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Chapter 9 Choosing the Right Research Design Chapter 9

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One-Way Designs The simplest possible experimental design Involves the manipulation of only one variable (single independent variable)

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One-Way Designs One-way designs must have a minimum of two groups A two-groups design is the simplest type of one-way design A one-way design with only two groups is most often analyzed with… The Independent Samples t-test

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One-Way Designs Experimental designs with more than two groups are called multiple groups designs One-way multiple groups designs are most often analyzed using… the one-way analysis of variance (ANOVA)

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Factorial Designs When experimental designs involve more than one independent variable they are called factorial designs Each independent variable has at least two levels (i.e. conditions of the variable)

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Factorial Designs Each independent variable is represented by a separate number which indicates the number of levels for that variable A 2 x 2 design has two independent variables with 2 levels each A 2 x 3 x 4 design has three independent variables. The first has 2 levels, the second has 3 levels and the third has 4 levels.

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Factorial Designs Factorial designs are most commonly analyzed using… Univariate analysis of variance if only one dependent variable is measured Multivariate analysis of variance (MANOVA) for research with multiple dependent variables 2 x 2 designs utilize a two-way ANOVA and 2 x 2 x 2 designs utilize a three-way ANOVA, etc.

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Factorial Designs There are 3 possible outcomes from a factorial design: No significance Main effects Interactions

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Factorial Designs Main effects indicate that a dependent variable is significantly different across the levels of an independent variable regardless of any other independent variable. Interactions indicate that a dependent variable is only significantly different across the levels of an independent variable depending on the level of a second independent variable.

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Within-Subjects Designs Between-subjects designs include all of the designs we have discussed so far Within-subjects or repeated measures designs are those in which a participant serves in more than one condition of a study.

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Within-Subjects Designs Advantages of within-subjects designs Fewer participants are needed because they are used in multiple conditions Fewer participants are needed because the design is more powerful There is less noise due to individual differences Thus person confounds are eliminated Within-subjects designs are the perfect form of matching

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Within-Subjects Designs Disadvantages of within-subjects designs Within-subjects designs are subject to certain forms of bias: Sequence effects - when the passage of time between conditions has an effect on performance

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Within-Subjects Designs Disadvantages of within-Subjects Designs Carryover effects- when responses to one stimulus directly influence the responses to another stimulus Figuring out the research hypothesis

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Within-Subjects Designs Types of Carryover effects Order effects- when a question takes on a different meaning following one question versus another or when a stimulus is influenced following another stimulus Practice effects- when an experience with one task makes it easier for someone to perform a different task Interference Effects- when an experience with one task makes it more difficult for someone to perform a different task

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Within-Subjects Designs Solutions to problems of within-subjects designs: Counterbalancing – Researcher varies the order in which participants experience the experimental conditions Complete counterbalancing – every possible order of experimental treatments Reverse counterbalancing – create a single order and then reverse it Partial counterbalancing - Selecting orders at random

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Within-Subjects Designs Within-subjects or repeated measures designs are most often analyzed using… Paired Samples T-test or Repeated measures analysis of variance

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Mixed-Model Designs At least one independent variable is manipulated between-subjects At least one independent variable is manipulated within-subjects Mixed-model designs are analyzed using mixed- model linear equation modeling

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