Recent advances in methods for analysis of longitudinal data arid incomplete repeated measures have been in the area of maximum likelihood (ML) and restricted maximum likelihood (REML) methods (e.g., Laird and Ware, 1982 Biometrics, Jennrich and Schluchter, 1986 Biometrics). This paper outlines the ML and REML approaches to the analysis of incomplete repeated measures data and growth curves, and then examines methods for small-sample adjustment of asymptotic Wald-type chi-square tests constructed from ML and REML estimates under four different assumed covariance structures. These adjustments involve transformation of the Wald Ghi-square statistic to an approximate F-statistic. In certain cases when data are complete and balanced, the transformed test statistics have exact F-distribution under the null hypothesis. The first three covariance structures: (1) Compound Symmetry, (2) First-Order Autoregressive, and (3) Multivariate (unstructured), are examined in the context of the analysis of a repeated measur...