1. EXPERIMENTS Lecture 5

2. Administrative STATA Course Mailing List Info:  majordomo@sims.berkeley.edu majordomo@sims.berkeley.edu  No subject  In body of email: subscribe i271b [your email] 2

3. 3 Thought Experiments Count Too…

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5. Two Essential Criteria in True Randomized Experimental Design 5 (1) Independent Variables must be manipulated (usually by experimenter, sometimes by context) (2) Participants must be assigned randomly to various conditions or groups Courtesy http://psychology.ucdavis.edu/SommerB/

6. Active versus Attribute Independent Variables 6 Active independent variable(s):  The I.V. is “given” to the participants, usually for some specified time period. It is often manipulated and controlled by the investigator. Attribute independent variable(s):  A predictive, defining characteristic of individuals. Cannot be manipulated.

7. Randomization in Sample and Assignment 7 Random Sample  System for choosing participants from a population  Generally, the larger the sampling population the better your generalizability becomes. Random Assignment  Method for assigning participants randomly to experiment conditions

8. True Experiments 8 True experiments protect against both time and group threats to internal validity by randomly assigning subjects to treatment and control groups. The treatment (independent variable) is active. If we cannot randomly assign subjects to different groups, then it is a quasi-experiment. The independent variable is active. If we cannot randomly assign subjects to groups because the groups contain the attribute of interest, and if we give all groups the same treatment, then it is an associational non-experiment. The independent variable is not active.

9. Pre-test, experimental manipulation and post- testing 9 Pre-test: allows us to check group equivalence before the intervention X is introduced. Experimental manipulation: An independent variable (X) that the experimenter manipulates. Post-test: allows us to check group equivalence after intervention X has been introduced. Hypothesis  Random Assignment  Measure D.V.  Treatment  Measure D.V. (pre-test)(post-test)

10. 10 Common types of true experiments R O O XO O R X O O R O O XO O (1) (2) XO(3) O(4) Pretest-Posttest Control Post-only Control Solomon 4-group

11. Example: Pen Study 11 Question: Do individuals in Japan and the US make differential choices about ‘unique’ versus ‘less unique’ items when given a choice?

12. Pen Study 12 Independent Variable  Cultural difference: Japanese students compared to US students Assignment  Subjects were not randomly assigned because they already fell into one of the two groups. Dependent Variable:  Pen layout (3 of one type, 1 of another)  Would they choose the ‘common’ pen or the ‘unique’ one?

13. Example: Trust-Building Study 13 Question: Do increased risk-taking behaviors over time increase interpersonal trust?

14. Trust-Building Study 14 Independent Variable  Experiment Condition (2 conditions):  Fixed partner on every trial, cannot control amount to entrust to partner  Fixed partner on every trial, can control amount to entrust to partner Assignment  Random assignment of participants to one of the 2 conditions.  Same experiment conducted in Japan and US, and comparisons made between the two studies. Dependent Variable  Cooperation rate (i.e., whether they returned the coins to the partner or not)

15. Pro’s and Con’s of Experiments 15 Pro’s  Gives researcher tight control over independent factors  Allows researcher to test key relationships with as few confounding factors as possible  Allows for direct causal testing Con’s  Usually a smaller N than surveys  Sometimes give up large amounts of external validity in favor of construct validity and direct causal analysis  Require a large amount of planning, training, and time– sometimes to test relationship between only 2 factors!