The use of multiple correspondence analysis to explore associations between categories of qualitative variables in healthy ageing

In pressPopulation studies are often characterized by a plethora of data that includes quantitative to qualitative variables. The main focus of this study was to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing underlying structures in large datasets used to investigate cognitive ageing. Principal component analysis (PCA) was used to obtain main cognitive dimensions (based on the continuous neurocognitive test variables) and MCA to detect and explore relationships of cognitive, clinical, physical and lifestyle categorical variables across the low-dimensional space. Altogether the technique allows to not only simplify complex data, providing a detailed description of the data and yielding a simple and exhaustive analysis, but also to handle a large and diverse dataset comprised of quantitative, qualitative, objective and subjective data. Two PCA dimensions were identified (general cognition/executive function and memory) and two main MCA dimensions were retained. As expected, poorer cognitive performance was associated with older age, less school years, unhealthier lifestyle indicators and presence of pathology. Interestingly, the first MCA dimension indicated the clustering of general/executive function and lifestyle indicators and education, while the second association between memory and clinical parameters and age. The clustering analysis with object scores method was used to identify groups sharing similar characteristics within each of the identified dimensions. Following MCA findings, the weaker cognitive clusters in terms of memory and executive function comprised individuals with characteristics contributing to a higher MCA dimensional mean score (age, less education and presence of indicators of unhealthier lifestyle habits and/or clinical pathologies). MCA provided a powerful tool to explore complex ageing data, covering multiple and diverse variables, showing not only if a relationship exists between variables but also how they are related, offering at the same time statistical results can be seen both analytically and visually.EC -European Commissio

Predicting continuous conflict perception with Bayesian Gaussian processes

Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception

National Identity versus European Identity The Dimensions of Change Developing the Greek teachers’ European identity

This study examines the attitudes of Greek teachers towards Europe in relation with their national identity. Because of the Greek debt crisis and Greece’s entry into the IMF and EFSM mechanisms, two successive studies were conducted using the same questionnaire (the first with 1036 and the second with 482 teachers from all over Greece in 2009 and 2011 respectively). A two-step cluster analysis was performed and four particular dimensions were identified. The statistical analysis showed that there is a shift away from a European orientation, a finding that is attributed to economic difficulties

EEG Searchlight Decoding Reveals Person- and Place-specific Responses for Semantic Category and Familiarity.

Proper names are linguistic expressions referring to unique entities, such as individual people or places. This sets them apart from other words like common nouns, which refer to generic concepts. And yet, despite both being individual entities, one's closest friend and one's favorite city are intuitively associated with very different pieces of knowledge-face, voice, social relationship, autobiographical experiences for the former, and mostly visual and spatial information for the latter. Neuroimaging research has revealed the existence of both domain-general and domain-specific brain correlates of semantic processing of individual entities; however, it remains unclear how such commonalities and similarities operate over a fine-grained temporal scale. In this work, we tackle this question using EEG and multivariate (time-resolved and searchlight) decoding analyses. We look at when and where we can accurately decode the semantic category of a proper name and whether we can find person- or place-specific effects of familiarity, which is a modality-independent dimension and therefore avoids sensorimotor differences inherent among the two categories. Semantic category can be decoded in a time window and with spatial localization typically associated with lexical semantic processing. Regarding familiarity, our results reveal that it is easier to distinguish patterns of familiarity-related evoked activity for people, as opposed to places, in both early and late time windows. Second, we discover that within the early responses, both domain-general (left posterior-lateral) and domain-specific (right fronto-temporal, only for people) neural patterns can be individuated, suggesting the existence of person-specific processes

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Preventing Household Food Waste in Italy: A Segmentation of the Population and Suggestions for Action

Household food waste represents one of the main challenges threatening the sustainability of modern food systems globally. As is widely recognised, a deeper understanding of wasteful behaviour profiles is the starting point of designing intervention strategies. The overall objective of this research is to explore the role of psychological factors that influence household wasteful food behaviour in Italy and to profile consumers with heterogeneous personal attitudes towards wasting food. Starting with data collected through a web-based survey realized on a sample of 530 individuals responsible for household shopping, a principal component analysis and a two-step cluster analysis revealed three different segments of consumers with heterogeneous wasteful behaviours. The clusters differ in relation to psychological factors, such as moral attitudes and concerns about and intentions to reduce food waste. The study findings provide insights for implementing prevention, reduction, and recovery strategies tailored to these different consumer profiles

Students’ evaluation of academic courses: An exploratory analysis to an Italian case study

Students’ evaluations of teaching is a common practice in higher education institutions, with the main purpose of improving course quality and effectiveness. In this paper we would like to contribute to the existing literature on course and teaching evaluation by providing an empirical analysis based on questionnaires collected by an Italian private institution, namely the Libera Università Maria Ss. Assunta (LUMSA), for several degrees in Social Sciences. In order to identify the main factors affecting students’ satisfaction, we use not only teaching indicators and degree-specific characteristics, but also control for student-specific characteristics. Our analysis is based on a Multiple Correspondence Analysis for categorical variables, which represents a very useful method to study the multidimensional relationship among qualitative variables, along with a hierarchical clustering, in order to better summarize the results. Our findings reveal that student satisfaction relates to teaching and course organization. Moreover, we find some evidence that students typically evaluate their course on the basis of their experience rather than their personal interests.publishedVersio

Students' evaluation of academic courses: An exploratory analysis to an Italian case study

Students' evaluations of teaching is a common practice in higher education institutions, with the main purpose of improving course quality and effectiveness. In this paper we would like to contribute to the existing literature on course and teaching evaluation by providing an empirical analysis based on questionnaires collected by an Italian private institution, namely the Libera Università Maria Ss. Assunta (LUMSA), for several degrees in Social Sciences. In order to identify the main factors affecting students' satisfaction, we use not only teaching indicators and degree-specific characteristics, but also control for student-specific characteristics. Our analysis is based on a Multiple Correspondence Analysis for categorical variables, which represents a very useful method to study the multidimensional relationship among qualitative variables, along with a hierarchical clustering, in order to better summarize the results. Our findings reveal that student satisfaction relates to teaching and course organization. Moreover, we find some evidence that students typically evaluate their course on the basis of their experience rather than their personal interests. publishedVersio

Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches

Introduction: Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design. Methods: Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay. Results: i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”. ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively. iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features. Conclusions: Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people