Single-case effect size calculation: Comparing regression and non-parametric approaches across previously published reading intervention data sets

Growing from demands for accountability and research-based practice in the field of education, there is recent focus on developing standards for the implementation and analysis of single-case designs. Effect size methods for single-case designs provide a useful way to discuss treatment magnitude in the context of individual intervention. Although a standard effect size methodology does not yet exist within single-case research, panel experts recently recommended pairing regression and non-parametric approaches when analyzing effect size data. This study compared two single-case effect size methods: the regression-based, Allison-MT method and the newer, non-parametric, Tau-U method. Using previously published research that measured the Words read Correct per Minute (WCPM) variable, these two methods were examined by comparing differences in overall effect size scores and rankings of intervention effect. Results indicated that the regression method produced significantly larger effect sizes than the non-parametric method, but the rankings of the effect size scores had a strong, positive relation. Implications of these findings for research and practice are discussed.     

A Field-Tested Task Analysis for Creating Single-Subject Graphs Using Microsoft<Superscript>®</Superscript> Office Excel

Creating single-subject (SS) graphs is challenging for many researchers and practitioners because it is a complex task with many steps. Although several authors have introduced guidelines for creating SS graphs, many users continue to experience frustration. The purpose of this article is to minimize these frustrations by providing a field-tested task analysis for creating SS graphs using Microsoft^® Office Excel. Results from the field test are presented and the task analysis, which includes steps for creating a variety of SS graphs, is provided. The article includes various illustrations, a list of prerequisite skills, tips, and troubleshooting items.     

Evaluating single-case research data for systematic review: A commentary for the special issue

The purpose of this commentary is to provide observation on the statistical procedures described throughout this special section from the perspective of researchers with experience in conducting systematic reviews and meta-analyses of single-case research to address issues of evidence-based practice. It is our position that both visual and statistical analyses are complimentary methods for evaluating single-case research data for these purposes. Given the recent developments regarding the use of single-case research to inform evidence-based practice and policy, the developments described in the present issue will be contextualized within the need for a widely accepted process for data evaluation to assist with extending the impact of single-case research. The commentary will, therefore, begin with providing an overview of the conceptual underpinnings of a systematic review of single-case research and will be followed by a discussion of several features that are essential to the development of a conceptually sound and widely used statistical procedure for single-case research. The commentary will conclude with recommendations and guidelines for the use of both visual and statistical analyses within primary research reports and recommendations for future research.     

A systematic evaluation of token economies as a classroom management tool for students with challenging behavior

A two-part systematic review was undertaken to assess the effectiveness of token economies in increasing rates of appropriate classroom behavior for students demonstrating behavioral difficulties. The first part of the review utilized the recently published What Works Clearinghouse (WWC) standards for evaluating single-subject research to determine the extent to which eligible studies demonstrated sufficient evidence to classify the token economy as an evidence-based practice. The second part of the review employed meta-analytic techniques across four different types of effect sizes to evaluate the quantitative strength of the findings. Methodological strengths and weaknesses across the studies were systematically investigated. Results indicated that the extant research on token economies does not provide sufficient evidence to be deemed best-practice based on the WWC criteria.