Causation & Experiments (cont.) Phil 12: Logic and Decision Making Winter 2010 UC San Diego 3/1/2010
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1 Causation & Experiments (cont.) Phil 12: Logic and Decision Making Winter 2010 UC San Diego 3/1/2010
2 CAPE Student evaluations now done online Please check your UCSD for link to evaluations for your courses Deadline: midnight before first day of finals
3 Review Experiments manipulate the independent variable and measure changes in the dependent variable - IVs sometimes called factors, values of each IV called levels Major concern is confounding variables: variables correlated with the independent variable that may be causes of the dependent variable - Subject confounds: differences between subjects - Procedural confounds: differences in way experimental and non-experimental groups are treated
4 Review Strategies for removing risk of confounds: - Randomization: attempt to neutralize effects of confounds, known and unknown - Locking: fixing a value of a variable - Matching: render experimental conditions equivalent in terms of possible known confounds - Measuring: treat as additional independent variable and then measure for correlations
5 Procedural variable confounds When you conduct a manipulation, generally more than one thing will be changed - These variables will then be correlated with the independent variable extraneous - If one of the other variables is causally related to the effect of interest, it rather than the variable you are considering may be the cause confound
6 Confounding Procedural Variables The president of the AGL corporation wanted to get his workers to be more productive - She found that when each employee was presented with a jar of jellybeans, productivity increased Was it the jellybeans that caused the increased productivity? Or was it: Novelty of the situation Attention from the president Desire to reciprocate
7 Controlling confounding procedural variables Manipulation Correlation or causation X Procedural variable X Independent variable [values]? Dependent variable [values] Strategy: break the correlation thereby breaking the effect of the confounding variable
8 Subject variable confounds Subjects in an experiment may be differently affected by different values of other variables - People of different ages sleep different amounts - Women might be affected differently than men These variables are extraneous If there is a correlation between these variables and the independent variable: - they, rather than the variable you are focusing on, may be what produce the change in the dependent variable Such variables are confounds
9 Controlling confounding subject variables Manipulation Independent variable [values] X Correlation or causation Dependent variable [values] Strategy: break the correlation thereby breaking the effect of the confounding variable Random assignment of subjects is a main strategy for breaking the correlation? X Subject variable
10 Controlling subject confounds: Between subjects randomization
11 Manipulate independent variable Task Key Behavior IV Task Key Behavior Statistically Significant DV Difference?
12 Controlling subject confounds: Within subjects design
13 Subjects serving as own control Task Task Key Behavior Key Behavior Statistically Significant DV Difference?
14 Between-subject design GOOD NEWS: - Participants are not contaminated in one condition as a result of having participated in the other BAD NEWS: - Requires a larger number of participants - Runs the risk of non-equivalence of subject groups
15 Within-subjects designs GOOD NEWS: - - Requires a smaller number of participants Rules out any differences between subjects BAD NEWS: - Potential contamination of participants behavior from previous trial: carryover effect Subjects might learn from one condition and that could alter their behavior in the second condition - Practice effect - Fatigue effect
16 Pretest Posttest Design There is always a danger in an experiment that the members of the two (or more) groups being studied already differ on the dependent variable Best control is to focus on change, not raw value of the dependent variable - Pretest: measure the dependent variable before the intervention - Posttest: measure the dependent variable after the intervention - Change = Posttest Pretest Manipulation Independent variable [values]? Dependent variable [change] [values]
17 Limitations of pretest-posttest design Just measuring change in one group using a pretest and a posttest allows for confounds Time has elapsed and subjects have gotten older (maturation) Events occurring between the pretest and posttest could affect the dependent variable (history) Experience with previous test may change performance Pretest and posttest may vary in difficulty Use of pretest-posttest does not obviate the need for a control group
18 Example: Exercise and sleep Is there a causal relation between exercise and sleep? Manipulation (instruct subjects to exercise little or much) Exercise [little, much] X Correlation or causation? X Job stress Sleep [<8 hours, >8 hours]
19 Example: alcohol and running speed Alcohol [no, yes]? 100 yard dash speed [seconds]
20 Between subjects or within subjects Between-subjects design - Different subjects would be used for the no-alcohol and alcohol conditions, and each would be tested only once Within-subjects design - Each subject would be tested under both the no-alcohol condition and the alcohol condition
21 Between subjects Jim Angela Megan Peter Roger Shane Sara Jessica
22 Within subjects Jim Angela Megan Peter Jim Angela Megan Peter
23 Counterbalance Alcohol condition rest No-alcohol condition Jim Angela Jim Angela No-alcohol condition rest Alcohol condition Megan Peter Megan Peter
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