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.

Visual analysis of single-case research is commonly described as a gold standard, but it is often unreliable. Thus, an objective tool for applying visual analysis is necessary, as an alternative to the Conservative Dual Criterion, which presents some drawbacks. The proposed free webbased tool enables assessing change in trend and level between two adjacent phases, while taking data variability into account. The application of the tool results in (a) a dichotomous decision regarding the presence or absence of an immediate effect, a progressive or delayed effect, or an overall effect and (b) a quantification of overlap. The proposal is evaluated by applying it to both real and simulated data, obtaining favorable results. The visual aid and the objective rules are expected to make visual analysis more consistent, but they are not intended as a substitute for the analysts' judgment, as a formal test of statistical significance, or as a tool for assessing social validity.

Reporting guidelines, such as the Consolidated Standards of Reporting Trials (CONSORT) Statement, improve the reporting of research in the medical literature (Turner et al., 2012). Many such guidelines exist and the CONSORT Extension to Nonpharmacological Trials (Boutron et al., 2008) provides suitable guidance for reporting between-groups intervention studies in the behavioral sciences. The CONSORT Extension for N-of-1 Trials (CENT 2015) was developed for multiple crossover trials with single individuals in the medical sciences (Shamseer et al., 2015; Vohra et al., 2015), but there is no reporting guideline in the CONSORT tradition for single-case research used in the behavioral sciences. We developed the Single-Case Reporting guideline In BEhavioural interventions (SCRIBE) 2016 to meet this need. This Statement article describes the methodology of the development of the SCRIBE 2016, along with the outcome of 2 Delphi surveys and a consensus meeting of experts. We present the resulting 26-item SCRIBE 2016 checklist. The article complements the more detailed SCRIBE 2016 Explanation and Elaboration article (Tate et al., 2016) that provides a rationale for each of the items and examples of adequate reporting from the literature. Both these resources will assist authors to prepare reports of single-case research with clarity, completeness, accuracy, and transparency. They will also provide journal reviewers and editors with a practical checklist against which such reports may be critically evaluated.

The current study proposes a new procedure for separately estimating slope change and level change between two adjacent phases in single-case designs. The procedure eliminates baseline trend from the whole data series before assessing treatment effectiveness. The steps necessary to obtain the estimates are presented in detail, explained, and illustrated. A simulation study is carried out to explore the bias and precision of the estimators and compare them to an analytical procedure matching the data simulation model. The experimental conditions include 2 data generation models, several degrees of serial dependence, trend, and level and/or slope change. The results suggest that the level and slope change estimates provided by the procedure are unbiased for all levels of serial dependence tested and trend is effectively controlled for. The efficiency of the slope change estimator is acceptable, whereas the variance of the level change estimator may be problematic for highly negatively autocorrelated data series.

Alternating treatments designs (ATDs) have received comparatively less attention than other single-case experimental designs in terms of data analysis, as most analytical proposals and illustrations have been made in the context of designs including phases with several consecutive measurements in the same condition. One of the specific features of ATDs is the rapid (and usually randomly determined) alternation of conditions, which requires adapting the analytical techniques. First, we review the methodologically desirable features of ATDs, as well as the characteristics of the published single-case research using an ATD, which are relevant for data analysis. Second, we review several existing options for ATD data analysis. Third, we propose 2 new procedures, suggested as alternatives improving some of the limitations of extant analytical techniques. Fourth, we illustrate the application of existing techniques and the new proposals in order to discuss their differences and similarities. We advocate for the use of the new proposals in ATDs, because they entail meaningful comparisons between the conditions without assumptions about the design or the data pattern. We provide R code for all computations and for the graphical representation of the comparisons involved. (PsycINFO Database Record