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Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to visualise and conceptualise the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting the representation to pairwise relationships can neglect potentially critical information shared by groups of three or more variables (higher-order statistical interdependencies). To overcome this important limitation, here we propose an information-theoretic framework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account on the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for future investigation. [ABSTRACT FROM AUTHOR]

We show that separable nonlinear least squares (SNLLS) estimation is applicable to all linear structural equation models (SEMs) that can be specified in RAM notation. SNLLS is an estimation technique that has successfully been applied to a wide range of models, for example neural networks and dynamic systems, often leading to improvements in convergence and computation time. It is applicable to models of a special form, where a subset of parameters enters the objective linearly. Recently, Kreiberg et al. (Struct Equ Model Multidiscip J 28(5):725–739, 2021. https://doi.org/10.1080/10705511.2020.1835484) have shown that this is also the case for factor analysis models. We generalize this result to all linear SEMs. To that end, we show that undirected effects (variances and covariances) and mean parameters enter the objective linearly, and therefore, in the least squares estimation of structural equation models, only the directed effects have to be obtained iteratively. For model classes without unknown directed effects, SNLLS can be used to analytically compute least squares estimates. To provide deeper insight into the nature of this result, we employ trek rules that link graphical representations of structural equation models to their covariance parametrization. We further give an efficient expression for the gradient, which is crucial to make a fast implementation possible. Results from our simulation indicate that SNLLS leads to improved convergence rates and a reduced number of iterations. [ABSTRACT FROM AUTHOR]

Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to marginalization of the random effects. Derivative computations of a fitted GLMM's likelihood are also difficult, especially because the derivatives are not by-products of popular estimation algorithms. In this paper, we first describe theoretical results related to GLMM derivatives along with a quadrature method to efficiently compute the derivatives, focusing on fitted lme4 models with a single clustering variable. We describe how psychometric results related to item response models are helpful for obtaining the derivatives, as well as for verifying the derivatives' accuracies. We then provide a tutorial on the many possible uses of these derivatives, including robust standard errors, score tests of fixed effect parameters, and likelihood ratio tests of non-nested models. The derivative computation methods and applications described in the paper are all available in easily obtained R packages. [ABSTRACT FROM AUTHOR]

We propose a two-step procedure to estimate structural equation models (SEMs). In a first step, the latent variable is replaced by its conditional expectation given the observed data. This conditional expectation is estimated using a James–Stein type shrinkage estimator. The second step consists of regressing the dependent variables on this shrinkage estimator. In addition to linear SEMs, we also derive shrinkage estimators to estimate polynomials. We empirically demonstrate the feasibility of the proposed method via simulation and contrast the proposed estimator with ML and MIIV estimators under a limited number of simulation scenarios. We illustrate the method on a case study. [ABSTRACT FROM AUTHOR]

In the psychology and educational science programs at Ghent University, only 36.1 % of the new incoming students in 2011 and 2012 passed all exams. Despite availability of information, many students underestimate the scientific character of social science programs. Statistics courses are a major obstacle in this matter. Not all enrolling students master the basic mathematical skills needed to pass statistics courses. Therefore, we propose a test that measures these skills. Our aim is to examine the predictive validity of the test with regard to the statistics course and also as to overall academic success. The results indicate that a test of very basic mathematics skills helps identify at-risk students at and before the start of the academic year. The practical implications of these results are discussed. The test aids the efficient use of means for remedial interventions and supports future students in choosing a higher education program that suits their potential. [ABSTRACT FROM AUTHOR]

Although spatial smoothing of fMRI data can serve multiple purposes, increasing the sensitivity of activation detection is probably its greatest benefit. However, this increased detection power comes with a loss of specificity when non-adaptive smoothing (i.e. the standard in most software packages) is used. Simulation studies and analysis of experimental data was performed using the packages neuRosim and fmri. In these studies, we systematically investigated the effect of spatial smoothing on the power and number of false positives in two particular cases that are often encountered in fMRI research: (1) Single condition activation detection for regions that differ in size, and (2) multiple condition activation detection for neighbouring regions. Our results demonstrate that adaptive smoothing is superior in both cases because less false positives are introduced by the spatial smoothing process compared to standard Gaussian smoothing or FDR inference of unsmoothed data. [ABSTRACT FROM AUTHOR]

This study aims at providing qualitative and quantitative evidence on the relevance of two broadly used mental health self-report measures—Impact of Event Scale Revised (IES-R) and Hopkins Symptom Checklist 37 for Adolescents (HSCL-37A)—for use in Eastern Democratic of Congo, as no psychological assessment instruments were available for this region. We therefore describe an apt procedure to adapt and translate standard screening instruments in close collaboration with the local community, feasible under challenging conditions in emergency settings. Focus groups and interviews with community key figures in psychosocial care were employed to ensure local validity of the adaptation and translation process. Consequently, the questionnaires’ internal consistency (Cronbach’s alpha) and construct validity (principal component analysis, testing of theoretical assumptions) were assessed based on a clustered school-based community survey among 1,046 adolescents (13–21 years) involving 13 secondary schools in the Ituri district in Eastern Democratic Republic of Congo. Key-informant qualitative data confirmed face and construct validity of all IES-R and all HSCL-37A anxiety items. Additional culture-specific symptoms of adolescent mental ill-health were added to enhance local relevance of the HSCL-37A depression and externalizing subscales. Quantitative analysis of the survey data revealed adequate internal consistency and construct validity of both adapted measures, yet weaker results for the externalizing scale. Furthermore, it confirmed the internalizing/externalizing factor structure of the HSCL-37A and the theoretically deviating intrusion/arousal versus active avoidance factor structure for the IES-R. Community-based adaptation can extend the validity and local relevance of mental health screening in emergency and low-income settings. The availability of adequate Swahili and Congolese French adaptations of the IES-R and HSCL-37A could stimulate the assessment of psychosocial needs in war-exposed Eastern Congolese adolescents. [ABSTRACT FROM AUTHOR]

The present article reports on two studies that investigated the utility of Young’s cognitive theory Young et al. (Schema therapy: A practitioner’s guide, Guilford Publications, New York, ) in adolescents. Study 1 focused on the factorial validity of the Young Schema Questionnaire (YSQ) in youth. In Study 2, the dimensionality of Young’s schemas and their (content-specific) association with psychopathology were investigated. In Study 1, 635 adolescents were asked to complete the YSQ. In Study 2, participants were 112 non-referred and 104 referred adolescents. They were interviewed with the Structured Clinical Interview for DSM-IV–Child edition and completed the YSQ and the Youth Self Report. Their parents were asked to fill out the Child Behavior Checklist. It was demonstrated that Young’s theoretically proposed taxonomy of schemas and domains can be retrieved in adolescents. Referred youth displayed a higher severity of maladaptive schemas as compared with non-referred adolescents. A content-specific association of schemas and psychopathology was established. Young’s schema theory might constitute a valuable framework to understand psychopathology in youth. [ABSTRACT FROM AUTHOR]

The present study investigates five types of continuity of personality and internalizing and externalizing problems (i.e., structural, differential, mean-level, individual-level and ipsative continuity) in a sample of referred children and adolescents ( N = 114) with a broad variety of psychological problems. Mothers were administered a child personality and psychopathology measure, i.e., the Hierarchical Personality Inventory for Children [Handleiding hiërarchische persoonlijkheidsvragenlijst voor kinderen (manual hierarchical personality inventory for children). Ghent University, Department of Developmental, Personality, and Social Psychology, Ghent, 2005] and the Child Behavior Checklist [Handleiding voor de cbcl/4–18: (Manual of the CBCL/4–18), Afdeling Kinder- en Jeugdpsychiatrie. Erasmus Universiteit Rotterdam, The Netherlands, 1996] at two measurement occasions, with a 26-months interval. Personality was substantially stable, paralleling findings for non-referred peers. Internalizing and Externalizing Problem Behavior were almost as stable as personality traits, suggesting that childhood psychopathology is more persistent than generally assumed. Strengths and limitations of the present study and implications for further research are discussed. [ABSTRACT FROM AUTHOR]

In a standard supervised classification paradigm, stimuli are presented sequentially, participants make a classification, and feedback follows immediately. In this article, we use a semisupervised classification paradigm, in which feedback is given after a prespecified percentage of trials only. In Experiment 1, feedback was given in 100%, 0%, 25%, and 50% of the trials. Previous research reported by Ashby, Queller, and Berretty (1999) indicated that in an information-integration task, perfect accuracy was obtained supervised (100%) but not unsupervised (0%). Our results show that in both the 100% and 50% conditions, participants were able to achieve maximum accuracy. However, in the 0% and the 25% conditions, participants failed to learn. To discover the influence of the no-feedback trials on the learning process, the 50% condition was replicated in Experiment 2, substituting unrelated filler trials for the no-feedback trials. The results indicated that accuracy rates were similar, suggesting no impact of the no-feedback trials on the learning process. The possibility of ever learning in a 25% setting was also researched in Experiment 2. Using twice as many trials, the results showed that all but 2 participants succeeded, suggesting that only the total number of feedback trials is important. The impact of the semisupervised learning results for ALCOVE, COVIS, and SPEED models is discussed. [ABSTRACT FROM AUTHOR]

Student wellbeing can be considered a major output indicator of quality of education. A positive classroom climate can contribute to a higher sense of wellbeing. Interpersonal relationships between teachers and students are an important aspect of the classroom climate. This study investigated how student wellbeing was predicted by student characteristics, interpersonal teacher behaviour and achievement. 594 students from 55 classes in 13 technical and vocational secondary schools were involved in this study. The results indicated that those students who attended school because they were highly motivated learners reported a higher sense of wellbeing than those who attended out of a sense of duty. It also appeared that student perception of interpersonal teacher behaviour predicted student wellbeing. As a case in point, when students viewed their language teacher as tolerant yet exacting discipline, a positive relationship was found with student wellbeing. Students also felt better when their mathematics teachers were less authoritarian, but the cooperative component was still important. [ABSTRACT FROM AUTHOR]

Student motivation as well as student perception of interpersonal teacher behaviour are linked to the sense of wellbeing at student level. However, while most of the variance in the measurement of student wellbeing was situated at student level, eleven percent of variance was found at classroom level. In this article we focus on this variance at classroom level and the moderating role that student perception of interpersonal teacher behaviour has. From an interpersonal perspective on teaching, the relationship between teacher wellbeing, perceptions of interpersonal teacher behaviour, and student wellbeing is examined. Grade 9 students of technical and vocational training schools are participating in this study. In the analyses a distinction is made between teaching academic subjects and teaching vocational subjects. There appears to be a direct link between the wellbeing of the teachers of academic subjects and the wellbeing of their students. Students who perceive their academic teacher as leading, helpful and friendly score higher on wellbeing, while wellbeing decreases when an academic teacher is perceived as strict and admonishing.The relationship between the teacher of vocational subjects who typifies himself as strict and admonishing, and the wellbeing of his students, is moderated by student perceptions of teacher interpersonal behaviour. A direct relationship between the wellbeing of the vocational teacher and the wellbeing of students is not found. Only when the vocational teacher's wellbeing is high and student perceptions of uncertain or dissatisfied interpersonal teacher behaviour is low, does student wellbeing increase. We conclude that for vocational subjects, student perceptions of interpersonal teacher behaviour are crucial moderators. Finally, students who are highly motivated to learn practical subjects, have a higher score on student wellbeing. By contrast, the fact that education is inherently obligatory has a negative influence on student wellbeing. [ABSTRACT FROM AUTHOR]

The present study examined the influence of features differing across problems (problem size and operation) and across individuals (gender, amount of daily arithmetic practice, calculator use, and arithmetic skill) on simple arithmetic performance. Regression analyses were used to investigate the role of these variables in both strategy selection and strategy efficiency. Results show that more skilled and highly practiced students used memory retrieval more often and executed their strategies more efficiently than did less skilled and practiced students. Furthermore, calculator use correlated with both retrieval and procedural strategy efficiency but not with strategy selection. Only very small associations with gender were observed, with boys retrieving slightly faster than girls. Implications of the present findings for models of mental arithmetic are discussed. [ABSTRACT FROM AUTHOR]

There is a lack of research illustrating the extent of psychiatric problems in European TCs. Furthermore, there is a need to obtain more insight into gender differences concerning comorbidity in the TC population. In an attempt to respond to previous shortcomings, three specific goals were formulated for the current study. The primary aim was to explore the lifetime prevalence of affective disorders in European TC clients. The second aim was to focus on related gender differences. And finally, client characteristics measured by the EuropASI were related to mood and anxiety disorders measured by the SCID-I. Nine out of ten substance abusers treated in a European TC program have an affective disorder. Gender differences are noted for two anxiety disorders: men have more obsessive-compulsive disorders; women have more post-traumatic stress disorders. The study illustrates that not only gender but also other client characteristics should be taken into account when organizing treatment. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published https://doi.org/10.1007/s11336-021-09749-2 [ABSTRACT FROM AUTHOR]