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AbstractOne of the commonly used single-case experimental designs (SCEDs) in education research is the ABAB design, commonly referred to as reversal design. This design is applicable when the studied behavior is reversible (i.e., reverses to baseline levels when the intervention is withdrawn) and it allows for a within-unit replication of the “basic effect”, that is, a comparison between two adjacent conditions (with and without an active intervention). Methodologically, one of its strengths is to provide three phase change moments to demonstrate experimental control. However, the withdrawal of the intervention (i.e., the comparison between the first intervention phase and the second baseline phase) has not received sufficient attention in the SCED literature. The aim of the current text is to propose quantifications that enable using the information from the withdrawal. Specifically, the quantifications allow for assessing the degree to which withdrawing the intervention leads to initial baseline levels as well as incorporating the reversal in an overall effect size. The proposals are illustrated with several real educational science data sets, including both within-unit analysis and replication of the ABAB design across units, and representing different degrees of reversal of the target behavior when the intervention is withdrawn. [ABSTRACT FROM AUTHOR]

One of the data features that are expected to be assessed when analyzing single-case experimental designs (SCED) data is trend. The current text deals with four different questions that applied researchers can ask themselves when assessing trend and especially when dealing with improving baseline trend: (a) What options exist for assessing the presence of trend?; (b) Once assessed, what criterion can be followed for deciding whether it is necessary to control for baseline trend?; (c) What strategy can be followed for controlling for baseline trend?; and (d) How to proceed in case there is baseline trend only in some A-B comparisons? Several options are reviewed for each of these questions in the context of real data, and tentative recommendations are provided. A new user-friendly website is developed to implement the options for fitting a trend line and a criterion for selecting a specific technique for that purpose. Trend-related and more general data analytical recommendations are provided for applied researchers. Trial registration: ClinicalTrials.gov identifier: NCT04560777 [ABSTRACT FROM AUTHOR]

There are currently a multitude of quantification techniques that have been developed for use with single-case designs. As a result, choosing an appropriate quantification technique can be overwhelming and it can be difficult for researchers to properly describe and justify their use of quantification techniques. However, providing clear descriptions and justifications is important for enhancing the credibility of single-case research, and allowing others to evaluate the appropriateness of the quantification technique used. The aim of this systematic literature review is to provide an overview of the quantification techniques that are used to analyze single-case designs, with a focus on the descriptions and justifications that are provided. A total of 290 quantifications occurred across 218 articles, and the descriptions and justifications that were provided for the quantification techniques that were used are systematically examined. Results show that certain quantification techniques, such as the non-overlap indices, are more commonly used. Descriptions and justifications provided for using the quantification techniques are sometimes vague or subjective. Single-case researchers are encouraged to complement visual analysis with the use of quantification techniques for which they can provide objective and appropriate descriptions and justifications, and are encouraged to use tools to guide their choice of quantification techniques. [ABSTRACT FROM AUTHOR]

Single-case experimental designs (SCEDs) are a class of experimental designs suited for answering research questions at an individual level. The main designs available in SCED research are phase designs, multiple baseline designs, alternation designs, and changing criterion designs. Embedded designs, also referred to as combination or hybrid designs, consist of one of these basic designs forms embedded in another design (e.g., a changing criterion design embedded in a multiple baseline design). Systematic reviews of SCEDs have repeatedly indicated that embedded designs are frequently used in applied SCED research. In spite of their popularity, specific recommendations on the conduct and analysis of embedded SCED designs are lacking to date. The purpose of the present article is therefore to provide guidance to applied researchers wishing to conduct embedded SCED designs in terms of design options, design requirements, randomization, and data analysis. [ABSTRACT FROM AUTHOR]

This text focuses on the quantitative integration of studies using alternating treatments designs (ATDs) and changing criterion designs (CCDs) with other types of single-case experimental designs (SCEDs), such as multiple-baseline and withdrawal, which have received more attention in terms of statistical developments. First, a review of how published meta-analyses have dealt with ATDs and CCDs suggests a variety of analytical strategies and insufficient transparency in reporting the exact comparisons performed. Second, we review data-analytical techniques for ATDs and CCDs, looking for alternatives that can be useful for meta-analysis. Third, without losing sight of the underlying logic of the ATDs and CCDs, we propose using multilevel models, comparing data paths in ATDs and the baseline to the last intervention subphase in CCDs. Furthermore, we suggest additional evaluations of the data pattern, beyond the quantification of the magnitude of effect. We also advocate for transparent reporting of how exactly the conditions are being compared for the different SCEDs. This is possible by specifying the design matrices for the multilevel models used. Fourth, in the context of one of the meta-analyses reviewed, we comment on a series of analytical decisions that need to be made before carrying out a meta-analysis using multilevel models. [ABSTRACT FROM AUTHOR]

Immediacy is one of six data aspects (alongside level, trend, variability, overlap, and consistency) that has to be accounted for when visually analyzing single‐case data. Given that it is one of the aspects that has received considerably less attention than other data aspects, the current text offers a review of the proposed conceptual definitions of immediacy (i.e., what it refers to) and also of the suggested operational definitions (i.e., how exactly is it assessed and/or quantified). Provided that a variety of conceptual and operational definitions is identified, we propose following a sensitivity analysis using a randomization test for assessing immediate effects in single‐case experimental designs, by identifying when changes were most clear. In such a sensitivity analysis, the immediate effects are tested for multiple possible intervention points and for different possible operational definitions. Robust immediate effects can be detected if the results for the different operational definitions converge. [ABSTRACT FROM AUTHOR]

The current text deals with the assessment of consistency of data features from experimentally similar phases and consistency of effects in single-case experimental designs. Although consistency is frequently mentioned as a critical feature, few quantifications have been proposed so far: namely, under the acronyms CONDAP (consistency of data patterns in similar phases) and CONEFF (consistency of effects). Whereas CONDAP allows assessing the consistency of data patterns, the proposals made here focus on the consistency of data features such as level, trend, and variability, as represented by summary measures (mean, ordinary least squares slope, and standard deviation, respectively). The assessment of consistency of effect is also made in terms of these three data features, while also including the study of the consistency of an immediate effect (if expected). The summary measures are represented as points on a modified Brinley plot and their similarity is assessed via quantifications of distance. Both absolute and relative measures of consistency are proposed: the former expressed in the same measurement units as the outcome variable and the latter as a percentage. Illustrations with real data sets (multiple baseline, ABAB, and alternating treatments designs) show the wide applicability of the proposals. We developed a user-friendly website to offer both the graphical representations and the quantifications. [ABSTRACT FROM AUTHOR]

Consistency is one of the crucial single-case data aspects that are expected to be assessed visually, when evaluating the presence of an intervention effect. Complementarily to visual inspection, there have been recent proposals for quantifying the consistency of data patterns in similar phases and the consistency of effects for reversal, multiple-baseline, and changing criterion designs. The current text continues this line of research by focusing on alternation designs using block randomization. Specifically, three types of consistency are discussed: consistency of superiority of one condition over another, consistency of the average level across blocks, and consistency in the magnitude of the effect across blocks. The focus is put especially on the latter type of consistency, which is quantified on the basis of partitioning the variance, as attributed to the intervention, to the blocking factor or remaining as residual (including the interaction between the intervention and the blocks). Several illustrations with real and fictitious data are provided in order to make clear the meaning of the quantification proposed. Moreover, specific graphical representations are recommend for complementing the numerical assessment of consistency. A freely available user-friendly webpage is developed for implementing the proposal. [ABSTRACT FROM AUTHOR]

Quality standards for single-case experimental designs (SCEDs) recommend inspecting six data aspects: level, trend, variability, overlap, immediacy, and consistency of data patterns. The data aspect consistency has long been neglected by visual and statistical analysts of SCEDs despite its importance for inferring a causal relationship. However, recently a first quantification has been proposed in the context of A-B-A-B designs, called CONsistency of DAta Patterns (CONDAP). In the current paper, we extend the existing CONDAP measure for assessing consistency in designs with more than two successive A-B elements (e.g., A-B-A-B-A-B), multiple baseline designs, and changing criterion designs. We illustrate each quantification with published research. [ABSTRACT FROM AUTHOR]

In the context of single-case experimental designs, replication is crucial. On the one hand, the replication of the basic effect within a study is necessary for demonstrating experimental control. On the other hand, replication across studies is required for establishing the generality of the intervention effect. Moreover, the "replicability crisis" presents a more general context further emphasizing the need for assessing consistency in replications. In the current text, we focus on replication of effects within a study, and we specifically discuss the consistency of effects. Our proposal for assessing the consistency of effects refers to one of the promising data analytical techniques, multilevel models, also known as hierarchical linear models or mixed effects models. One option is to check, for each case in a multiple-baseline design, whether the confidence interval for the individual treatment effect excludes zero. This is relevant for assessing whether the effect is replicated as being non-null. However, we consider that it is more relevant and informative to assess, for each case, whether the confidence interval for the random effects includes zero (i.e., whether the fixed effect estimate is a plausible value for each individual effect). This is relevant for assessing whether the effect is consistent in size, with the additional requirement that the fixed effect itself is different from zero. The proposal for assessing consistency is illustrated with real data and is implemented in free user-friendly software. [ABSTRACT FROM AUTHOR]

Changing criterion designs (CCD) are single-case experimental designs that entail a step-by-step approximation of the final level desired for a target behavior. Following a recent review on the desirable methodological features of CCDs, the current text focuses on an analytical challenge: the definition of an objective rule for assessing the correspondence between criterion levels and behavior. We review extensively applied research and methodological recommendations for establishing experimental control, such as the range-bound CCD, and integrate them into a proposal for a formal expression of an objective rule. We illustrate how this proposal can lead to establishing the acceptable level of behavior prior to gathering the intervention phase data and how it leaves space for professional judgment. A discussion of software implementing the suggested proposal is included. [ABSTRACT FROM AUTHOR]

Multilevel modeling is an approach that can be used to summarize single-case experimental design (SCED) data. Multilevel models were developed to analyze hierarchical structured data with units at a lower level nested within higher level units. SCEDs use time series data collected from multiple cases (or subjects) within a study that allow researchers to investigate intervention effectiveness at the individual level and also to investigate how these individual intervention effects change over time. There is an increased interest in the field regarding how SCEDs can be used to establish an evidence base for interventions by synthesizing data from a series of intervention studies. Although using multilevel models to meta-analyze SCED studies is promising, application is often hampered by being potentially excessively technical. First, this article provides an accessible description and overview of the potential of multilevel meta-analysis to combine SCED data. Second, a summary of the methodological evidence on the performance of multilevel models for meta-analysis is provided, which is useful given that such evidence is currently scattered over multiple technical articles in the literature. Third, the actual steps to perform a multilevel meta-analysis are outlined in a brief practical guide. Fourth, a suggestion for integrating the quantitative results with a visual representation is provided. [ABSTRACT FROM AUTHOR]

Alternating treatments designs (ATDs) are single-case experimental designs entailing the rapid alternation of conditions, and the specific sequence of conditions is usually determined at random. The visual analysis of ATD data entails comparing the data paths formed by connecting the measurements from the same condition. Apart from visual analyses, there are at least two quantitative analytical options also comparing data paths. On option is a visual structured criterion (VSC) regarding the number of comparisons for which one conditions has to be superior to the other to consider that the difference is not only due to random fluctuations. Another option, denoted as ALIV (a comparison involving Actual and Linearly Interpolated Values), computes the mean difference between the data paths and uses a randomization test to obtain a p value. In the current study, these two options are compared, along with a binomial test, in the context of simulated data, representing ATDs with a maximum of two consecutive administrations of the same condition and a randomized block design. Both VSC and ALIV control Type I error rates, although these are closer to the nominal 5% for ALIV. In contrast, the binomial test is excessively liberal. In terms of statistical power, ALIV plus a randomization test is superior to VSC. We recommend that applied researchers complement visual analysis with the quantification of the mean difference, as per ALIV, and with a p value whenever the alternation sequence was determined at random. We have extended an already existing website providing the graphical representation and the numerical results. [ABSTRACT FROM AUTHOR]

The current paper is a call for and illustration of a way of closing the gap between basic research and professional practice in the field of neurorehabilitation. Methodologically, single-case experimental designs and the guidelines created regarding their conduct are highlighted. Statistically, we review two data analytical options, namely (a) indices quantifying the difference between pairs of conditions in the same metric as the target behavior and (b) a formal statistical procedure offering a standardized overall quantification. The paper provides guidance in the analysis and suggests free software in order to illustrate, in the context of data from behavioral interventions with children with developmental disorders, that informative analyses are feasible. We also show how the results of individual studies can be made eligible for meta-analyses, which are useful for establishing the evidence basis of interventions. Nevertheless, we also point at decisions that need to be made during the process of data analysis. [ABSTRACT FROM AUTHOR]

The present article aims to present a series of software developments in the quantitative analysis of data obtained via single-case experimental designs (SCEDs), as well as the tutorial describing these developments. The tutorial focuses on software implementations based on freely available platforms such as R and aims to bring statistical advances closer to applied researchers and help them become autonomous agents in the data analysis stage of a study. The range of analyses dealt with in the tutorial is illustrated on a typical single-case dataset, relying heavily on graphical data representations. We illustrate how visual and quantitative analyses can be used jointly, giving complementary information and helping the researcher decide whether there is an intervention effect, how large it is, and whether it is practically significant. To help applied researchers in the use of the analyses, we have organized the data in the different ways required by the different analytical procedures and made these data available online. We also provide Internet links to all free software available, as well as all the main references to the analytical techniques. Finally, we suggest that appropriate and informative data analysis is likely to be a step forward in documenting and communicating results and also for increasing the scientific credibility of SCEDs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We developed a reporting guideline to provide authors with guidance about what should be reported when writing a paper for publication in a scientific journal using a particular type of research design: the single-case experimental design. This report describes the methods used to develop the Single-Case Reporting guideline In BEhavioural interventions (SCRIBE) 2016. As a result of 2 online surveys and a 2-day meeting of experts, the SCRIBE 2016 checklist was developed, which is a set of 26 items that authors need to address when writing about single-case research. This 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. We recommend that the SCRIBE 2016 is used by authors preparing manuscripts describing single-case research for publication, as well as journal reviewers and editors who are evaluating such manuscripts. SCIENTIFIC ABSTRACT 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 behavioural 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 behavioural 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. [ABSTRACT FROM AUTHOR]

In this paper we reflect on the numerous calls for the development of benchmarks for interpreting effect size indices, reviewing several possibilities. Such benchmarks are aimed to provide criteria so that analysts can judge whether the size of the effect observed is rather "small", "medium" or "large". The context of this discussion is single-case experimental designs, for which a great variety of procedures have been proposed, with their different nature (e.g., being based on amount of overlap vs. a standardized mean difference) posing challenges to interpretation. For each of the alternatives discussed we point at their strengths and limitations. We also comment how such empirical benchmarks can be obtained, usually by methodologists, and illustrate how these benchmarks can be used by applied researchers willing to have more evidence on the magnitude of effect observed and not only whether an effect is present or not. One of the alternatives discussed is a proposal we make in the current paper. Although it has certain limitations, as all alternatives do, we consider that it is worth discussing it and the whole set of alternatives in order to advance in interpreting effect sizes, now that computing and reporting their numerical values is (or is expected to be) common practice. [ABSTRACT FROM AUTHOR]

BACKGROUND AND AIMS: Balint's Syndrome is a rare condition, often associated with hypoxic brain damage. The major characteristic is an inability to localise objects in space, another is simultanagnosia frequently resulting in reading difficulties.We present RN, a 37 year old woman whose major problem with reading was her inability to recognise individual letters correctly in either lower or upper case. We noted, however, that she was better if the letters were shown in red type. The aims were to determine if RN could relearn letters of the alphabet, investigate whether colour affected her ability to learn, and to explore more specifically whether the red type also helped her to read words. METHOD: Using a single case experimental ABA design, we first determined that the optimal font for RN was size 16. In the baseline (A) phase, we assessed her ability to read all lower and upper case letters of the alphabet in black ink. In the intervention (B) phase we used font size 16 in red ink and an errorless learning approach to teaching the letters. Sessions ran 5 times per week (20 minutes per session). The intervention was then applied to picture recognition and word reading with four sets of 10 words and corresponding pictures. RESULTS: A consistent difference was noted between initial baseline and intervention. Improvement carried over when we returned to baseline. CONCLUSION: Using red type and an errorless learning approach enabled RN to re-learn letters of the alphabet and read words she was previously unable to read. This did not however generalise to her everyday life. [ABSTRACT FROM AUTHOR]

We developed a reporting guideline to provide authors with guidance about what should be reported when writing a paper for publication in a scientific journal using a particular type of research design: the single-case experimental design. This report describes the methods used to develop the Single-Case Reporting guideline In BEhavioural interventions (SCRIBE) 2016. As a result of 2 online surveys and a 2-day meeting of experts, the SCRIBE 2016 checklist was developed, which is a set of 26 items that authors need to address when writing about single-case research. This 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. We recommend that the SCRIBE 2016 is used by authors preparing manuscripts describing single-case research for publication, as well as journal reviewers and editors who are evaluating such manuscripts. 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 forN-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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

In the context of the evidence-based practices movement, the emphasis on computing effect sizes and combining them via meta-analysis does not preclude the demonstration of functional relations. For the latter aim, we propose to augment the visual analysis to add consistency to the decisions made on the existence of a functional relation without losing sight of the need for a methodological evaluation of what stimuli and reinforcement or punishment are used to control the behavior. Four options for quantification are reviewed, illustrated, and tested with simulated data. These quantifications include comparing the projected baseline with the actual treatment measurements, on the basis of either parametric or nonparametric statistics. The simulated data used to test the quantifications include nine data patterns in terms of the presence and type of effect and comprise ABAB and multiple-baseline designs. Although none of the techniques is completely flawless in terms of detecting a functional relation only when it is present but not when it is absent, an option based on projecting split-middle trend and considering data variability as in exploratory data analysis proves to be the best performer for most data patterns. We suggest that the information on whether a functional relation has been demonstrated should be included in meta-analyses. It is also possible to use as a weight the inverse of the data variability measure used in the quantification for assessing the functional relation. We offer an easy to use code for open-source software for implementing some of the quantifications. [ABSTRACT FROM PUBLISHER]

In this editorial discussion we reflect on the issues addressed by, and arising from, the papers in this special issue on Single-Case Experimental Design (SCED) study methodology. We identify areas of consensus and disagreement regarding the conduct and analysis of SCED studies. Despite the long history of application of SCEDs in studies of interventions in clinical and educational settings, the field is still developing. There is an emerging consensus on methodological quality criteria for many aspects of SCEDs, but disagreement on what are the most appropriate methods of SCED data analysis. Our aim is to stimulate this ongoing debate and highlight issues requiring further attention from applied researchers and methodologists. In addition we offer tentative criteria to support decision-making in relation to the selection of analytical techniques in SCED studies. Finally, we stress that large-scale interdisciplinary collaborations, such as the current Special Issue, are necessary if SCEDs are going to play a significant role in the development of the evidence base for clinical practice. [ABSTRACT FROM AUTHOR]

This paper introduces the Special Issue ofNeuropsychological Rehabilitationon Single Case Experimental Design (SCED) methodology. SCED studies have a long history of use in evaluating behavioural and psychological interventions, but in recent years there has been a resurgence of interest in SCED methodology, driven in part by the development of standards for conducting and reporting SCED studies. Although there is consensus on some aspects of SCED methodology, the question of how SCED data should be analysed remains unresolved. This Special Issues includes two papers discussing aspects of conducting SCED studies, five papers illustrating use of SCED methodology in clinical practice, and nine papers that present different methods of SCED data analysis. A final Discussion paper summarises points of agreement, highlights areas where further clarity is needed, and ends with a set of resources that will assist researchers conduct and analyse SCED studies. [ABSTRACT FROM AUTHOR]

Abstract: If single-case experimental designs are to be used to establish guidelines for evidence-based interventions in clinical and educational settings, numerical values that reflect treatment effect sizes are required. The present study compares four recently developed procedures for quantifying the magnitude of intervention effect using data with known characteristics. Monte Carlo methods were used to generate AB designs data with potential confounding variables (serial dependence, linear and curvilinear trend, and heteroscedasticity between phases) and two types of treatment effect (level and slope change). The results suggest that data features are important for choosing the appropriate procedure and, thus, inspecting the graphed data visually is a necessary initial stage. In the presence of serial dependence or a change in data variability, the nonoverlap of all pairs (NAP) and the slope and level change (SLC) were the only techniques of the four examined that performed adequately. Introducing a data correction step in NAP renders it unaffected by linear trend, as is also the case for the percentage of nonoverlapping corrected data and SLC. The performance of these techniques indicates that professionals'' judgments concerning treatment effectiveness can be readily complemented by both visual and statistical analyses. A flowchart to guide selection of techniques according to the data characteristics identified by visual inspection is provided. [Copyright &y& Elsevier]