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April 30, 2026
HSE Researchers Compile Scientific Database for Studying Childrens Eating Habits
The database created at HSE University can serve as a foundation for studying children’s eating habits. This is outlined in the study ‘The Influence of Age, Gender, and Social-Role Factors on Children’s Compliance with Age-Based Nutritional Norms: An Experimental Study Using the Dish-I-Wish Web Application.’ The work has been carried out as part of the HSE Basic Research Programme and was presented at the XXVI April International Academic Conference named after Evgeny Yasin.
April 30, 2026
New Foresight Centre Study Identifies the Most Destructive Global Trends for Humankind
A team of researchers from the HSE International Research and Educational Foresight Centre has examined how global trends affect the quality of human life—from life expectancy to professional fulfilment. The findings of the study titled ‘Human Capital Transformation under the Influence of Global Trends’ were published in Foresight.
April 28, 2026
Scientists Develop Algorithm for Accurate Financial Time Series Forecasting
Researchers at the HSE Faculty of Computer Science benchmarked more than 200,000 model configurations for predicting financial asset prices and realised volatility, showing that performance can be improved by filtering out noise at specific frequencies in advance. This technique increased accuracy in 65% of cases. The authors also developed their own algorithm, which achieves accuracy comparable to that of the best models while requiring less computational power. The study has been published in Applied Soft Computing.

 

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Психометрическое моделирование: систематическая критика базовых допущений и некоторые альтернативы

Сибирский психологический журнал. 2022. № 86. С. 6–25.
Tyumeneva Y. A.

Due to the popularity of psychometrics, where probabilistic modelling takes a leading position, it seems useful to present a critical view of these techniques, which, unfortunately, is not extensively discussed, especially in the Russian-language literature. This paper is a systematic review of the growing criticism of several assumptions of psychometrics: 1) the existence of unobservable personality traits and abilities which are supposed to determine the performance of test items; 2) the stochastic nature of traits, and as a consequence, the need for probabilistic models to study them; 3) aggregate data represent individual characteristics; 4) as in the natural sciences, models in psychometrics help reveal psychological reality that is not directly observable; 5) if testing the agreement of empirical data with a model yields a positive result, then the model correctly describes reality. These assumptions are criticized on the basis of the circular nature of the definitions of the traits themselves, logical errors ingrained in the assumptions, the methodological mixture of variability and randomness regarding behavior, the lack of a causal link between inter-individual variation in responses and individual responses; superficial analogies with models in the natural science to which psychometrics refers, as well as the substitution of scientific tasks for instrumental and pragmatic ones. We conclude that modelling in psychometrics is counterproductive if used as a method of exploring the psychological reality behind the test. Some alternative practices of quantitative research are discussed. For example, testing the existence of variation at the level of the individual and the experimental search for its explanation. There are other possible alternatives, such as, a network perspective on psychological phenomena; faceted theory; or observation-oriented modeling. Although this kind of research is much more difficult to implement than standard “goodness-of-fit” tests, it is probably this kind of research that can provide an increase in psychological knowledge.

Research target: Psychology
Language: Russian
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Keywords: моделированиереализмmeasurementrealismрепрезентацияrepresentationпсихометрикаmodellingpsychometricsдопущениячерта личности измерениеlatent constructtraitassumptionsлатентный конструкт
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