@ 2012 Wadsworth, Cengage Learning Chapter 10 Extending the Logic of Experimentation: Within-Subjects and Matched-Subjects 2012 Wadsworth, Cengage Learning

Topics 1.Within-Subjects Designs 2.Mixed Designs 3.Matched-Subjects Designs

@ 2012 Wadsworth, Cengage Learning Within-Subjects Designs

@ 2012 Wadsworth, Cengage Learning Within-Subjects Designs The participant’s own performance is the basis of comparison Compare the performances of the same set of participants on the dependent variable following different treatments Let’s look at an experiment – First as a between-subjects experiment – Then as a within-subjects experiment

@ 2012 Wadsworth, Cengage Learning Within-Subjects Designs (cont’d.) If we identify the individual participants, the design is:

@ 2012 Wadsworth, Cengage Learning Within-Subjects Designs (cont’d.) We can perform this same study as a within-subjects design

@ 2012 Wadsworth, Cengage Learning Table 10.1 Analysis of Variance F Table for the Basketball Study as a Between-Subjects Design and as a Within-Subjects Design

@ 2012 Wadsworth, Cengage Learning Within-Subjects Designs (cont’d.) Potential problem in basketball study – The results from the no-feedback condition might have a potential carryover effect on the feed-back condition To control for this potential problem: – Use a counterbalancing procedure Intrasubject counterbalancing

@ 2012 Wadsworth, Cengage Learning An Illustration of Within-Subjects Research Interaction effect – Crashes were caused by a visual illusion but only when the pilots were distracted from instruments Perform a simple 2 X 3 X 2 design

@ 2012 Wadsworth, Cengage Learning An Illustration of Within-Subjects Research (cont’d.) Dependent variable: amount of landing error Using a between-subjects design – Randomly assign pilots to one of the four possible conditions (A 1 B 1, A 1 B 2, A 2 B 1, A 2 B 2 ) Is a between-subjects design the best alternative? Use a within-subjects design – Each cell is composed of the same participants

@ 2012 Wadsworth, Cengage Learning Within-Subjects Designs Advantages of within-subjects designs: – Ensures that all groups are equal on every factor at the beginning of the experiment – The total number of research participants can be reduced dramatically – Statistically more sensitive to changes in the treatment effect

@ 2012 Wadsworth, Cengage Learning Within-Subjects Designs (cont’d.) Disadvantages of within-subjects design – Within-subjects design is not appropriate when: The treatment has a lasting effect or The purpose of the study is to test for a lasting effect – Extremely sensitive to time-related effects Order effects: effects brought about through continued repetition of the tasks – Fatigue effects: a decline in performance – Practice effects: an improvement in performance

@ 2012 Wadsworth, Cengage Learning Counterbalancing Conditions for complete counterbalancing: – Each condition must occur equally often – Each condition must precede and follow all other conditions an equal number of times Intragroup counterbalancing: ensures that every possible sequence appears at each presentation of the treatment

@ 2012 Wadsworth, Cengage Learning Counterbalancing (cont’d.) Latin square design – Special case of incomplete counterbalancing – Common use: psychopharmacological research Counterbalancing will not control for a differential order effect

@ 2012 Wadsworth, Cengage Learning Repeated Measures One common application of within-subjects designs – Repeated measures as one factor in a factorial design – Useful in studying psychological processes that occur over time – Widely used in studying human and animal learning processes

@ 2012 Wadsworth, Cengage Learning Mixed Designs

@ 2012 Wadsworth, Cengage Learning Mixed Designs Designs that include both “within” and “between” components Example: – Assume that we want to perform a biofeedback experiment to determine the effect of feedback on our ability to control heart rate

@ 2012 Wadsworth, Cengage Learning Mixed Designs (cont’d.) Such an experiment is diagrammed as:

@ 2012 Wadsworth, Cengage Learning Mixed Designs (cont’d.) Figure 10.1

@ 2012 Wadsworth, Cengage Learning Matched-Subjects Designs

@ 2012 Wadsworth, Cengage Learning Matched Subjects Design By using a matched-subjects design, we: – Reap some of the advantages of within-subjects designs and – Take advantage of the random assignment of participants that is possible with a between- subjects design Matching can be used in either of two ways: – As a control procedure – As an experimental procedure

@ 2012 Wadsworth, Cengage Learning Matching as a Control Procedure When a particular individual variable or characteristic has a high correlation with the dependent variable – Equal groups may be obtained by matching along this characteristic

@ 2012 Wadsworth, Cengage Learning Matching as a Control Procedure (cont’d.) Two steps in forming matched groups of participants: – Pairs of participants are matched on some measure that is correlated with performance on the dependent variable – One member of each pair is assigned randomly to either the experimental or the control group; the other member is then assigned to the other group

@ 2012 Wadsworth, Cengage Learning Figure 10.2 The six major steps in a matched-subjects design used to study changes in brain chemistry resulting from prolonged sensory deprivation. (1) Rank order all subjects on the aspect of blood chemistry that is known to be correlated with brain neurochemistry. (2) Form pairs of subjects on the basis of this rank order. (3) Randomly assign one member of each pair to the experimental group and one member to the control group. (4) Conduct the experimental treatments. (5) Conduct neurochemical analyses. (6) Compare the results for the experimental and control animals. Note that, except for our ranking and matching procedures, the design is similar to the completely randomized designs discussed previously

@ 2012 Wadsworth, Cengage Learning Matching as an Experimental Procedure Randomized block design: resulting procedure when the matching factor is analyzed Time estimation study factorial design

@ 2012 Wadsworth, Cengage Learning Matching as an Experimental Procedure (cont’d.) Advantages resulting from the prior matching of participants to groups – Knowledge gained from the interaction effect – Matching ensures that the groups in a study are equal before the treatment is introduced – Matching participants reduces the within-groups variance (error variance)

@ 2012 Wadsworth, Cengage Learning Terminology Glass and Stanley (1970) suggest: – Reserve the term blocking for cases in which the matching takes place on a nominal-scale factor – The use of twins be considered matching on a nominal-scale factor – When the matching uses ordinal measurements, use the term stratifying – When interval or ratio scales are used, use the term leveling

@ 2012 Wadsworth, Cengage Learning Summary Within-subjects designs: every participant serves in every group and receives all levels of the independent variable Mixed design: combines both “within” and “between” designs Matched-subjects procedure may be used either as a control procedure or as an experimental procedure