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Abstract

A variety of statistical procedures have been recommended for the evaluation of treatment effects in correlated sequential measures. Analysis of covariance is generally favored and recognized as an appropriate procedure although other techniques have been suggested as simpler and essentially equivalent. This paper examines several of these techniques and reports results of a Monte Carlo study comparing Type I error rate per experiment for the following procedures (1) analysis of variance of difference scores, (2) analysis of variance of final scores, and (3) analysis of covariance. It is shown that of these procedures only analysis of covariance produces error rates close to chance. Analysis of covariance is recommended for the assessment of treatment effects in data with pretreatment-posttreatment measures of the dependent variable and where experimental control is not possible.

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Estimation of Treatment Effect and Measurement of Change

Abstract

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