Literature Review of Justifications Provided for Single-Case Quantification Techniques

• In Event: Analysis and Meta-Analysis of Single-Case Experimental Data: Methodological Developments

Abstract

There are currently many different quantification techniques available for use with single-case designs. It is essential that a proper quantification technique is selected for use; using an inappropriate technique can lead to a misrepresentation of the true intervention effect. (Manolov et al., 2021). The justifications that are provided for using quantification techniques can help those who are unfamiliar with the techniques and understand the appropriateness of the techniques. Past research has found that researchers do not commonly provide justifications for using the quantification techniques that they choose (Solomon, 2014). The purpose of this literature review is to review past single-case literature to determine a) whether researchers are providing justifications for using quantification techniques and b) the quality of the justifications that are provided.
For this literature review, the search terms ("single case" OR "single subject" OR "multiple baseline" OR "multiple probe" OR "withdrawal design" OR "reversal design" OR "ABAB design" OR "changing criterion design" OR "alternating treatment" OR "multielement design") were entered into PsycINFO. The search year was limited to 2019, and grey literature (e.g., dissertations) was included in the search. The search resulted in a total of 951 articles to be reviewed and considered for inclusion in the literature search results. Single-case designs that did not utilize a quantitative analysis technique (e.g., only used visual analysis, means and standard deviations) were excluded.
For this study, the justifications that are provided in the identified studies will be coded into categories that are well-grounded in previous single-case design literature (e.g., Manolov & Moeyaert, 2017; Manolov et al., 2021). These categories are a) research question and quantification desired; b) design features; c) data characteristics; and d) expected data patterns in the baseline and intervention condition. Subcategories will be added to these four dimensions throughout the coding process, and the justifications provided in the articles will be coded into these subcategories. Subcategories will be refined through discussion and clarification between two researchers of this study. To evaluate the appropriateness of the justifications that are provided in the articles, the justifications from the articles will be compared to information provided from the original founding articles for each of the quantification techniques.

Authors

• Joelle Fingerhut, Rutgers University

• Mariola Moeyaert, University at Albany - SUNY

• Rumen Manolov, University of Barcelona

• Xinyun Xu, University at Albany-State University of New York