Randomization tests are nonparametric statistical tests that obtain their validity by computationally mimicking the random assignment procedure that was used in the design phase of a study. Because randomization tests do not rely on a random sampling assumption, they can provide a better alternative than parametric statistical tests for analyzing data from single-case designs. In this article, an R package is described for use in designing single-case phase (AB, ABA, and ABAB) and alternation (completely randomized, alternating treatments, and randomized block) experiments, as well as for conducting statistical analyses on data gathered by means of such designs. The R code is presented in a step-by-step way, which at the same time clarifies the rationale behind single-case randomization tests.

Vaginismus is commonly described as a persistent difficulty in allowing vaginal entry of a penis or other object. Lifelong vaginismus occurs when a woman has never been able to have intercourse. A replicated single-case A-B-phase design was used to investigate the effectiveness of therapist-aided exposure for lifelong vaginismus. A baseline period (Phase A) was contrasted with exposure + follow-up (Phase B), using random switching between phases. The main outcome measure (intercourse ability) was assessed daily for 24 weeks. Ten women participated. The exposure consisted of a maximum of three 2-hr sessions during 1 week at a university hospital. The participant performed vaginal penetration exercises on herself, in the presence of a female therapist. Two follow-up sessions were scheduled over a 5-week period. Nine of the 10 participants reported having intercourse after treatment, and in 5 of the 9, intercourse was possible within the 1st week of treatment. The results remained at 1-year follow-up. Furthermore, exposure was successful in decreasing fear and negative penetration beliefs posttreatment and at 3-month and 1-year follow-ups. Therapist-aided exposure appears to be an effective treatment for lifelong vaginismus.

Multiple-baseline designs are variants of single-case designs well suited to behavioral research. In this article, we want to bring these designs to the attention of experimental psychologists and social and behavioral researchers in general, discuss such designs' advantages and limitations for valid inference in behavioral research, and suggest a statistical data-analytic technique to complement visual inspection, together with software to conduct those analyses.A multiple-baseline design consists of a series of replicated single-case designs, in which the replications are carried out at the same time. They extend the basic singlecase AB phase design by implementing several of those AB designs simultaneously to different persons, behaviors, or settings (Ferron & Scott, 2005;Onghena & Edgington, 2005). For convenience, these separate persons, behaviors, or settings will henceforth be called units.

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We conducted two experiments to test whether individuals' strategy choices in a numerosity judgement task are affected by the strategy that was used on the previous trials. Both experiments demonstrated that a previously used strategy indeed influences individuals' strategy choices. Individuals were more inclined to reuse the strategy that they had used on the previous trials. However, this study also demonstrated that this influence is limited to those items that do not have a strong association with a specific strategy. Possible underlying mechanisms for the observed effect are discussed.During the last decades, many studies have shown that people use multiple strategies to solve a wide range of cognitive tasks. Although this variability in strategy use has been studied most extensively in the domain of arithmetic (e