Legal mandates and best practice recommendations for the education of students with autism spectrum disorders (ASD) emphasize the importance of systematic, ongoing observational data collection in order to monitor progress and demonstrate accountability. The absence of such documentation in decision-making on instructional objectives indicates a weakness in bridging the research-to-practice gap in special education. Utilizing a multiple baseline design across participants, the current study evaluated the effects of a prototypical teacher training program (i.e., workshop, checklist, in-classroom training with feedback, and maintenance with a thinned schedule of feedback) on the frequency of data collection on core deficits of ASD and the use of data-based decision-making. Results indicate increases in daily mean frequency of data collection following intervention. Maintenance and generalization indicates variable responding across participants. Effect size (Cohen's d) indicates a large, clinically significant effect of the training program. Results are discussed in relation to training models, maintenance, and future research.

This study examined the bias and precision of four residualized variable validity estimates (C0, C1, C2, C3) across a number of study conditions. Validity estimates that considered measurement error, correlations among error scores, and correlations between error scores and true scores (C3) performed the best, yielding no estimates that were practically significantly different than their respective population parameters, across study conditions. Validity estimates that considered measurement error and correlations among error scores (C2) did a good job in yielding unbiased, valid, and precise results. Only in a select number of study conditions were C2 estimates unable to be computed or produced results that had sufficient variance to affect interpretation of results. Validity estimates based on observed scores (C0) fared well in producing valid, precise, and unbiased results. Validity estimates based on observed scores that were only corrected for measurement error (C1) performed the worst. Not only did they not reliably produce estimates even when the level of modeled correlated error was low, C1 produced values higher than the theoretical limit of 1.0 across a number of study conditions. Estimates based on C1 also produced the greatest number of conditions that were practically significantly different than their population parameters.