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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Nonequivalent group design

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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.