The Behavior Rating Inventory of Executive Functions evaluates executive functioning through the observation of students' performance in real contexts. Most psychometric studies of the scale have only tested the first-order structure, despite the hierarchical configuration of its theoretical model. A bifactor model was conducted on a normative sample of 5- to 18-year-old Chileans (M age = 11.3 years, SD = 3.7) to test a hierarchical structure of three first-order factors and an independent second-order factor. Bifactor analyses showed best fit for the proposed hierarchical structure. Findings supported a method to evaluate executive functioning models that provides a general global factor score that may complement existing indices and thus help clinicians to make better inferences.

Since the mid-1950s, there has been a clear predominance of the Frequentist approach to hypothesis testing, both in psychology and in social sciences. Despite its popularity in the field of statistics, Bayesian inference is barely known and used in psychology. Frequentist inference, and its null hypothesis significance testing (NHST), has been hegemonic through most of the history of scientific psychology. However, the NHST has not been exempt of criticisms. Therefore, the aim of this chapter is to introduce a Bayesian approach to hypothesis testing that may represent a useful complement, or even an alternative, to the current NHST. The advantages of this Bayesian approach over Frequentist NHST will be presented, providing examples that support its use in psychology and social sciences. Conclusions are outlined.

Despite their theoretical appeal, Bayesian methods for the assessment of poor effort and malingering are still rarely used in neuropsychological research and clinical diagnosis. In this article, we outline a novel and easy-to-use Bayesian latent group analysis of malingering whose goal is to identify participants displaying poor effort when tested. Our Bayesian approach also quantifies the confidence with which each participant is classified and estimates the base rates of malingering from the observed data. We implement our Bayesian approach and compare its utility in effort assessment to that of the classic below-chance criterion of symptom validity testing (SVT). In two experiments, we evaluate the accuracy of both a Bayesian latent group analysis and the below-chance criterion of SVT in recovering the membership of participants assigned to the malingering group. Experiment 1 uses a simulation research design, whereas Experiment 2 involves the differentiation of patients with a history of stroke from coached malingerers. In both experiments, sensitivity levels are high for the Bayesian method, but low for the below-chance criterion of SVT. Additionally, the Bayesian approach proves to be resistant to possible effects of coaching. We conclude that Bayesian latent group methods complement existing methods in making more informed choices about malingering.

The hypnotics tested did not interfere with the nocturnal sleep-induced improvement of memory.