs
Reversal Designs IV is introduced and removed one or more
times.
1) A-B design - simplest of all designs- measure baseline behavior, apply treatment and compare behavior after treatment to baseline.- does not allow to establish cause-effect
treatment
Behavior at Baseline Behavior during/ after treatment
A-B design
A-B-A design Baseline measurement Apply treatment Measure change in behavior (posttest 1) Remove treatment Behavior “should” go back to baseline
(final assessment)
Remove treatment
Behavior with treatment
Behavior at Baseline
treatment
Behavior back to Baseline
A-B-A design
A-B-A-B design
Baseline measurement Apply treatment Measure change in behavior (posttest 1) Remove treatment Behavior “should” go back to baseline
(assessment) Apply treatment again Measure change in behavior (posttest 2)
More ethical to end with treatment.
Remove treatment
Behavior with treatment Behavior at Baseline
treatment
Behavior back to Baseline
treatment
Behavior with treatment
A-B-A-B design
Multiple-Baseline Designs
Effects of IV are assessed across several participants, behaviors and situations.
Control for confounds by introducing treatment at different times for different participants, behaviors and situations.
Multiple-baseline designs
Multiple-baseline across participants Determine who has most stable baseline and
introduce treatment to that subject first.
Multiple-baseline across behaviors Determine most stable behavior and start with
treatment on that behavior and then start on 2nd behavior.
Multiple-baseline across situations Determine when behavior is occurring and
tackle one situation at a time.