McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. Using Between-Subjects and Within- Subjects Experimental Designs

2 Between-Subjects Design Different groups of subjects are randomly assigned to the levels of your independent variable Data are averaged for analysis Within-Subjects Design A single group of subjects is exposed to all levels of the independent variable Data are averaged for analysis

3 Single-Subject Design Single subject, or small group of subjects is (are) exposed to all levels of the independent variable Data are not averaged for analysis; the behavior of single subjects is evaluated

4 Error variance is the variability among scores not caused by the independent variable Error variance is common to all three experimental designs Error variance is handled differently in each design Sources of error variance Individual differences among subjects Environmental conditions not constant across levels of the independent variable Fluctuations in the physical/mental state of an individual subject

5 Taking steps to reduce error variance Hold extraneous variables constant by treating subjects as similarly as possible Match subjects on crucial characteristics Increasing the effectiveness of the independent variable Strong manipulations yield less error variance than weak manipulations Use a dependent variable that is sensitive enough to detect effect of your independent variable

6 Randomizing error variance across groups Distribute error variance equivalently across levels of the independent variable Accomplished with random assignment of subjects to levels of the independent variable Statistical analysis Random assignment tends to equalize error variance across groups, but not guarantee that it will You can estimate the probability that observed differences are due to error variance by using inferential statistics

7 Single-Factor Randomized Groups Design The randomized two-group design The randomized multiple group design The multiple control group design Matched-Groups Designs The matched-groups design The matched-pairs design The matched multigroup design

8 Subjects are randomly assigned to treatment groups Two groups (Experimental and Control) are needed to constitute an experiment The randomized two-group design is the simplest experiment to conduct The amount of information yielded may be limited

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10 Additional levels of the independent variable can be added to form a randomized multigroup design If different levels of the independent variable represent quantitative differences, the design is a parametric design If different levels of the independent variable represent qualitative differences, the design is a nonparametric design The multiple control group design including a number of control groups is a variant of the randomized multigroup design

11 In a matched groups design matched sets of subjects are randomly assigned to groups Steps involved in conducting a two-group matched groups experiment Obtain a sample of subjects Measure the subjects for a certain characteristic (e.g., intelligence) that you feel may relate to the dependent variable Match the subjects according to the characteristic (e.g., pair subjects with similar intelligence test scores) to form pairs of similar subjects

12 Randomly assign one subject from each pair of subjects to the control group and the other to the experimental group Carry out the experiment in the same manner as a randomized group experiment

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14 Advantages Control over subject variables that can mask effects of your independent variable Increases sensitivity to effects of your independent variable Disadvantages If matched characteristic has small effect the power of statistical test is reduced Pretesting required for matching makes design more demanding than a randomized experiment Requires large pool of subjects Unwieldy for matched multigroup designs

15 In a within-subjects design the same subjects are used in all conditions Subjects are not randomly assigned to treatment conditions Evaluate changes in behavior within subjects across multiple treatments Sometimes called a repeated-measures design Closely related to the matched-groups design

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17 Advantages Reduces error variance due to individual differences among subjects across treatment groups Reduced error variance results in a more powerful design Effects of independent variable are more likely to be detected Disadvantages More demanding on subjects, especially in complex designs Subject attrition is a problem Carryover effects: Exposure to a previous treatment affects performance in a subsequent treatment

18 Learning Learning a task in the first treatment may affect performance in the second Fatigue Fatigue from earlier treatments may affect performance in later treatments Habituation Repeated exposure to a stimulus may lead to unresponsiveness to that stimulus

19 Sensitization Exposure to a stimulus may make a subject respond more strongly to another Contrast Subjects may compare treatments, which may affect behavior Adaptation If a subject undergoes adaptation (e.g., dark adaptation), then earlier results may differ from later ones

20 Counterbalancing The various treatments are presented in a different order for different subjects May be complete or partial Will not work for irreversible changes

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22 The Latin Square Design Used when you make the number of treatment orders equal to the number of treatments Taking Steps to Minimize Carryover Techniques such as pre-training, practice sessions, or rest periods between treatments can reduce some forms of carryover Make Treatment Order an Independent Variable Allows you to measure the size of carryover effects, which can be taken into account in future experiments

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24 A within-subjects design may be best when Subject variables are correlated with the dependent variable It is important to economize on participants or subjects You want to assess the effects of increasing exposure on behavior

25 Adding a second independent variable to a single-factor design results in a factorial design Two components can be assessed The main effect of each independent variable The separate effect of each independent variable Analogous to separate experiments involving those variables Represented by means for each level of each factor The interaction between independent variables When the effect of one independent variable changes over levels of a second Represented by the “cell means” in your design

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29 More than two independent variables are included in a higher-order factorial design As factors are added, the complexity of the experimental design increases The number of possible main effects and interactions increases The number of subjects required increases The volume of materials and amount of time needed to complete the experiment increases

30 You can include or delete cells in a design to go beyond a full factorial design In a fractional factorial design you do not cross all levels of your multiple independent variables Select only those levels of your independent variable relevant to testing your hypotheses You should choose the design that best tests your hypotheses