Cited by 352Behavioral and Psychological StudiesData Visualization and AnalyticsHuman Resource Development and Performance Evaluation

Visual aids and structured criteria for improving visual inspection and interpretation of single-case designs

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Fisher, Wayne W., Kelley, Michael E., Lomas, Joanna E.·2003·Journal of Applied Behavior Analysis

Abstract

Because behavior analysis is a data-driven process, a critical skill for behavior analysts is accurate visual inspection and interpretation of single-case data. Study 1 was a basic study in which we increased the accuracy of visual inspection methods for A-B designs through two refinements of the split-middle (SM) method called the dual-criteria (DC) and conservative dual-criteria (CDC) methods. The accuracy of these visual inspection methods was compared with one another and with two statistical methods (Allison & Gorman, 1993; Gottman, 1981) using a computer-simulated Monte Carlo study. Results indicated that the DC and CDC methods controlled Type I error rates much better than the SM method and had considerably higher power (to detect real treatment effects) than the two statistical methods. In Study 2, brief verbal and written instructions with modeling were used to train 5 staff members to use the DC method, and in Study 3, these training methods were incorporated into a slide presentation and were used to rapidly (i.e., 15 min) train a large group of individuals (N = 87). Interpretation accuracy increased from a baseline mean of 55% to a treatment mean of 94% in Study 2 and from a baseline mean of 71% to a treatment mean of 95% in Study 3. Thus, Study 1 answered basic questions about the accuracy of several methods of interpreting A-B designs; Study 2 showed how that information could be used to increase the accuracy of human visual inspectors; and Study 3 showed how the training procedures from Study 2 could be modified into a format that would facilitate rapid training of large groups of individuals to interpret single-case designs.

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  1. Crosbie, J. (1995). Interrupted time-series analysis with short series: Why it is problematic; how it can be improved. In J. M. Gottman & G. Sackett (Eds.), The analysis of change (pp. 361-395).
  2. Page, T. J., Iwata, B. A., & Reid, D. H. (1982). Py- ramidal training: A large-scale application with in- stitutional staff. Journal of Applied Behavior Anal- ysis, 15, 335-351.

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Authors
Fisher, W. W., Kelley, M. E., & Lomas, J. E.
Affiliations
Louisiana State University (US)
Corresponding author
Fisher, Wayne W.
Venue
Journal of Applied Behavior Analysis
Publisher
Wiley
Published
2003-09
Volume
36
Issue
3
Pages
387-406
ISSN
  • 0021-8855 (ISSN-L)
  • 1938-3703
OpenAlex
W2048320738
Cited by
351
References
28
Citations by year
2026: 19 · 2025: 11 · 2024: 15 · 2023: 25 · 2022: 29 · 2021: 33 · 2020: 32 · 2019: 16 · 2018: 14 · 2017: 22 · 2016: 13 · 2015: 21 · 2014: 22 · 2013: 16 · 2012: 14
FWCI
3.65
Type
journal article
Language
en
Open access
Locations
  • Journal of Applied Behavior Analysis (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Health and Human Services (5 R01 HD37837-03)
  • National Institutes of Health (5r01hd037837-03)
  • National Institute of Child Health and Human Development
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (5 R01 HD37837-03)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Behavioral and Psychological Studies
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
  • Social Sciences › Psychology › Applied Psychology › Human Resource Development and Performance Evaluation
Keywords
visual inspection, single-case experimental design, type I error control, statistical power, interpreting accuracy, employee training, modeling, behavior analysis
MeSH terms
Audiovisual Aids, Behavior Therapy, Computer Graphics, Computer Simulation, Humans, Mathematical Computing, Monte Carlo Method, Outcome and Process Assessment, Health Care, Regression Analysis, Software, Reproducibility of Results, Observer Variation

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