Sleep variability and fatigue in adolescents: Associations with school‐related features

This study aims to evaluate the influences of sleep duration and sleep variability (SleepV), upon adolescents' school‐related situations. The Health Behaviour in School‐Aged Children (HBSC) survey is based on a self‐completed questionnaire. The participants were 3164 pupils (53.7% girls), attending the 8th and 10th grades, 14.9 years old, and were inquired about subjective sleep duration during the week and weekends, SleepV, fatigue, difficulties in sleep initiation, school achievement, feelings towards schools, pressure with school work and skipping classes. Multiple regression models used, as dependent variables: (a) school achievement, (b) disliking school, (c) pressure with school work and (d) skipping classes, using as independent variables, each of the remaining school‐related variables, fatigue, total sleep duration and difficulties in sleep initiation. The average sleep duration in the week and during weekdays was lower than recommended for these age groups, and almost half of students had high SleepV between weekdays and weekends. A logistic model revealed that the absence of SleepV was associated with lower perception of school work pressure, less frequent skipping classes, more infrequent fatigue and more infrequent difficulties in sleep initiation. Poor sleep quality, SleepV and insufficient sleep duration affected negatively school‐related variables.     

Hierarchical classes models for three-way three-mode binary data: interrelations and model selection

Several hierarchical classes models can be considered for the modeling of three-way three-mode binary data, including the INDCLAS model (Leenen, Van Mechelen, De Boeck, and Rosenberg, 1999), the Tucker3-HICLAS model (Ceulemans, Van Mechelen, and Leenen, 2003), the Tucker2-HICLAS model (Ceulemans and Van Mechelen, 2004), and the Tucker1-HICLAS model that is introduced in this paper. Two questions then may be raised: (1) how are these models interrelated, and (2) given a specific data set, which of these models should be selected, and in which rank? In the present paper, we deal with these questions by (1) showing that the distinct hierarchical classes models for three-way three-mode binary data can be organized into a partially ordered hierarchy, and (2) by presenting model selection strategies based on extensions of the well-known scree test and on the Akaike information criterion. The latter strategies are evaluated by means of an extensive simulation study and are illustrated with an application to interpersonal emotion data. Finally, the presented hierarchy and model selection strategies are related to corresponding work by Kiers (1991) for principal component models for three-way three-mode real-valued data.     

Probability judgment in hierarchical learning: a conflict between predictiveness and coherence

Why are people's judgments incoherent under probability formats? Research in an associative learning paradigm suggests that after structured learning participants give judgments based on predictiveness rather than normative probability. This is because people's learning mechanisms attune to statistical contingencies in the environment, and they use these learned associations as a basis for subsequent probability judgments. We introduced a hierarchical structure into a simulated medical diagnosis task, setting up a conflict between predictiveness and coherence. Thus, a target symptom was more predictive of a subordinate disease than of its superordinate category, even though the latter included the former. Under a probability format participants tended to violate coherence and make ratings in line with predictiveness; under a frequency format they were more normative. These results are difficult to explain within a unitary model of inference, whether associative or frequency-based. In the light of this, and other findings in the judgment and learning literature, a dual-component model is proposed.     

Testing Variance Components in Linear Mixed Modeling Using Permutation

Abstract

Inference of variance components in linear mixed modeling (LMM) provides evidence of heterogeneity between individuals or clusters. When only nonnegative variances are allowed, there is a boundary (i.e., 0) in the variances’ parameter space, and regular inference statistical procedures for such a parameter could be problematic. The goal of this article is to introduce a practically feasible permutation method to make inferences about variance components while considering the boundary issue in LMM. The permutation tests with different settings (i.e., constrained vs. unconstrained estimation, specific vs. generalized test, different ways of calculating p values, and different ways of permutation) were examined with both normal data and non-normal data. In addition, the permutation tests were compared to likelihood ratio (LR) tests with a mixture of chi-squared distributions as the reference distribution. We found that the unconstrained permutation test with the one-sided p-value approach performed better than the other permutation tests and is a useful alternative when the LR tests are not applicable. An R function is provided to facilitate the implementation of the permutation tests, and a real data example is used to illustrate the application. We hope our results will help researchers choose appropriate tests when testing variance components in LMM.