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AbstractShould evaluation research move toward the status of an independent field or discipline or should it be nurtured as a subspecialty within academic and problem areas? Since the field is new, it may be too early to settle on any single model for training.

AbstractKey concepts of the volume are summarized in the editor's concluding chapter.

Growth curve analysis provides important informational benefits regarding intervention outcomes over time. Rarely, however, should outcome trajectories be assumed to be linear. Instead, both the shape and the slope of the growth curve can be estimated. Non-linear growth curves are usually modeled by including either higher-order time variables or orthogonal polynomial contrast codes. Each has limitations (multicollinearity with the first, a lack of coefficient interpretability with the second, and a loss of degrees of freedom with both) and neither encourages direct testing of alternative hypothesized curve shapes. Especially in studies with relatively small samples it is likely to be useful to preserve as much information as possible at the individual level. This article presents a step-by-step example of the use and testing of hypothesized curve shapes in the estimation of growth curves using hierarchical linear modeling for a small intervention study. DOI:10.2458/azu_jmmss_v3i2_herman