Abstract
Classical methods of repeated measures analysis of variance and growth curve analysis require balanced data sets. Typically, in repeated measures analysis, a group or groups of subjects are all measured under various treatment conditions (Winer, 1971). The order of the treatments are randomized for each subject, and there should be no missing observations. In growth curve analysis, all subjects are measured at the same times, and, again, there should be no missing data (Grizzle and Allen, 1961).
This work was supported by the National Institute of General Medical Studies, Grant Number GM38519.
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Jones, R.H. (1994). Longitudinal Data Models with Fixed and Random Effects. In: Bozdogan, H., et al. Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0800-3_11
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DOI: https://doi.org/10.1007/978-94-011-0800-3_11
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