Affordable Access

Publisher Website

How to assess and take into account trend in single-case experimental design data.

Authors
  • Manolov, Rumen1
  • Lebrault, Hélène2, 3, 4
  • Krasny-Pacini, Agata5, 6, 7
  • 1 Department of Social Psychology and Quantitative Psychology, Faculty of Psychology Barcelona, Spain. , (Spain)
  • 2 Rehabilitation department for children with congenital neurological injury, Saint Maurice Hospitals Saint Maurice, France. , (France)
  • 3 Sorbonne Université, Laboratoire d'Imagerie Biomédicale, LIB Paris, France. , (France)
  • 4 GRC 24, Handicap Moteur et Cognitif et Réadaptation (HaMCRe); Sorbonne Université Paris, France. , (France)
  • 5 Pôle de Médecine Physique et de Réadaptation, Institut Universitaire de réadaptation Clemenceau StrasbourgHôpitaux Universitaires de Strasbourg, UF 4372, Strasbourg, France. , (France)
  • 6 Unité INSERM 1114 Neuropsychologie Cognitive et Physiopathologie De La Schizophrénie, Département de Psychiatrie, Hôpital Civil de Strasbourg, Strasbourg, France. , (France)
  • 7 Université de Strasbourg, Faculté de Médecine Strasbourg.
Type
Published Article
Journal
Neuropsychological Rehabilitation
Publisher
Informa UK (Taylor & Francis)
Publication Date
Apr 01, 2024
Volume
34
Issue
3
Pages
388–429
Identifiers
DOI: 10.1080/09602011.2023.2190129
PMID: 36961228
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

One of the data features that are expected to be assessed when analyzing single-case experimental designs (SCED) data is trend. The current text deals with four different questions that applied researchers can ask themselves when assessing trend and especially when dealing with improving baseline trend: (a) What options exist for assessing the presence of trend?; (b) Once assessed, what criterion can be followed for deciding whether it is necessary to control for baseline trend?; (c) What strategy can be followed for controlling for baseline trend?; and (d) How to proceed in case there is baseline trend only in some A-B comparisons? Several options are reviewed for each of these questions in the context of real data, and tentative recommendations are provided. A new user-friendly website is developed to implement the options for fitting a trend line and a criterion for selecting a specific technique for that purpose. Trend-related and more general data analytical recommendations are provided for applied researchers.Trial registration: ClinicalTrials.gov identifier: NCT04560777.

Report this publication

Statistics

Seen <100 times