Further developments in summarising and meta-analysing single-case data: An illustration with neurobehavioural interventions in acquired brain injury

Abstract

Data analysis for single-case designs is an issue that has prompted many researchers to propose a variety of alternatives, including use of randomisation tests, regression-based procedures, and standardised mean difference. Another option consists in computing unstandardised or raw differences between conditions: the changes in slope and in level, or the difference between the projected baseline (including trend) and the actual treatment phase measurements. Apart from the strengths of these procedures (potentially easier interpretation clinically, separate estimations and an overall quantification of effects, reasonable performance), they require further development, such as (a) creating extensions for dealing with methodologically strong designs such as multiple baseline, (b) achieving comparability across studies and making possible meta-analytical integrations, and (c) implementing software for the extensions. The proposals are illustrated herein in the context of a meta-analysis of 28 studies on (neuro)behavioural interventions in adults who have challenging behaviours after acquired brain injury.

Keywords:

• Single case
• Effect size
• Multiple-baseline designs
• Challenging behaviours
• Rehabilitation

ORCID

Rumen Manolov http://orcid.org/0000-0002-9387-1926

Notes

¹For more details about how the previously developed R code can be used, consult the original articles (Manolov & Solanas, 2013; Solanas et al., 2010) and the supplementary material of the article by Manolov et al. (2014).

²For instance, a criticism by Baron and Derenne (2000) of an early quantitative integration of SCED results was directed towards using more than one outcome from an experiment.

Additional information

Funding

This work was partially supported by the Agència de Gestió d'Ajust Universitaris i de Recerca de la Generalitat de Catalunya [grant 2014SGR71].