title: Recommendations for Choosing Single-Case Data Analytical Techniques.
abstract: The current paper responds to the need to provide guidance to applied single-case researchers regarding the possibilities of data analysis. The amount of available single-case data analytical techniques has been growing during recent years and a general overview, comparing the possibilities of these techniques, is missing. Such an overview is provided that refers to techniques that yield results in terms of a raw or standardized difference and procedures related to regression analysis, as well as nonoverlap and percentage change indices. The comparison is provided in terms of the type of quantification provided, data features taken into account, conditions in which the techniques are appropriate, possibilities for meta-analysis, and evidence available on their performance. Moreover, we provide a set of recommendations for choosing appropriate analysis techniques, pointing at specific situations (aims, types of data, researchers' resources) and the data analytical techniques that are most appropriate in these situations. The recommendations are contextualized using a variety of published single-case data sets in order to illustrate a range of realistic situations that researchers have faced and may face in their investigations.
authors.affiliation: University of Barcelona. Electronic address: rrumenov13@ub.edu.
authors.forename: Rumen
authors.initials: R
authors.lastname: Manolov
authors.affiliation: State University of New York.
authors.forename: Mariola
authors.initials: M
authors.lastname: Moeyaert
mesh_terms: D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design; D035842:Research Subjects
keywords: data analysis; recommendations; single-case designs