Chapter 11

• Within-Subjects Design


  A design in which each subject serves in more than one condition of the experiment
• Power


  An experiment's ability to detect the independent variable's effect on the dependent variable
• Increased power


  Greater chance of detecting a genuine effect of the IV
• Within-Subjects Factorial Design


  A factorial design in which subjects receive all conditions in the experiment
• Mixed Design


  A design that combines within- and between-subjects variables in a single experiment
• Advantages of Within-Subjects Designs

  • Use fewer subjects
  • Save time or training
  • Greater statistical power
  • More complete record of subject's performance
• Disadvantages of Within-Subjects Designs

  • Subjects participate longer
  • Resetting equipment may consume time
  • Treatment condition may interfere with each other
  • Treatment order may confound results
• We cannot use a within-subjects design when one treatment condition produces another due to interference
• A within-subjects design is usually preferable when you need to control large individual differences or have a small number of subjects
• Order Effect


  Change in subjects' performance that occurs when a treatment condition falls in different places in a series of treatments
• Fatigue effects


  Changes in performance caused by fatigue, boredom, or irritation
• Practice effects


  Change in subjects' performance resulting from practice
• Progressive error


  Changes in subjects' responses that are caused by testing in multiple treatment conditions
• Counterbalancing


  A technique for controlling order effects by distributing progressive error across the different treatment conditions of the experiment
• Subject-by-Subject Counterbalancing


  A technique for controlling progressive error for each individual subject by presenting all treatment conditions more than once
• Reverse Counterbalancing


  A technique for controlling progressive error for each individual subject by presenting all treatment conditions twice, first in one order, then in the reverse order
• Block Randomization


  A process of randomization that first creates treatment blocks containing one random order of the conditions in the experiment
• Across-Subjects Counterbalancing


  A technique for controlling progressive error that pools all subjects' data together to equalize the effects of progressive error for each condition
• Complete Counterbalancing


  A technique for controlling progressive error using all possible sequences that can be formed out of the treatment conditions and using each sequence the same number of times
• Partial Counterbalancing


  A technique for controlling progressive error by using some subset of the available sequences of treatment conditions
• Randomized Partial Counterbalancing


  The experimenter randomly selects as many sequences of treatment conditions as there are subjects for the experiment
• Latin Square Counterbalancing


  A partial counterbalancing technique in which a matrix, or square, of sequences is constructed so that each treatment appears only once in any order position
• Carryover Effects


  The persistence of the effect of a treatment condition after the condition ends
• Balanced Latin Square


  A partial counterbalancing technique for constructing a matrix, or square, of sequences in which each treatment condition appears only once in each position in a sequence and precedes and follows every other condition an equal number of times