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Understanding Control in Experimental Design: Enhancing Validity and Reducing Variability

This text explores the concept of control in experimental design, defining its roles in providing standards for comparison and reducing error variability. It discusses methods such as using control groups, random assignment, and within-subject designs to enhance statistical power and internal validity. Key topics include the significance of ruling out confounds, matching participants, and the implications of nuisance variables. Examples from bar-pressing experiments with rats illustrate these principles, emphasizing the importance of careful experimental control for reliable results.

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Understanding Control in Experimental Design: Enhancing Validity and Reducing Variability

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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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