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Chapter 9 Chapter 9 Choosing the Right Research Design One-Way Designs • The simplest possible experimental design • Involves the manipulation of only one variable (single independent variable) 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 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) 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) 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. 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. Factorial Designs • There are 3 possible outcomes from a factorial design: • No significance • Main effects • Interactions 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. 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. 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 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 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 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 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 Within-Subjects Designs • Within-subjects or repeated measures designs are most often analyzed using… • Paired Samples T-test or • Repeated measures analysis of variance 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 mixedmodel linear equation modeling