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