1. Single-Factor Experiments What is a true experiment? Between-subjects designs Within-subjects designs Designs to avoid (not true experiments)

2. What is a true experiment? Experiment = study in which researcher has complete control over all aspects of the study 2 Essential features of experiments: Control Group or Control Condition (actually, or 2 or more levels of an IV) Random allocation of subjects to groups (if it has a between-subjects factor)

3. Some Terminology IV = what the experimenter manipulates (varies) in an experiment; the hypothesized cause DV = what the experimenter measures to test the hypothesis in an experiment; the hypothesized effect Factor = IV Level = condition = treatment: –One value of an IV –Example: for the factor “gender,” the levels are “male” and “female” (in a quasi-experiment) –Example: for the factor “number of witnesses” in a bystander intervention experiment, the levels could be “one” “two” and “four”

4. Between-subjects designs At least 2 conditions (groups) –Control and Experimental Condition, or –2 or more levels of IV without “control” condition Each subject is assigned to only one condition Random assignment of subjects to conditions Design a between-subjects experiment testing this hypothesis: Vocalization impairs short-term memory.

5. Between-subjects Designs with Multiple Conditions More than 2 levels of the IV Randomly assign subjects to conditions Design a between-subjects experiment testing to answer the following questions: –Does vocalization impair STM? –Does it matter whether the vocalization is aloud or just “in your head”?

6. Within-Subjects Designs At least 2 conditions –Control and Experimental Condition, or –2 or more levels of IV without “control” condition Each subject is assigned to all conditions Counterbalance to control for order and sequence effects Design a within-subjects experiment testing this hypothesis: Sub-vocalized speech impairs short-term memory.

7. Counterbalancing in Within-Subjects Designs Counterbalancing within subjects –Useful when each level of the IV occurs multiple times for each subject –Each subject gets both sequences of the conditions Counterbalancing within groups (not within each subject) –Useful when there are many (more than 2) conditions –Necessary if each condition occurs only once per subject –An equal number of subjects get each sequence of the conditions

8. Order and Sequence Effects Order effects –Result from the position in which a condition occurs (first, second, third, etc) –If the order of conditions is “A-B”, order effects on B would be the effects that result from B being in the second position in the list –Example: practice effects Sequence effects –Result from which conditions precede or follow a condition –If the order of conditions is “A-B”, sequence effects on B would be the effects that result from B following A –Example: color perception

9. Controlling Order and Sequence Effects Controlling Order Effects –Counterbalance the position in which each condition appears –ABC, CBA, ACB: C occurs first, second, and third an equal number of times Controlling Sequence Effects –Counterbalance what each condition follows –ABC, BAC : C follows A half the time, and follows B half the time.

10. Controlling Order and Sequence Effects Within Subjects Only possible when each condition occurs at least twice for each subject Method depends on how many times each condition occurs per subject: –Many times: randomize order of conditions –A few times: use block randomization of conditions –Twice: use reverse counterbalancing

11. Controlling Order and Sequence Effects Within Groups When it is not possible or practical to control within subjects Necessary if each condition occurs only once for each subject Sequences and orders controlled within a group of subjects, but not within each subject Latin Square – a technique for partial counterbalancing (when full counterbalancing is impractical)

12. Latin Square A square matrix with length = number of conditions Each condition occurs only once on each row and only once in each column: abcd bcda cdab dabc

13. Balanced Latin Square A Latin Square in which each condition is preceded by every other condition exactly once: abcd bdac cadb dcba

14. Designs to Avoid One-group post-test only Post-test only with non-equivalent control group One-group pretest-posttest design