CHAPTER CHAPTER 7 Experimental and Quasi-Experimental Research

Chapter Outline • Sources of invalidity • Threats to internal validity • Threats to external validity • Controlling threats to internal validity • Controlling threats to external validity • Types of designs

Experimental Research Tries to Establish Cause and Effect • Selection of a good theoretical framework • Application of appropriate experimental design • Use of correct statistical model and analysis • Proper selection and control of independent variables • Appropriate selection and measurement of dependent variables • Correct interpretation of results

Three Criteria for Cause and Effect 1. The cause must precede the effect in time. 2. The cause and effect must be correlated with each other. 3. The correlation between cause and effect cannot be explained by another variable. If the condition is necessary and sufficient to produce the effect, then it is the cause.

Distinguishing Between Types of Validity • Internal validity: Did the treatments (IV) cause the change in the outcome (dv)? • External validity: To what populations, settings, or treatments can the outcome be generalized? • Is there a trade-off between internal and external validity? • Can a series of studies address the trade-off?

Threats to Internal Validity • History: Events that are not part of treatment • Maturation: Events due to passage of time • Testing: Effects of more than one test administration • Instrumentation: Change in calibration of measurements • Statistical regression: Selection based on extreme score (continued)

Threats to Internal Validity • Selection biases: Nonrandom participant selection • Experimental mortality: Differential loss of participants • Selection–maturation interaction: Passage of time influencing groups differently • Expectancy: Influence of experimenters on participants

Threats to External Validity • Reactive or interactive effects of testing: Pretest may make participants sensitive to treatment. • Reactive effects of experimental arrangements: Setting constraints may influence generalizability. • Multiple-treatment interference: One treatment may influence the next treatment.

Controlling Threats to Internal Validity • Randomization - Real randomization - Matched pairs (not matched groups) - Randomizing treatments or counterbalancing • Placebos • Blind setups (continued)

Controlling Threats to Internal Validity • Double-blind setups • Reactive effects of testing: Eliminate pretest. • Instrumentation - Calibration and test reliability - Halo effects • Experimental mortality: Keeping participants

Controlling Threats to External Validity • Selecting from larger populations - Participants - Treatments - Situations • Ecological validity: Does the setting capture the essence of the real world?

Types of Designs: Preexperimental Designs One-shot studies T O One-group pretest-posttest O1 T O2 Statistical analysis? Static group comparison T O1 ------------- Statistical analysis? O2

Types of Designs: True Experimental Designs Randomized-groups design R T O1 Statistical analysis? R O2 Extending the levels—randomized-groups design R T1 O1 R T2 O2 Statistical analysis? R O3 (continued)

Types of Designs: True Experimental Designs (continued)

Types of Designs: True Experimental Designs Pretest-posttest randomized-groups R O1 T O2 R O3 O4 Statistical analysis? Extending the design on the RM factor R O1 T O2 T O3 Statistical analysis? R O4 O5 O6 (continued)

Types of Designs: True Experimental Designs Solomon four-group design—purpose R O1 T O2 R O3 O4 R T O5 R O6 Statistical analysis (factorial ANOVA) No treatment Treatment Pretested O4 O2 Unpretested O6 O5 (continued)

Quasi-Experimental Designs: Time Series Campbell and Stanley 1963.

Quasi-Experimental Designs: Reversal

Quasi-Experimental Designs: Ex Post Facto This is one of the preexperimental designs, but with the treatment not under the control of the experimenter. T O1 ----------------------- Statistical analysis? O2

Quasi-Experimental Designs: Single Participant Identify participant and follow over time. • Does the treatment produce the same effect each time? • Are treatment effects cumulative, or does participant return to baseline? • Does participant’s response become less variable over treatment times? (continued)

Quasi-Experimental Designs: Single Participant • Is participant’s magnitude of response sensitive to multiple treatment applications? • Do varying intensities, frequencies, and lengths of treatment produce varying responses?