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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.

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Presentation on theme: "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."— Presentation transcript:

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 1.Control = providing a standard for comparison 2.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)SPSS data file Rat #Experimental Condition (light on) Control Condition (light off)

5 Example of a Control Condition, revised experimental procedure DV = number of bar presses (SPSS data file)SPSS data file Rat #Experimental Condition (light on) Control Condition (light off)

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

7 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

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? 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

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)Excel spreadsheet 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)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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