We are pleased to bring you this special issue of Brain Impairment on quantitative data analysis, an area of increasing complexity and sophistication. In planning the special issue, our intention was to bring together a set of articles covering diverse and topical areas in the field, with the idea of having the volume serve as a ‘go-to’ resource. The special issue is aimed at researchers, clinicians engaged in research, and advanced students all of whom may have passing familiarity with a particular data analytic technique, but wish to know more about it and how to apply it. Accordingly, our aim is to equip the reader with concrete, hands-on information that can be applied in the day-to-day world of research. The authors of the articles comprising the special issue, each of whom is an expert in his/her field, were charged with the task of writing a practical guide and providing worked examples to illustrate the application of their selected technique/s. The papers in the special issue cover three domains: the single-case method, between-groups design, and psychometric aspects of instrument development.

Single case designs (SCDs) allow researchers to objectively evaluate the impact of an intervention by repeatedly measuring a dependent variable across baseline and intervention conditions. Rooted in baseline logic, SCDs evaluate change over time, with each participant serving as his or her own control during the course of a study. Formative and summative evaluation of data is critical to determining causal relations. Visual analysis involves evaluation of level, trend, variability, consistency, overlap, and immediacy of effects within (baseline and intervention) and between conditions (baseline to intervention). The purpose of this paper is to highlight the process for visually analysing data collected in the context of a SCD and to provide structures and procedures for evaluating the six data characteristics of interest. A checklist with dichotomous responses (i.e., yes/no) is presented to facilitate implementation and reporting of systematic visual analysis.

Single-case experimental designs meeting evidence standards are useful for identifying empirically-supported practices. Part of the research process entails data analysis, which can be performed both visually and numerically. In the current text, we discuss several statistical techniques focusing on the descriptive quantifications that they provide on aspects such as overlap, difference in level and in slope. In both cases, the numerical results are interpreted in light of the characteristics of the data as identified via visual inspection. Two previously published data sets from patients with traumatic brain injury are re-analysed, illustrating several analytical options and the data patterns for which each of these analytical techniques is especially useful, considering their assumptions and limitations. In order to make the current review maximally informative for applied researchers, we point to free user-friendly web applications of the analytical techniques. Moreover, we offer up-to-date references to the potentially useful analytical techniques not illustrated in the article. Finally, we point to some analytical challenges and offer tentative recommendations about how to deal with them.

This paper presents a unilevel and multilevel approach for the analysis and meta-analysis of single-case experiments (SCEs). We propose a definition of SCEs and derive the specific features of SCEs’ data that have to be taken into account when analysing and meta-analysing SCEs. We discuss multilevel models of increasing complexity and propose alternative and complementary techniques based on probability combining and randomisation test wrapping. The proposed techniques are demonstrated with real-life data and corresponding R code.

This paper presents ‘Tips’ for researchers of brain impairment who are interested in conducting randomised controlled trials. The paper is intended for researchers who are planning to undertake their first trial, but may also be of interest to more experienced trialists or clinicians who want to further their understandings of clinical trials. The Tips include suggestions for how to design, conduct, analyse and report clinical trials.

There has been controversy over Null Hypothesis Significance Testing (NHST) since the first quarter of the 20th century and misconceptions about it still abound. The first section of this paper briefly discusses some of the problems and limitations of NHST. Overwhelmingly, the ‘holy grail’ of researchers has been to obtain significant p-values. In 1999 the American Psychological Association (APA) recommended that if NHST was used in data analysis, then researchers should report effect sizes (ESs) and their confident intervals (CIs) as well as p-values. The APA recommendations are summarised in the next section of the paper. But as neuropsychological rehabilitation clinicians, the primary interest is (or should be) to determine whether or not the effect of an intervention is clinically important, not just statistically significant. In this context, ESs and their CIs provide information relevant to clinicians. The next section of the paper reviews common ESs and worked out examples are provided for the calculation of three commonly used ES (Cohen's d, Hedge's g and Glass’ delta). Web-based resources for calculating other ESs and their CIs are also reviewed.

Systematic reviews have developed over the past 40 years as a method for integrating findings from the available studies relating to clinical problems and interventions into one publication. Systematic reviews employ a variety of data analytic techniques including meta-analysis, which combines treatment effects across disparate studies in order to produce a truer estimate of treatment effect. The Cochrane Collaboration was established in order to facilitate access to high-quality evidence and specifies stringent guidelines for the production of systematic reviews. A Cochrane Systematic Review (CSR) includes consideration of the risk-of-bias of the selected studies in reaching conclusions. A recent CSR is used as an example to demonstrate the process of conducting a CSR, the data analytic methods employed and the assumptions made when employing these methods. There is a discussion of issues the reader will need to be aware of when considering the findings of a CSR and how this might differ from other systematic reviews including some consideration of how CSRs apply to the brain impairment literature.

Item response theory has its origins in educational measurement and is now commonly applied in health-related measurement of latent traits, such as function and symptoms. This application is due, in large part, to gains in the precision of measurement attributable to item response theory and corresponding decreases in response burden, study costs, and study duration. The purpose of this paper is twofold: introduce basic concepts of item response theory and demonstrate this analytic approach in a worked example, a Rasch model (1PL) analysis of the Eating Assessment Tool (EAT-10), a commonly used measure for oropharyngeal dysphagia. The results of the analysis were largely concordant with previous studies of the EAT-10 and illustrate for brain impairment clinicians and researchers how IRT analysis can yield greater precision of measurement.

The past 20 years have seen the development of instruments designed to specify standards and evaluate the adequacy of published studies with respect to the quality of study design, the quality of findings, as well as the quality of their reporting. In the field of psychometrics, the first minimum set of standards for the review of psychometric instruments was published in 1996 by the Scientific Advisory Committee of the Medical Outcomes Trust. Since then, a number of tools have been developed with similar aims. The present paper reviews basic psychometric properties (reliability, validity and responsiveness), compares six tools developed for the critical appraisal of psychometric studies and provides a worked example of using the COSMIN checklist, Terwee-m statistical quality criteria, and the levels of evidence synthesis using the method of Schellingerhout and colleagues (2012). This paper will aid users and reviewers of questionnaires in the quality appraisal and selection of appropriate instruments by presenting available assessment tools, their characteristics and utility.