Threats to Internal Validity in Multiple-Baseline Design Variations

Multiple baseline designs—both concurrent and nonconcurrent—are the predominant experimental design in modern applied behavior analytic research and are increasingly employed in other disciplines. In the past, there was significant controversy regarding the relative vigor of concurrent and nonconcurrent multiple baseline designs. The consensus in recent textbooks and methodological papers is that nonconcurrent designs are less rigorous than concurrent designs because of their presumed limited ability to address the threat of coincidental events (i.e., history). This skepticism of nonconcurrent designs stems from an emphasis on the importance of across-tier comparisons and relatively low importance placed on replicated within-tier comparisons for addressing threats to internal validity and establishing experimental control. In this article, we argue that the primary reliance on across-tier comparisons and the resulting deprecation of nonconcurrent designs are not well-justified. In this article, we first define multiple baseline designs, describe common threats to internal validity, and delineate the two bases for controlling these threats. Second, we briefly summarize historical methodological writing and current textbook treatment of these designs. Third, we explore how concurrent and nonconcurrent multiple baselines address each of the main threats to internal validity. Finally, we make recommendations for rigorous use, reporting, and evaluation of multiple baseline designs

Emerging Opportunities in Higher Education: Applied Behavior Analysis and Autism

This article discusses applying behavior analysis to autism

A Framework for the Implementation of Evidence-Based Practice in Schools

Colloquium presented at the University of Akureyr

Improving the Implementation of Function-Based Interventions Using an Online Data Management Application

The success of behavioral interventions depends not just on the quality of procedures employed, but on the extent to which procedures are implemented. This study used a multiple-baseline across participants design to assess the impact of an online data management application on the fidelity and impact of individual student behavior support plans in typical school contexts. Three students with patterns of problem behavior and their supporting adults participated in the study. The research question examined if a functional relation exists between use of (a) performance self-assessment and (b) student impact assessment via an online data management system on the fidelity of behavior support plan implementation by adults and improvement in academic engagement and problem behavior by students. Results indicate the efficacy of the treatment package in improving treatment fidelity, decreasing student problem behavior, and increasing student academic engagement. Potential contributions of the study are discussed in terms of establishing efficient data systems for schools to use in monitoring staff and student behavior and using these data in a meaningful way that result in improved student outcomes and sustained behavior change

Positive Behavior Supports: Promoting Effective Implementation for Your Classroom

In school-wide positive behavioral interventions supports (SWPBIS), a continuum of behavior support is provided to students in general and special education settings. However, it can be challenging for teachers to determine (a) which support strategies to implement given the unique characteristics of their classrooms, (b) how to ensure those strategies are implemented with fidelity, and (c) how to evaluate and monitor the success of those strategies. This presentation will address these challenges by offering practical suggestions. Participants will learn about evidence-based behavior support strategies as they relate to aspects of the instructional environment in general and special education settings, with an emphasis on selecting strategies that are best matched to the context of the classroom. Participants will also learn about ways to assess fidelity and how to use data to evaluate the effectiveness of behavior support strategies