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Psychophysiologists often use repeated measures analysis of variance (RMANOVA) and multivariate analysis of variance (MANOVA) to analyze data collected in repeated measures research designs. ANOVA and MANOVA are nomothetic approaches that focus on group means. Newer multilevel modeling techniques are more informative than ANOVA because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study. This article was written as an introduction to growth curve modeling for applied researchers. A growth model is defined that can be used in place of RMANOVAs and MANOVAs for single-group and mixed repeated measures designs. The model is expanded to test and control for the effects of baseline levels of physiological activity on stimulus-specific responses. Practical, conceptual, and statistical advantages of growth curve modeling are discussed.

Evidence-based practice approaches to interventions has come of age and promises to provide a new standard of excellence for school psychologists. This article describes several definitions of evidence-based practice and the problems associated with traditional statistical analyses that rely on rejection of the null hypothesis for the establishment of invention effectiveness. Metaanalysis as an approach to ascertain EBPs is reviewed along with the inherent difficulties associated with single subject design research such as autocorrelations. Four meta-analytic approaches are reviewed which include Percentage of Nonoverlapping Data points (PND), the Busk and Serlin: Assumption models, ITSACORR, and Hierarchical Linear Modeling (HLM). HLM is offered as the most promising approach for the analysis for single subject designs. Monte Carlo simulations are modeled with varying degrees of autocorrelations, differing numbers of data points, and simulated effects sizes to show that HLM is an acceptable approach for controlling the risk of Type I errors.

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