BHS 204-01 Methods in Behavioral Sciences I May 14, 2003 Chapter 8 (Ray) Between-Subjects Designs (Cont.)

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Presentation transcript:

BHS Methods in Behavioral Sciences I May 14, 2003 Chapter 8 (Ray) Between-Subjects Designs (Cont.)

Completely Randomized Design  Two groups: Treatment group – randomly assigned, receives treatment, then measurement. Control group – randomly assigned, receives no treatment (placebo), then measurement.  Each subject must be equally likely to be placed in either group.  Dependent variable is compared – treatment effect.

Multi-Level Design  Instead of a treatment group and a control group, the independent variable occurs at multiple levels: RGroup ALevel 1 TM RGroup BLevel 2 TM RGroup CLevel 3 TM RGroup DLevel 4 TM  Analyzed using ANOVA (F-ratio) and planned comparisons (t-tests).

Factorial Design  Multiple independent variables (factors), each with at least two levels. Each level of each independent variable must exist for the other independent variable. The dependent variable (measurement) is the same for all groups (conditions).  Treatment effects are called main effects.  Independent variables may combine to cause interaction effects.

Figure 8.3. (p. 175) Matrix showing the four possible combinations of each of the two levels of a 2 x 2 factorial random-subject design. Notice that each cell contains one of the four possible combinations of our two independent variables (housing condition and feeding schedule.

Figure 8.2. (p. 173) Schematic representation of 2 x 2, 3 x 3, and 2 x 3 x 2 factorial designs. Note that the total number of treatment conditions in each design can be obtained by multiplying the number of levels of each factor.

Table 8.2. (p. 179)

Figure 8.5. (p. 181) The main effects and interaction effect of treatments A and B are all nonsignificant.

Figure 8.6. (p. 183) Treatment A is significant; treatment B and the interaction are nonsignificant.

Figure 8.7. (p. 184) B is significant; A and the interaction are nonsignificant.

Figure 8.8. (p. 184) The interaction is significant; A and B are nonsignificant.

Figure 8.9. (p. 185) A and the interaction are significant; B is nonsignificant.

Figure (p. 186) B and the interaction are significant; A is nonsignificant.

Figure (p. 186) A and B are significant; the interaction is nonsignificant.

Figure (p. 187) A, B, and the interaction are all significant.

Why use a factorial design?