1. Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Using Specialized Research Designs

2. 2 A mixed design includes a between-subjects and a within-subjects factor in the same design Subjects randomly assigned to between-subjects factor Within each level of the between-subjects factor is the same within-subjects factor Also called a split-plot design Allows you to evaluate the effects of variables that cannot be manipulated effectively within- subjects Complex mixed designs include more than two factors, with any combination of between- subjects and within-subjects factors

3. 3 Extended contact condition (between-subjects factor) Time of measurement (within-subjects factor)

4. 4 In a nested design different levels of a within-subjects factor are nested under a between-subjects factor Different levels of the within-subjects factor (e.g., B1& B2 and B3 & B4) are “nested” under different levels of a between-subjects factor (e.g., A1 and A2, respectively) Types of nested designs Nesting tasks Nesting groups of subjects

5. 5 Factor A is between- subjects Different levels of the within-subjects factor (B) nested under each level of the between- subjects factor (A)

6. 6 Including a covariate in an experimental design A covariate is a correlational variable (e.g., self- esteem) in an experimental design “Subtracting out” the influence of the covariate reduces error variance Makes your design more sensitive to the effects of the independent variable Covariates typically are continuous or discrete variables with a large number of levels

7. 7 Including a quasi-independent variable in an experimental design A quasi-independent variable is a correlational variable (e.g., gender) that looks like an experimental variable Resulting design looks like a factorial experimental design The quasi-independent variable must not be interpreted as causing changes in the dependent variable Advantages Allows you to test generality of findings across quasi-independent variable Reduces error variance Disadvantages Results may be misinterpreted Extra effort finding subjects that differ on quasi-independent variable

8. 8 Time Series Design Make several observations of behavior before and after introducing your independent variable Interrupted Time Series Design Make several observations before and after some naturally occurring event Equivalent Time Samples Design Repeatedly introduce the treatment condition, alternated with periods of observation without the treatment Nonequivalent Control Group Design Include a time series component and a control group that is not exposed to the independent variable

9. 9 In a pretest-posttest design a pretest administered before exposure to experimental treatment Unlike quasi-experimental designs, this is a true experimental design Used to assess the impact of some change on performance There is a problem with pretest sensitization Taking the pretest may alter the way a person performs in an experiment

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11. 11 Variation on the pretest-posttest design Allows you to evaluate the impact of a pretest on posttest performance Adds two groups to the basic pretest- posttest design A treatment-posttest group A posttest only group

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13. 13 The Cross-Sectional Design Participants from different age groups are run through a study at the same time Creating “cohort” groups based on participants’ ages Allows you to collect developmental data in a short period of time May not be appropriate studies using widely ranging age groups Generation effects may be a problem

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15. 15 The Longitudinal Design A single group of participants is measured several times over some period of time (e.g., months or years) Avoids the generation effect that may plague a cross- sectional study May still have a cross-generational problem Results from a longitudinal study on one generation may not generalize to another Problems with the longitudinal design Subject mortality Multiple observation effects

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17. 17 The Cohort-Sequential Design Combines a cross-sectional and longitudinal component in the same design Allows you to test for, but not eliminate, generation effects

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