Consistency in Single-Case ABAB Phase Designs: A Systematic Review

被引:6
|
作者
Tanious, Rene [1 ,2 ]
De, Tamal Kumar [1 ]
Michiels, Bart [1 ]
van den Noortgate, Wim [1 ]
Onghena, Patrick [1 ]
机构
[1] Katholieke Univ Leuven, Leuven, Belgium
[2] Katholieke Univ Leuven, Fac Psychol & Educ Sci, Tiensestr 102 box 3762, B-3000 Leuven, Belgium
关键词
single-case experimental designs; consistency; systematic review; ABAB; visual analysis; ON-TASK BEHAVIOR; OCCUPATIONAL-THERAPY INTERVENTION; AUTISM SPECTRUM DISORDER; MIDDLE SCHOOL STUDENTS; BASE-LINE DESIGNS; YOUNG-CHILDREN; SOCIAL STORIES; CEREBRAL-PALSY; PHYSICAL-ACTIVITIES; FUNCTIONAL-ANALYSIS;
D O I
10.1177/0145445519853793
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
The current article presents a systematic review of consistency in single-case ABAB phase designs. We applied the CONsistency of DAta Patterns (CONDAP) measure to a sample of 460 data sets retrieved from 119 applied studies published over the past 50 years. The main purpose was to (a) identify typical CONDAP values found in published ABAB designs and (b) develop interpretational guidelines for CONDAP to be used for future studies to assess the consistency of data patterns from similar phases. The overall distribution of CONDAP values is right-skewed with several extreme values to the right of the center of the distribution. The B-phase CONDAP values fall within a narrower range than the A-phase CONDAP values. Based on the cumulative distribution of CONDAP values, we offer the following interpretational guidelines in terms of consistency: very high, 0 <= CONDAP <= 0.5; high, 0.5 < CONDAP <= 1; medium, 1 < CONDAP < 1.5; low, 1.5 < CONDAP <= 2; very low, CONDAP > 2. We give examples of combining CONDAP benchmarks with visual analysis of single-case ABAB phase designs and conclude that the majority of data patterns (41.2%) in published ABAB phase designs is medium consistent.
引用
收藏
页码:1377 / 1406
页数:30
相关论文
共 50 条
  • [41] Tutorial: Artificial Neural Networks to Analyze Single-Case Experimental Designs
    Lanovaz, Marc J.
    Bailey, Jordan D.
    PSYCHOLOGICAL METHODS, 2024, 29 (01) : 202 - 218
  • [42] Further Analysis of Advanced Quantitative Methods and Supplemental Interpretative Aids with Single-Case Experimental Designs
    Falligant, John Michael
    Kranak, Michael P.
    Hagopian, Louis P.
    PERSPECTIVES ON BEHAVIOR SCIENCE, 2022, 45 (01) : 77 - 99
  • [43] Further Analysis of Advanced Quantitative Methods and Supplemental Interpretative Aids with Single-Case Experimental Designs
    John Michael Falligant
    Michael P. Kranak
    Louis P. Hagopian
    Perspectives on Behavior Science, 2022, 45 : 77 - 99
  • [44] Investigating immediacy in multiple-phase-change single-case experimental designs using a Bayesian unknown change-points model
    Natesan Batley, Prathiba
    Minka, Tom
    Hedges, Larry Vernon
    BEHAVIOR RESEARCH METHODS, 2020, 52 (04) : 1714 - 1728
  • [45] Single-case Design Studies in Children with Cerebral Palsy: A Scoping Review
    Beckers, Laura W. M. E.
    Stal, Rosalinde A.
    Smeets, Rob J. E. M.
    Onghena, Patrick
    Bastiaenen, Caroline H. G.
    DEVELOPMENTAL NEUROREHABILITATION, 2020, 23 (02) : 73 - 105
  • [46] All Good Things Come in Threes: A Systematic Review and Delphi Study on the Advances and Challenges of Ambulatory Assessments, Network Analyses, and Single-Case Experimental Designs
    Schemer, Lea
    Glombiewski, Julia Anna
    Scholten, Saskia
    CLINICAL PSYCHOLOGY-SCIENCE AND PRACTICE, 2023, 30 (01) : 95 - 107
  • [47] Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots
    Manolov, Rumen
    Tanious, Rene
    BEHAVIOR MODIFICATION, 2022, 46 (03) : 581 - 627
  • [48] Assessing consistency of effects when applying multilevel models to single-case data
    Rumen Manolov
    John M. Ferron
    Behavior Research Methods, 2020, 52 : 2460 - 2479
  • [49] Direct Training to Improve Educators' Treatment Integrity: A Systematic Review of Single-Case Design Studies
    Fallon, Lindsay M.
    Kurtz, Kathryn D.
    Mueller, Marlana R.
    SCHOOL PSYCHOLOGY QUARTERLY, 2018, 33 (02) : 169 - 181
  • [50] Monte Carlo Analyses for Single-Case Experimental Designs: An Untapped Resource for Applied Behavioral Researchers and Practitioners
    Friedel, Jonathan E.
    Cox, Alison
    Galizio, Ann
    Swisher, Melissa
    Small, Megan L.
    Perez, Sofia
    PERSPECTIVES ON BEHAVIOR SCIENCE, 2022, 45 (01) : 209 - 237