control
DESCRIPTION
Control. Any means used to rule out threats to validity Example Hypothesis: Rats learned to press a bar when a light was turned on. Data for 10 rats bar pressing behavior when the light was on (on board) Did the experiment work?. Control: 2 Uses. - PowerPoint PPT PresentationTRANSCRIPT
Control
• Any means used to rule out threats to validity
• Example– Hypothesis: Rats learned to press a bar when a
light was turned on.– Data for 10 rats bar pressing behavior when the
light was on (on board)
• Did the experiment work?
Control: 2 Uses
1. Control = providing a standard for comparison
2. Control = reducing error variability
Control as Providing a Standard for Comparison
• Control Group• Control Condition• Two or more levels of an IV• Known base rate in the population
What is an example of each for the bar-pressing experiment?
Which is the weakest method of control?Which is best for the bar-pressing experiment?
Example of a Control Condition
DV = number of bar presses (SPSS data file)
Rat # Experimental Condition (light on)
Control Condition (light off)
1 0 0
2 1 0
3 1 0
4 2 0
5 2 1
6 2 1
7 3 1
8 3 2
9 3 2
10 3 3
Example of a Control Condition, revised experimental procedure
DV = number of bar presses (SPSS data file)
Rat # Experimental Condition (light on)
Control Condition (light off)
1 2 0
2 2 0
3 2 0
4 2 0
5 2 1
6 3 1
7 2 1
8 2 0
9 3 0
10 3 0
Control as Reducing Error Variability
• The meaning of “control” in Skinner’s work
• Increases statistical power
Control: 2 Uses
1. Control = providing a standard for comparison
• Ruling out confounds• Increases internal validity
2. Control = reducing error variability• Increases statistical power• Increases statistical validity
Strategies for Control
• Subject as Own Control (within-subjects)
• Random Assignment
• Matching
• Building in Nuisance Variables
• Statistical Control
• Replication
Subject as Own Control (within-subjects designs)
• Generally better than between-subjects– Rules out more possible confounds
– Provides more statistical power
• When is a within-subjects design inappropriate?1. Not logically possible
2. Participating in more than one condition will reveal the hypothesis or introduce demand characteristics
3. Contrast effects between conditions are likely
Random Assignment
• “each subject has an equal and independent chance of being assigned to every condition”
• Reduces the likelihood of confounds(Excel spreadsheet demo)
• The defining feature of a “true experiment”• Quasi-experiment: when participants are not
randomly assigned to groups
Matching
• Procedure to ensure that experimental and control groups are equated on one or more variables before the experiment
• Only useful when the matched variable correlates substantially with the DV (example)
• Howto:– Create pairs matched on some variable you think will
be correlated with the DV
– Randomly assign members of each pair to conditions
Building in Nuisance Variables
• Nuisance variable = a variable that is not relevant to the hypothesis, but is difficult to remove from an experiment and is therefore made part of the design
• Not a confound! Not confounded with IV.• Including a nuisance variable can increase
statistical power• Examples:
– night vs. day student (text, p. 200)– Counterbalancing variables
Statistical Control
• Mathematical (statistical) way of equating subjects who differ on a nuisance variable that is correlated with the DV
• “Analysis of Covariance”• Useful when random assignment and
matching are not possible• Example: Studying effects of teaching
techniques on grades, using IQ as covariate
Replication
= repeating an experiment to see if the results will be the same
• Direct replication – repeating an experiment exactly
• Systematic replication – extending an experiment to new subjects, dependent variables, independent variables, etc.
Strategies for Control:Related to which type of Validity?
• Subject as Own Control
• Random Assignment
• Matching
• Building in Nuisance Variables
• Statistical Control
• Replication