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Following the status-anxiety hypothesis, the psychological consequences of income inequality should be particularly severe for economically vulnerable individuals. Oddly, however, income inequality is often found to affect vulnerable low-income and advantaged high-income groups equally. We argue that economic vulnerability is better captured by a financial-scarcity measure and hypothesize that income inequality primarily impairs the psychological health of people facing scarcity. First, repeated cross-sectional international data (from the World Values Survey: 146,034 participants; 105 country waves) revealed that the within-country effect of national income inequality on feelings of unhappiness was limited to individuals facing scarcity (≈25% of the World Values Survey population). Second, longitudinal national data (Swiss Household Panel: 14,790 participants; 15,595 municipality years) revealed that the within-life-course effect of local income inequality on psychological health problems was also limited to these individuals (< 10% of the Swiss population). Income inequality by itself may not be a problem for psychological health but, rather, may be a catalyst for the consequences of financial scarcity.

This paper aims to introduce multilevel logistic regression alysis in a simple and practical way. First, we introduce the basic principles of logistic regression alysis (conditiol probability, logit transformation, odds ratio). Second, we discuss the two fundamental implications of running this kind of alysis with a nested data structure: In multilevel logistic regression, the odds that the outcome variable equals one (rather than zero) may vary from one cluster to another (i.e. the intercept may vary) and the effect of a lower-level variable may also vary from one cluster to another (i.e. the slope may vary). Third and filly, we provide a simplified three-step “turnkey” procedure for multilevel logistic regression modeling:-Prelimiry phase: Cluster- or grand-mean centering variables -Step #1: Running an empty model and calculating the intraclass correlation coefficient (ICC) -Step #2: Running a constrained and an augmented intermediate model and performing a likelihood ratio test to determine whether considering the cluster-based variation of the effect of the lower-level variable improves the model fit -Step #3 Running a fil model and interpreting the odds ratio and confidence intervals to determine whether data support your hypothesisCommand syntax for Stata, R, Mplus, and SPSS are included. These steps will be applied to a study on Justin Bieber, because everybody likes Justin Bieber.1

In this article, we review existing research on the complementarity of social media data and survey data for the study of public opinion. We start by situating our review in the extensive literature (N = 187) about the uses, challenges, and frameworks related to the use of social media for studying public opinion. Based on 187 relevant articles (141 empirical and 46 theoretical) - we identify within the 141 empircal ones six main research approaches concerning the complementarity of both data sources. Results show that the biggest share of the research has focused on how social media can be used to confirm survey findings, especially for election predictions. The main contribution of our review is to detail and classify other growing complementarity approaches, such as comparing both data sources on a given phenomenon, using survey measures as a proxy in social media research, enriching surveys with SMD, recruiting individuals on social media to conduct a second survey phase, and generating new insight on “old” or “under-investigated” topics or theories using SMD. We discuss the advantages and disadvantages associated with each of these approaches in relation to four main research purposes, namely the improvement of validity, sustainability, reliability, and interpretability. We conclude by discussing some limitations of our study and highlighting future paths for research.

This piece is meant to help you understand and master two-level linear modeling in an accessible, swift, and fun way (while being based on rigorous and up-to-date research). It is divided into four parts:* PART 1 presents the three key principles of two-level linear modeling.* PART 2 presents a three-step procedure for conducting two-level linear modeling using SPSS, Stata, R, or Mplus (from centering variables to interpreting the cross-level interactions).* PART 3 presents the results from a series of simulations comparing the performances of SPSS, Stata, R, and Mplus.* PART 4 gives a Q&A addressing multilevel modeling issues pertaining to statistical power, effect sizes, complex design, and nonlinear two-level regression.The empirical example used in this tutorial is based on genuine data pertaining to ʼ90s and post-ʼ00s boy band member hotness and Instagram popularity. In reading this paper, you will have the opportunity to win a signed picture of Justin Timberlake.

Building new value chains, through the valorization of biomass components for the development of innovative bio‐based products (BBPs) aimed at specific market sectors, will accelerate the transition from traditional production technologies to the concept of biorefineries. Recent studies aimed at mapping the most relevant innovations undergoing in the field of BBPs (Fabbri et al. 2019, Final Report of the Task 3 BIOSPRI Tender Study on Support to R&I Policy in the Area of Bio‐based Products and Services, delivered to the European Commission (DG RTD)), clearly showed the dominant position played by the plastics sector, in which new materials and innovative technical solutions based on renewable resources, concretely contribute to the achievement of relevant global sustainability goals. New sustainable solutions for the plastic sector, either bio‐based or bio‐based and biodegradable, have been intensely investigated in recent years. The global bioplastics and biopolymers market size is expected to grow from USD 10.5 billion in 2020 to USD 27.9 billion by 2025 (Markets and Markets, 2020, Bioplastics & Biopolymers Market by Type (Non‐Biodegradable/Bio‐Based, Biodegradable), End‐Use Industry (Packaging, Consumer Goods, Automotive & Transportation, Textiles, Agriculture & Horticulture), Region ‐ Global Forecast to 2025), and this high growth is driven primarily by the growth of the global packaging end‐use industry. Such relevant opportunities are the outcomes of intensive scientific and technological research devoted to the development of new materials with selected technical features, which can represent feasible substitutes for the fossil‐based plastic materials currently used in the packaging sectors and other main fields. This article offers a map of the latest developments connected to the plastic sector, achieved through the application of biotechnological routes for the preparation of completely new polymeric structures, or drop‐in substitutes derived from renewable resources, and it describes the specific role played by biotechnology in promoting and making this transition faster. Biotechnology holds the power to boost the transition from fossil‐based to bio‐based plastics by offering new sustainable routes for polymer synthesis based on waste biomasses.

Vaccine hesitancy is a public health concern in South Africa and internationally. Literature on vaccine hesitancy associates this with mistrust of the government. We present a qualitative analysis of opinions about COVID-19 vaccination expressed by South African Twitter (now X) users during the first year of the vaccine rollout in South Africa. We conducted a thematic analysis of 800 randomly selected tweets containing vaccine-related keywords, sampled from four time periods in 2021. We categorised comprehensible South African non-news tweets as pro-vaccination (24.75% of sample), anti-vaccination (20.25%) or ambivalent (4.5%), and then identified themes. Among pro-vaccination tweets, the most common themes were criticism of the government's handling of vaccine procurement and the rollout; concerns that the vaccine was urgently needed and/or not being made available fast enough; and statements that vaccines were safe and/or effective against COVID-19. Among anti-vaccination tweets, the most common themes were claims that the vaccine was harmful or too risky; suspicion of the government's intentions with respect to the vaccine it was offering the public; and opposition to mandatory or 'forced' vaccination. Criticism and mistrust of the government were present among both pro- and anti-vaccination tweets, though for different reasons. We discuss this in light of literature recommending trust-building as a response to vaccine hesitancy.

Erik Erikson’s theory of human development defines generativity as the concern for the continuation of life after an individual’s death. According to the theory, such a concern has a wide spectrum that ranges from the desire to procreate to the willingness to contribute for the sake of generations that have yet to come, and is thus closely related to concepts of social responsibility and agency. Although this is a well-known aspect of the theory it is only marginally measured in the common quantitative measures of generativity—e.g. the Loyola Generativity Scale. In this study we present the Social Generativity Scale (SGS), which is focused on responsibility for future generation. Correlational analysis showed that the SGS is more consistently linked to future orientation than other generativity measures (i.e. measured with consideration of future consequences), inclusiveness, and political engagement, and negatively related to social dominance orientation and prejudice. The results suggest that the SGS better captures the social responsibility dimension of the generativity concept than previous measures, and for this reason it is complementary to those scales that comprehend generativity as the concern for personal continuation after death and desire of parenting.

Background: While the negative effects of spousal bereavement on well-being are well documented in empirical research, the large individual differences in psychological adaptation are still not well understood. Objective: This contribution aims to identify patterns of psychological adaptation to spousal loss in old age and to shed light on the role of intra- and interpersonal resources and contextual factors as discriminant variables among these patterns. Methods: The data stem from a cross-sectional questionnaire study of 402 widowed individuals (228 women, 174 men) aged between 60 and 89 years (mean age 74.41 years), who lost their partner within the last 5 years, and 618 married individuals, who served as controls (312 women, 306 men; mean age 73.82 years). Results: The exploratory latent profile analysis of the well-being outcomes of depressive symptoms, hopelessness, loneliness, life satisfaction and subjective health revealed three different groups in the widowed sample: ‘resilients' (54% of the sample), ‘copers' (39%) and ‘vulnerables' (7%). The most important variables for group allocation were intrapersonal resources - psychological resilience and the Big Five personality traits - but also the quality of the former relationship and how the loss was experienced. Conclusion: Successful adaptation to spousal loss is primarily associated with high scores in psychological resilience and extraversion and low scores in neuroticism. Our results shed light on the variability in psychological adaptation and underline the important role of intrapersonal resources in facing spousal loss in old age.

Relying on the 2013 and 2016 rounds of individual questionnaires from the Swiss Household Panel (SHP), we use multiple correspondence analysis to map Swiss media consumption practices while making use of the longitudinal character of panel data in an innovative way. Our results show that individual practices can be distinguished along two main dimensions: on the one hand, the reliance on new media, which is explained mainly by the age cohort, and on the other hand, the consumption of news, which is explained mainly by changes in political interest as well as by gender.

Post‐surgery microbial infections are still one of the main reasons for implant failure, which results in very high physical and psychological pain for the patient and an increased cost for the healthcare system. A polymer nanocomposite antibacterial coating on titanium implants represents a valuable alternative to the more expensive and energy‐consuming technological solutions nowadays used. In this regard, a sprayable thermoset nanocomposite composed of silanized‐terminals polyethylene glycol (PEG)/ZnO nanoparticle is herein proposed. Initially, PEG's terminals' solvent‐free silanization and curing are studied by Fourier Transform Infrared and µRaman spectroscopies. Scanning Electron Microscope investigations and scratch tests have shown that the spraying procedure optimization and the oxidation treatment of the titanium substrate lead to a homogeneous coverage and improved adhesion of the coatings. The antibacterial activity is tested against not only both S. aureus and P. aeruginosa bacterial American Type Culture Collection strains, but also using very aggressive antibiotic‐resistant clinical strains. Interestingly, antibacterial activity, evaluated by time‐killing tests, is observed for all tested bacterial strains. Live/dead tests further confirm that 5 wt% of ZnO allows obtaining a bacteriostatic activity within 24 h, whereas a complete growth inhibition (bactericidal activity) of both tested strains is observed for coatings with 20 wt% of ZnO nanoparticles. Sprayable silanized‐polyethylene glycol (PEG)/ZnO nanocomposite coatings are developed for preventing post‐surgery microbial infections of titanium implants. The proposed coatings have a simple and reproducible application procedure and show very high adhesion to the titanium substrate. Moreover, the combination of PEG and ZnO results in a material with versatile antibacterial properties against several different bacterial strains and biofilm formation.