Statistical reform: Evidence-based practice, meta-analyses, and single subject designs

• Published in: Psychology in the schools Vol. 44; no. 5; pp. 483 - 493
• Main Authors: Jenson, William R., Clark, Elaine, Kircher, John C., Kristjansson, Sean D.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.05.2007; John Wiley & Sons, Inc; Wiley
• Subjects: Biological and medical sciences; Effect Size; Effectiveness; Error of Measurement; Evidence based; Fundamental and applied biological sciences. Psychology; Hypothesis Testing; Intervention; Interventions; Meta Analysis; Methodology. Experimentation; Monte Carlo Methods; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychometrics. Statistics. Methodology; Research Design; Risk; School Psychologists; School psychology; Statistical Analysis; Statistics. Mathematics; Case study; Human; Statistical method; Methodology; Evidence-based practice; Review; School psychology; Metaanalysis
• ISSN: 0033-3085; 1520-6807
• DOI: 10.1002/pits.20240

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. Meta‐analysis 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. © 2007 Wiley Periodicals, Inc. Psychol Schs 44: 483–493, 2007.