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This chapter explores the use of matched-subjects procedures in behavioral sciences research, including factorial designs and mixed designs. It discusses the process of matching subjects based on physical or mental characteristics and how matching can control for potential confounds. The chapter also covers the analysis of matched designs using appropriate statistical tests such as paired-sample t-tests and repeated-measures ANOVA. Additionally, it explains the use of randomized block design when a subject characteristic is measured instead of controlled. Overall, this chapter provides insights into how matched-subjects procedures can enhance control and validity in behavioral sciences research.
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BHS 204-01Methods in Behavioral Sciences I May 19, 2003 Chapter 9 (Ray) Within-Subjects Designs (Cont.), Matched-Subjects Procedures
Mixed Designs • In a factorial design, one independent variable can be between-subjects while the other is within-subjects. • Requires a mixed-model ANOVA.
Matched-Subjects Procedures • Matching is a method of reducing within-group variance (error) and equating groups. • Different subjects are in each group, so this is a between-subjects experiment. • Subjects are selected to be closely similar across the groups. • Identical twins, married couples, siblings. • Pairs may be equated using some other factor.
Factors Used to Match Subjects • Any physical or mental characteristics that can be measured. • Intelligence, achievement motivation, age. • Socio-economic status, experience with a task. • Whatever factor is used to match subjects must be highly correlated with the dependent variable or matching is pointless.
Matching as a Control Procedure • Matching can control for the effects of potential confounds: • When studying the advantage of a new pair of running shoes, athletic ability is a confound. • When studying the effects of nutrition on aging, education level is a confound. • Matching is used to ensure equality of the measured factor across groups (not within group).
How To Match Subjects • Measure the matching factor and rank order the subjects according to it. • Example: best to worst runners. • Form pairs consisting of best and second-best, third-best and fourth-best, etc. • Randomly assign one member of pair to treatment group and one to control group. • During analysis, compare members of pairs.
Analysis of Matched Designs • When subjects are matched as a control procedure, each member of pair is compared with the other member of that pair. • Appropriate statistics: • Paired-sample t-test • Repeated-measures ANOVA
Randomized Block Design • When a subject characteristic is measured instead of controlled, then matching is used as an experimental procedure. • Demographic (subject) variables are used to form two or more groups. • Subjects are equated within groups by that variable. • Examples: sex, age, ethnicity, IQ, etc. • The subject variable is analyzed as a factor.
Analysis of Block Designs • The subject variable is treated as a factor. • Examine main effect and interactions. • Caution is needed during interpretation. • A subject variable cannot be considered causal because it was not manipulated. • Subject variables tell us how far the results may be generalized – who they apply to.
