This study aimed to examine whether early change in self-reported quality of life (QoL) was a predictor of outcomes in the treatment of anorexia nervosa (AN). Given suggestions that people with AN overestimate their QoL when unwell, we hypothesised that any early change in self-reported QoL, be it an early improvement or early worsening, would predict better outcomes in terms of end-of-treatment body mass index (BMI), eating disorder (ED) psychopathology, and QoL. Participants were 78 adult outpatients engaged in cognitive behaviour therapy for anorexia nervosa (CBT-AN) either with or without the embedded compulsive exercise module “compuLsive Exercise Activity TheraPy” (LEAP). Polynomial regression was utilised to examine the effects of varying combinations of baseline and 10-week self-reported physical-health-relatedr QoL (SF-12; PHRQoL subscale), mental-health-related QoL (SF-12; MHRQoL subscale), and eating-disorder-specific QoL (EDQoL; global, psychological, cognitive/physical, financial, and school/work subscales) on end-of-treatment BMI, ED psychopathology, and QoL. Greater magnitudes of early change in global EDQoL scores, both positive and negative, predicted better MHRQoL but not BMI or ED psychopathology at the end of treatment. Psychological EDQoL ratings also accounted for 38.1% of the variance in end-of-treatment ED psychopathology, although tests examining the 6ratings may be meaningful in predicting treatment outcomes. The positive impact of early worsening in QoL ratings suggests that early QoL ratings are inflated due to denial and poor insight. Clinicians should be reassured that early QoL decline does not indicate treatment failure.

(1)Background.Early childhood experiences have long-lasting effects on subsequent mental and physical health, education, and employment. Measurement of these effects relies on insensitive behavioral signs, subjective assessments by adult observers, neuroimaging or neurophysiological studies, or retrospective epidemiologic outcomes. Despite intensive search, the underlying mechanisms for these long-term changes in development and health status remain unknown.(2)Methods.We analyzed scalp hair from healthy children and their mothers using an unbiased proteomics platform using tandem mass spectrometry, ultra-performance liquid chromatography, and collision induced dissociation to reveal commonly observed hair proteins with spectral count of 3 or higher.(3)Results.We observed 1368 non-structural hair proteins in children, 1438 non-structural hair proteins in mothers, with 1288 proteins showing individual variability. Mothers showed higher numbers of peptide spectral matches and hair proteins compared to children, with important age-related differences between mothers and children. Age-related differences were also observed in children, with differential protein expression patterns between younger (2 years and below) and older children (3-5 years). We observed greater similarity in hair protein patterns between mothers and their biological children as compared to mothers and unrelated children. The top 5% proteins driving population variability represent biological pathways associated with brain development, immune signaling, and stress response regulation.(4)Conclusion.Non-structural proteins observed in scalp hair include promising biomarkers to investigate the long-term developmental changes and health status associated with early childhood experiences.

Knowledge about brain source localizations for religious states of mind is still limited. Previous studies have usually not set a direct emphasis on experience. The present study investigated the phenomenon of religious experience using inverse solution calculations, and it is one of the first to measure the dimension of experience directly. A total of 60 evangelical Christians participated in an experiment where they were asked to engage in worship and try to connect with God. Using a bar slider, the participants continuously rated how strongly they sensed God’s presence at any given moment. A selection of songs helped to induce the desired experience. Measurements were made using EEG with 64 electrodes and inverse solutions were calculated with sLORETA. We appropriated two mutually compatible hypotheses from the literature pertaining to religious experiences: the executive inhibition hypothesis (reformulated as the frontal relaxation hypothesis) and the temporal involvement hypothesis. Our results did not yield any information about the frontal areas; however, they indicated that the right temporal cortex appeared to be involved during the experience.

The Lp loss function has been used for model-robust estimation of structural equation models based on robustly fitting moments. This article addresses the choice of the tuning parameter ε that appears in the differentiable approximations of the nondifferentiable Lp loss functions. Moreover, model-robust estimation based on the Lp loss function is compared with a recently proposed differentiable approximation of the L0 loss function and a direct minimization of a smoothed version of the Bayesian information criterion in regularized estimation. It turned out in a simulation study that the L0 loss function slightly outperformed the Lp loss function in terms of bias and root mean square error. Furthermore, standard errors of the model-robust SEM estimators were analytically derived and exhibited satisfactory coverage rates.

Currently, two-level Mokken scale analysis for clustered test data is being developed. This paper contributes to this development by providing model-fit procedures for two-level Mokken scale analysis. New theoretical insights suggested that the existing model-fit procedure from traditional (one-level) Mokken scale analyses can be used for investigating model fit at both level 1 (respondent level) and level 2 (cluster level) of two-level Mokken scale analysis. However, the traditional model-fit procedure requires some modifications before it can be used at level 2. In this paper, we made these modifications and investigated the resulting model-fit procedure. For two model assumptions, monotonicity and invariant item ordering, we investigated the false-positive count and the sensitivity count of the level 2 model-fit procedure, with respect to the number of model violations detected, and the number of detected model violations deemed statistically significant. For monotonicity, the detection of model violations was satisfactory, but the significance test lacked power. For invariant item ordering, both aspects were satisfactory.

The present Special Issue of Psych, which has been now fully released, aimed to highlight the importance of the Mobile Mental Health Units (MMHUs) in delivering services in rural and remote areas in Greece, and to stress their role as an easily accessible setting that provides a wide range of community-based psychosocial interventions, well beyond usual psychiatric care [...]

In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated in research. Depending on the degree of violation, this can lead to biased standard errors of higher-level parameters and thus to incorrect inferences. In this article, we describe a resampling technique for obtaining standard errors—Zitzmann’s jackknife. We conducted a Monte Carlo simulation study to compare the technique with the commonly used delete-1 jackknife, the robust standard error in Mplus, and a modified version of the commonly used delete-1 jackknife. Findings revealed that the resampling techniques clearly outperformed the robust standard error in rather small samples with high levels of heteroscedasticity. Moreover, Zitzmann’s jackknife tended to perform somewhat better than the two versions of the delete-1 jackknife and was much faster.

Cognitive diagnostic models (CDMs) are increasingly being used in various assessment contexts to identify cognitive processes and provide tailored feedback. However, the most commonly used estimation method for CDMs, marginal maximum likelihood estimation with Expectation–Maximization (MMLE-EM), can present difficulties when sample sizes are small. This study compares the results of different estimation methods for CDMs under varying sample sizes using simulated and empirical data. The methods compared include MMLE-EM, Bayes modal, Markov chain Monte Carlo, a non-parametric method, and a parsimonious parametric model such as Restricted DINA. We varied the sample size, and assessed the bias in the estimation of item parameters, the precision in attribute classification, the bias in the reliability estimate, and computational cost. The findings suggest that alternative estimation methods are preferred over MMLE-EM under low sample-size conditions, whereas comparable results are obtained under large sample-size conditions. Practitioners should consider using alternative estimation methods when working with small samples to obtain more accurate estimates of CDM parameters. This study aims to maximize the potential of CDMs by providing guidance on the estimation of the parameters.

This article is an introduction to Cluster Optimized Proximity Scaling (COPS) aimed at practitioners, as well as a tutorial on the usage of the corresponding R package cops. COPS is a variant of multidimensional scaling (MDS) that aims at providing a clustered configuration while still representing multivariate dissimilarities faithfully. It subsumes most popular MDS versions as special cases. We illustrate the ideas, use, flexibility and versatility of the method and the package with data from clinical psychology on how modules of the Modular Approach to Therapy for Children (MATCH) are used by clinicians in the wild. We supplement the COPS analyses with density-based hierarchical clustering in the original space and faceting with support vector machines. We find that scaling with COPS gives a sensible and insightful spatial arrangement of the modules, allows easy identification of clusters of modules and provides clear facets of modules corresponding to the MATCH protocols. In that respect COPS works better than both standard MDS and clustering.

Stevens’s classification of scales into nominal, ordinal, interval, and ratio types is among the most controversial yet resilient ideas in psychological and educational measurement. In this essay, I challenge the notion that scale type is essential for the development of measures in these fields. I highlight how the concept of scale type, and of interval-level measurement in particular, is variously interpreted by many researchers. These (often unstated) differences in perspectives lead to confusion about what evidence is appropriate to demonstrate interval-level measurement, as well as the implications of scale type for research in practice. I then borrow from contemporary ideas in the philosophy of measurement to demonstrate that scale type can only be established in the context of well-developed theory and through experimentation. I conclude that current notions of scale type are of limited use, and that scale type ought to occupy a lesser role in psychometric discourse and pedagogy.