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Control

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.

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Control

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  1. 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?

  2. Control: 2 Uses • Control = providing a standard for comparison • Control = reducing error variability

  3. 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?

  4. Example of a Control Condition DV = number of bar presses (SPSS data file)

  5. Example of a Control Condition, revised experimental procedure DV = number of bar presses (SPSS data file)

  6. Control as Reducing Error Variability • The meaning of “control” in Skinner’s work • Increases statistical power

  7. Control: 2 Uses • Control = providing a standard for comparison • Ruling out confounds • Increases internal validity • Control = reducing error variability • Increases statistical power • Increases statistical validity

  8. Strategies for Control • Subject as Own Control (within-subjects) • Random Assignment • Matching • Building in Nuisance Variables • Statistical Control • Replication

  9. 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? • Not logically possible • Participating in more than one condition will reveal the hypothesis or introduce demand characteristics • Contrast effects between conditions are likely

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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.

  15. Strategies for Control:Related to which type of Validity? • Subject as Own Control • Random Assignment • Matching • Building in Nuisance Variables • Statistical Control • Replication

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