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Two-Factor Mixed Designs in Analysis of Variance: One Between-Subjects Factor and One Within-Subjects Factor

Sanford L. Braver, David P. MacKinnon, Melanie Page

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Abstract

In chapters 3 through 7 you learned how to analyze between-subjects and within-subjects (i.e., repeated measures) designs. In this chapter, these two designs are combined to form a mixed design (sometimes also called a split-plot design). Mixed designs are widely used because of their desirable statistical properties: reduced error variance, owing to the within-subjects factor, and ease of the interpretation of the between-subjects factor’s effects. In this chapter, the simplest mixed design, that with two factors, one between-and one within-subjects factor is described. Larger mixed designs are covered in chapter 9.

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In chapters 3 through 7 you learned how to analyze between-subjects and within-subjects (i.e., repeated measures) designs. In this chapter, these two designs are combined to form a mixed design (sometimes also called a split-plot design). Mixed designs are widely used because of their desirable statistical properties: reduced error variance, owing to the within-subjects factor, and ease of the interpretation of the between-subjects factor’s effects. In this chapter, the simplest mixed design, that with two factors, one between-and one within-subjects factor is described. Larger mixed designs are covered in chapter 9.

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Available abstract

In chapters 3 through 7 you learned how to analyze between-subjects and within-subjects (i.e., repeated measures) designs. In this chapter, these two designs are combined to form a mixed design (sometimes also called a split-plot design). Mixed designs are widely used because of their desirable statistical properties: reduced error variance, owing to the within-subjects factor, and ease of the interpretation of the between-subjects factor’s effects. In this chapter, the simplest mixed design, that with two factors, one between-and one within-subjects factor is described. Larger mixed designs are covered in chapter 9.

Key concepts: Factor (programming language), Statistics, Variance (accounting), Mathematics, Computer science, Economics, Programming language, Accounting

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