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Nonequivalent Group Designs

Robert A. Karabinus

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Abstract

In pretest-posttest nonequivalent group designs, naive use of analysis of covariance or multiple regression can lead to Type I errors. In the evaluation of a Title IV-C, ESEA project, thorough analysis of the interaction term of a repeated model analysis of variance was shown to help identify areas of significant change. Meaningful differences were also supported by the standardized difference score, Effect Size. It was suggested that a two-fold increase of Effect Size from pretest to posttest might be an effective guide in the determination of meaningfully significant change.

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What this paper is about

In pretest-posttest nonequivalent group designs, naive use of analysis of covariance or multiple regression can lead to Type I errors. In the evaluation of a Title IV-C, ESEA project, thorough analysis of the interaction term of a repeated model analysis of variance was shown to help identify areas of significant change. Meaningful differences were also supported by the standardized difference score, Effect Size. It was suggested that a two-fold increase of Effect Size from pretest to posttest might be an effective guide in the determination of meaningfully significant change.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In pretest-posttest nonequivalent group designs, naive use of analysis of covariance or multiple regression can lead to Type I errors. In the evaluation of a Title IV-C, ESEA project, thorough analysis of the interaction term of a repeated model analysis of variance was shown to help identify areas of significant change. Meaningful differences were also supported by the standardized difference score, Effect Size. It was suggested that a two-fold increase of Effect Size from pretest to posttest might be an effective guide in the determination of meaningfully significant change.

Key concepts: Analysis of covariance, Statistics, Analysis of variance, Repeated measures design, Regression analysis, Psychology, Covariance, Variance (accounting)

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