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Maximizing Research Design: Mixed, Nested, Experimental, Quasi-Experimental, Developmental Designs

This chapter explores specialized research designs such as mixed designs, nested designs, experimental and quasi-experimental designs, as well as developmental designs. These designs allow for the evaluation of various variables and the collection of developmental data over time.

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Maximizing Research Design: Mixed, Nested, Experimental, Quasi-Experimental, Developmental Designs

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  1. Chapter 10 Using Specialized Research Designs

  2. The Mixed Design • Includes a between-subjects and a within-subjects factor in the same design • Allows you to evaluate the effects of variables that cannot be manipulated effectively within-subjects • Complex mixed designs would include more than two factors, with any combination of between-subjects and within-subjects factors

  3. The Nested Design • Combines between-subjects and within-subjects factors • Different levels of within-subjects factors (e.g., A and B, C and D) are “nested” under different levels of a between-subjects factor (level 1 and level 2) • Types of nested designs • Nesting tasks • Nesting groups of subjects

  4. Example of a Nested Design

  5. Combining Experimental and Correlational Designs • 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

  6. 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

  7. Quasi-Experimental Designs • 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

  8. Nonequivalent Control Group Design • Include a time series component and a control group that is not exposed to the independent variable

  9. The Pretest-Posttest Design • 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

  10. A Mixed Design With Pretest-Posttest as the Within-Subjects Factor

  11. The Solomon Four-Group Design • 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

  12. The Solomon Four-Group Design

  13. Developmental Designs • 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

  14. 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

  15. 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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