Getting the best out of single case data
Abstract
This article analyzes the value of single-case designs (SCDs) in the fields of personalized healthcare and education, highlighting their growing relevance in research with small samples. In these designs, the same subjects are repeatedly assessed based on the same outcomes, which offers methodological advantages but also poses statistical challenges.
The author compares three common statistical methods for analyzing SCD data: randomization tests, Bayesian percentage of non-overlapping data (PAND-B), and time series regression (TSR). While PAND-B allows for the incorporation of prior knowledge and avoids questionable assumptions required by TSR, it has limitations: it does not adequately consider ordinal or quantitative information and provides invalid estimates when samples are unequal.
To overcome these shortcomings, the use of the Generalized Relative Effect (GRE), based on the Brunner-Munzel relative effect statistic, is proposed. This approach retains applicability to all levels of measurement (nominal, ordinal, and quantitative) and allows for the integration of prior theoretical or empirical knowledge through Bayesian prior distributions.
Downloads
Metrics
-
Abstract139
-
pdf92
References
McDonald S, Vieira R, Johnston DW. Analysing N-of-1 observational data in health psychology and behavioural medicine: a 10-step tutorial for beginners. Health Psych Behav Med. 2020, 8(1), 32-54. https://doi.org/10.1080/21642850.2019.1711096
Parker RI, Brossart DF. Evaluating single-case research data: a comparison of seven statistical methods. Behav Therap. 2003, 34, 189-211. https://doi.org/10.1016/S0005-7894(03)80013-8
Tanious R, Onghena P. A systematic review of applied single-case research published between 2016 and 2018: study designs, randomization, data aspects, and data analysis. Behav Res Meth. 2021, 53, 1371-84. https://doi.org/10.3758/s13428-020-01502-4
Onghena P. One by one: the design and analysis of replicated randomized single-case experiments. In: Van de Schoot R & Miocevic M (Eds.), Small sample size solutions: a guide for applied researchers and practitioners (chapter 6). New York: Routledge; 2020. https://www.taylorfrancis.com/chapters/oa-edit/10.4324/9780429273872-8/one-one-patrick-onghena
Leppink J. Diseños experimentales de caso único y sus estadísticas [Single case experimental designs and their statistics]. Rev Esp Educ Med, 2022, 3(3), 1-3. https://doi.org/10.6018/edumed.528911
Leppink J. El efecto relative generalizado (ERG): su introducción y un tutorial en R [The generalized relative effect (GRE): its introduction and a tutorial in R. CERES HEMJ. 2025, 3(2), e79. https://doi.org/10.62234/ceresv3n2-002
Karch JD. Psychologists should use Brunner-Munzel’s instead of Mann-Whitney’s U test as the default nonparametric procedure. Adv Methods Pract Psychol Sci. 2021, 4(2). https://doi.org/10.1177/2515245921999602
R Core Team. R: A language and environment for statistical computing (version 4.4.1) [Computer software]. Retrieved (May 24, 2025) from: https://www.r-project.org
Copyright (c) 2025 Servicio de Publicaciones de la Universidad de Murcia
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The works published in this magazine are subject to the following terms:
1. The Publications Service of the University of Murcia (the publisher) preserves the economic rights (copyright) of the published works and favors and allows them to be reused under the use license indicated in point 2.
2. The works are published under a Creative Commons Attribution-NonCommercial-NoDerivative 4.0 license.
3. Self-archiving conditions. Authors are allowed and encouraged to disseminate electronically the pre-print versions (version before being evaluated and sent to the journal) and / or post-print (version evaluated and accepted for publication) of their works before publication , since it favors its circulation and earlier diffusion and with it a possible increase in its citation and reach among the academic community.
