The Role of Locus of Control and Perceived Stress in Dealing with Unemployment during Economic Crisis

This study aimed to examine the psychological profile of unemployed people during economic crisis. Specifically, we studied the relationships between locus of control (LOC), perceived stress, maintenance of health behaviors such as smoking, drinking and exercising and the optimism about finding a new job. Participants were 201 Greek unemployed, ranging from 20 to 64 years old. Measures included, among others, a short form of LOC Scale, the 14-item Perceived Stress Scale, as well as health behavior and perceived optimism indicators. Results indicated that the discharged felt unable, even powerless to efficiently cope with the unemployment status. Unemployed with an external LOC orientation tend to experience more stress. Among all of the examined health behaviors, only physical activity seems to be related to LOC orientation as well as perceived stress. Finally, the external LOC orientation of the unemployed as well as their higher levels of perceived stress were related to a tendency to feel less optimistic about finding a new job. Future interventions, either aiming to control stress and promote physical activity or aiming to eliminate external LOC orientation should be applied as soon as possible after job loss. Keywords: Locus of Control, Perceived Stress, Unemploymen

Dimensionality assessment in ordinal data: a comparison between parallel analysis and exploratory graph analysis

In the social sciences, accurately identifying the dimensionality of measurement scales is crucial for understanding latent constructs such as anxiety, happiness, and self-efficacy. This study presents a rigorous comparison between Parallel Analysis (PA) and Exploratory Graph Analysis (EGA) for assessing the dimensionality of scales, particularly focusing on ordinal data. Through an extensive simulation study, we evaluated the effectiveness of these methods under various conditions, including varying sample size, number of factors and their association, patterns of loading magnitudes, and symmetrical or skewed item distributions with assumed underlying normality or non-normality. Results show that the performance of each method varies across different scenarios, depending on the context. EGA consistently outperforms PA in correctly identifying the number of factors, particularly in complex scenarios characterized by more than a single factor, high inter-factor correlations and low to medium primary loadings. However, for datasets with simpler and stronger factor structures, specifically those with a single factor, high primary loadings, low cross-loadings, and low to moderate interfactor correlations, PA is suggested as the method of choice. Skewed item distributions with assumed underlying normality or non-normality were found to noticeably impact the performance of both methods, particularly in complex scenarios. The results provide valuable insights for researchers utilizing these methods in scale development and validation, ensuring that measurement instruments accurately reflect theoretical constructs

Beyond tandem analysis: Joint dimension reduction and clustering in R

We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and reduced K-means; both methods combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means, i-FCB and cluster correspondence analysis, which combine multiple correspondence analysis with K-means. Two examples on real data sets are provided to illustrate the usage of the main functions

STABILITY OF JOINT DIMENSION REDUCTION AND CLUSTERING

Several methods for joint dimension reduction and cluster analysis of categorical, continuous or mixed-type data have been proposed over time. These methods combine dimension reduction (PCA/MCA/PCAmix) with partitioning clus- tering (K-means) by optimizing a single objective function. Cluster stability assess- ment is a critical and inadequately discussed topic in the context of joint dimension reduction and clustering. We introduce a resampling scheme that combines boot- strapping and a measure of cluster agreement to assess global cluster stability of joint dimension reduction and clustering solutions and a Jaccard similarity approach for empirical evaluation of the stability of individual clusters. Both approaches are imple- mented in the R package clustrd

Exploring the meaning and productivity of a polysemous prefix

This paper follows a corpus-based approach to the meaning and productivity of the Modern Greek prepositional prefix para-. A semantic categorization of the prefix is proposed and its productivity is measured across semantic categories, registers, text types and grammatical categories. Para- was found to be more productive in non-locational and evaluative meanings. Its most productive meaning is excess, while the locational meaning of proximity still remains strong. It is also more productive in written than spoken registers and the grammatical category of nouns. The findings of the study can have implications about the prefix’s ongoing grammaticalization and its affixal status

An Optimal Scaling Approach to Collaborative Filtering using Categorical Principal Component Analysis and Neighborhood Formation

Abstract. Collaborative Filtering (CF) is a popular technique employed by Recommender Systems, a term used to describe intelligent methods that generate personalized recommendations. The most common and accurate approaches to CF are based on latent factor models. Latent factor models can tackle two fundamental problems of CF, data sparsity and scalability and have received considerable attention in recent literature. In this work, we present an optimal scaling approach to address both of these problems using Categorical Principal Component Analysis for the low-rank approximation of the user-item ratings matrix, followed by a neighborhood formation step. The optimal scaling approach has the advantage that it can be easily extended to the case when there are missing data and restrictions for ordinal and numerical variables can be easily imposed. We considered different measurement levels for the user ratings on items, starting with a multiple nominal and consecutively applying nominal, ordinal and numeric levels. Experiments were executed on the MovieLens dataset, aiming to evaluate the aforementioned options in terms of accuracy. Results indicated that a combined approach (multiple nominal measurement level, "passive" missing data strategy) clearly outperformed the other tested options

Achaiki Iatriki : official publication of the medical society of western Greece and Peloponnesus

In the current issue, the editorial by Cauchi et al. argues for eco-friendly measures in endoscopy and emphasies the role of healthcare providers in reducing waste. The editorial adeptly employs the three Rs (Reduce, Reuse, Recycle) framework to tackle waste management, offering practical solutions. The editorial by Milionis et al. focuses on the reverse cascade screening for paediatric familial hypercholesterolaemia (FH), which is an upcoming tool for public health. Advantages, practices, and challenges regarding FH are thoroughly discussed. Lastly, the editorial by Fousekis et al. presents the main aspects of a chronic immune-mediated cutaneous disease, dermatitis herpetiformis (DH), which constitutes an extraintestinal manifestation of celiac disease, including its diagnosis, pathogenesis, and management. Moreover, this issue includes three review articles. The review article by Krontira et al. discusses the evolving data on the epidemiology, diagnostic approach and appropriate management of foreign body and caustic substance ingestion, based on updated guidelines published by gastroenterological and endoscopic societies. The review by Halliasos et al. provides data on the clinical presentation, diagnosis, and management of metastatic acute spinal cord compression, focusing on the importance of a multidisciplinary team approach, including spine surgeons, radiation oncologists, medical oncologists, palliative care clinicians, physiotherapists, and psychologists. Lastly, the review by Schinas et al. outlines the potential of immune modulation in the treatment of infections and the need for individualised approaches in the modern world of personalised medicine by examining some of the key strategies and immune-based therapies being developed to combat infectious diseases.peer-reviewe

A Fuzzy Coding Approach to Data Processing Using the Bar

The bar is an alternative to Likert-type scale as a response format option used in closed-form questionnaires. An important advantage of using the bar is that it provides a variety of data post-processing options (i.e., ways of partitioning the values of a continuous variable into discrete groups). In this context, continuous variables are usually divided into equal-length or equalarea intervals according to a user-specified distribution (e.g. the Gaussian). However, this transition from continuous into discrete can lead to a significant loss of information. In this work, we present a fuzzy coding of the original variables which exploits linear and invertible triangular membership functions. The proposed coding scheme retains all of the information in the data and can be naturally combined with an exploratory data analysis tech nique, Correspondence Analysis, in order to visually investigate both linear and non-linear variable  associations. The proposed approach is illustrated with a real-world application to a student course evaluation dataset

Benchmarking distance-based partitioning methods for mixed-type data

Supplementary Material of the paper "Benchmarking distance-based partitioning methods for mixed-type data