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When changes in educational or psychological status are being measured, every subject in the sample must be observed on several chronologically successive occasions. In the pursuit of such longitudinal data, traditional researchers have been content to administer only a pre-test and a post-test (thus collecting two waves of data on each subject). More recently, however, methodologists have argued that multiwave data (i.e., more than two waves) must be collected for the effective measurement of change. Multi-wave data allows a suitable mathematical model to be fitted to each of the individual growth records as a way of summarizing the growth of each subject. Subsequent investigations of between-individual differences in growth can then be based on the results of these fits. In this article, individual growth-modeling permits the reliability of change measurement to be examined. This reliability is shown to depend upon three factors: the magnitude of the inter-individual heterogeneity in true growth, the size of the measurement-error variance, and the number of waves of data that have been collected. The paper demonstrates that dramatic increases in the reliability of change measurement can be achieved by collecting relatively few additional waves of data, a finding that has considerable import for the informed design of longitudinal studies of individual change.
John B. Willett (Fri,) studied this question.