Chapter 9

Choosing the Right Research Design

Chapter 9

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 mixed-model linear equation modeling