Search

The aim of the current study was to assess associations between loneliness and personal wellbeing among young people. Framed by social ecological theory, the study examined demographic, social, and community factors associated with personal wellbeing and, critically, identified malleable moderators of the relationship between loneliness and personal wellbeing that could be targeted in intervention efforts.We used cross-sectional, secondary data from 965 young people (aged 16-24) from the Community Life Survey in England. Loneliness was measured using a single-item direct measure; personal wellbeing was measured through a composite measure containing items relating to happiness, life satisfaction and a sense that life is worthwhile (α = 0.88). Regression techniques were used to assess direct associations between individual, social, and community factors and personal wellbeing, and identify factors that moderate the association between loneliness and personal wellbeing. Loneliness was negatively associated with wellbeing. Frequency of chatting with neighbours and having people to provide help moderated the relationship between loneliness and personal wellbeing. Young people who were full-time students or reported good physical health had higher personal wellbeing, while being a carer was predictive of decreased wellbeing. Having people to count on was the only social variable significantly associated with personal wellbeing, while all community factors were found to be strongly associated with increased wellbeing. Our results advance the current literature by identifying that supportive social relationships and close community ties are important for reducing the negative impact of loneliness on youth wellbeing. Intervention efforts to improve wellbeing could benefit from specifically targeting these aspects of young people’s social and community lives, while acknowledging individual vulnerabilities, such as poor physical health.

Vignette methods are widely used in psychology and the social sciences to obtain responses to multi-dimensional scenarios or situations. Where quantitative data are collected this presents challenges to the selection of an appropriate statistical model. This depends on subtle details of the design and allocation of vignettes to participants. A key distinction is between factorial survey experiments where each participant receives a different allocation of vignettes from the full universe of possible vignettes and experimental vignette studies where this restriction is relaxed. The former leads to nested designs with a single random factor and the latter to designs with two crossed random factors. In addition, the allocation of vignettes to participants may lead to fractional or unbalanced designs and a consequent loss of efficiency or aliasing of the effects of interest. Many vignette studies (including some factorial survey experiments) include unmodeled heterogeneity between vignettes leading to potentially serious problems if traditional regression approaches are adopted. These issues are reviewed and recommendations are made for the efficient design of vignette studies including the allocation of vignettes to participants. Multilevel models are proposed as a general approach to handling nested and crossed designs including unbalanced and fractional designs. This is illustrated with a small vignette data set looking at judgements of online and offline bullying and harassment.

This study examines how people with disabilities and chronic health conditions—members of a large and diverse group often overlooked by Canadian public policy—are making sense of the Canadian federal government’s response to COVID-19. Using original national online survey data collected in June 2020 (N= 1,027), we investigate how members of this group view the government’s overall response. Although survey results show broad support for the federal government’s pandemic response, findings also indicate fractures based on disability type and specific health condition, political partisanship, region, and experiences with COVID-19. Among these, identification with the Liberal party and receipt of CERB stand out as associated with more positive views. Further examination of qualitative responses shows that these views are also linked to differing perspectives surrounding government benefits and spending, partisan divisions, and other social and cultural cleavages. La présente étude porte sur la façon dont les personnes atteintes de handicap ou de maladie chronique — appartenant à un vaste groupe diversifié, souvent ignoré des politiques publiques canadiennes — interprètent la réaction du gouvernement fédéral canadien à la COVID‑19. À l’aide de données nationales exclusives tirées d’un sondage en ligne effectué en juin 2020 (N= 1 027), nous analysons le point de vue des membres de ce groupe quant à la réaction globale du gouvernement. Bien que les résultats du sondage témoignent d’un large appui à la réaction du gouvernement fédéral à la pandémie, nos observations révèlent également des failles selon la nature du handicap et les particularités liées à l’état de santé, l’allégeance politique, la région et les expériences en ce qui a trait à la COVID‑19. Entre autres éléments, l’allégeance au Parti libéral et le statut de bénéficiaire de la PCU se signalent comme étant associés à des points de vue plus positifs. Un examen plus approfondi des réactions qualitatives montre que ces points de vue sont également liés à des perspectives divergentes entourant les prestations et les dépenses gouvernementales, les divisions partisanes et autres clivages sociaux et culturels.

Personally expressed identity is who or what an individual themselves says they are, and it should be studied at scale. At scale means with data on millions of individuals, which is newly available and comes timestamped and geocoded. This work introduces a dataset for the study of identity at scale and describes the method for collecting and aggregating such data. Further, tools and theory for working with the data are presented. A demonstration analysis provides evidence that personal, individual development and changing cultural norms can be observed with these data and methods.

In marketing world, social media is playing a crucial role nowadays. One of the most recent strategies that exploit social contacts for the purpose of marketing, is referral marketing, where a person shares information related to a particular product among his/her social contacts. When this spreading of marketing information goes viral, the diffusion process looks like an epidemic spread. In this work, we perform a systematic study with a goal to device a methodology for using the huge amount of survey data available to understand customer behaviour from a more mathematical and quantitative perspective. We perform an unsupervised natural language processing and hierarchical clustering based analysis of the responses of a recent survey focused on referral marketing to correlate the customers’ psychology with transitional dynamics, and investigate some major determinants that regulate the diffusion of a campaign. In addition to natural language processing for topic modeling, detailed differential equation based analysis and graph theoretical treatment have been carried out to explore the conditions of success for the campaign in terms of realistic parameters both for homogeneous and heterogeneous population structure. Finally, experiments have been performed for generation of a recommendation network to understand the diffusion dynamics in realistic scenario. A complete mathematical treatment with analysis over real social networks helped us to determine key customer motivations and their impacts on a marketing strategy, which are important to ensure an effective spread of a designed marketing campaign. Because of its systematic generalized formulation, the prescribed quantitative framework may be useful in all areas of social dynamics, beyond the field of marketing.

Climate change has led to rising sea levels and warmer sea surface temperatures. These factors contribute greatly to the intensity of hurricanes and floods they provoke. Projections estimate there will be an increase of 45% to 87% in the frequency of Category >4 hurricanes originating in the Atlantic Basin, which typically impact the Caribbean and Continental United States of America. During the 2019 Hurricane Season, there were 20 depressions, 18 storms, 6 hurricanes and 3 major hurricanes. Through this work, we explored the response on Social Media to these natural phenomena as a function of their trajectory, intensity, and previous exposure of the population to intense natural disasters. Data was collected through the Twitter API. The influences of hurricane proximity and intensity on volume of Social Media production was explored. Hurricane Dorian, with its trajectory strongly threatening the previously exposed Puerto Rico, and eventually causing widespread damage in the Abaco Islands of the Bahamas, presented the strongest case for the evaluation of the dichotomy of responses between populations with differences in previous history of exposure. The landscape of historic hurricane exposure Caribbean has radically changed in recent years. Taking advantage of Big Data to help elucidate these dynamics could be instrumental in the tailoring of emergency preparedness plans and the effective design of mental health first aid strategies.

During free play, children decide with which peers they want to spend their time. The distribution of time and social effort amongst their peers can be indicative for the inclusivity of the group. In this appraisal paper, we explore a recently introduced measure to estimate social mixing in groups, called social fluidity. We estimate social fluidity for 27 data sets of children's face-to-face interaction networks collected during playtime breaks and discuss how this measure can be used by field experts to characterize group behavior as well as individual behavior. We therefore draft a step-by-step plan to analyze proximity data based on the behavioral model and the social fluidity measure introduced by Colman et al.

This study determined the best fit model of work performance among print media employees in Region XI. In an attempt to answer the objective, a quantitative research method utilizing a descriptive-correlational research design was employed. The study was conducted in Region XI which is located at the Southeastern part of Mindanao, Philippines. The employees of print media companies in Region XI were the respondents particularly those in printing press business industry that offers printing, publications, and adverting services. There were 419 employees of printing industry participated in the study. The study was employed the questionnaires adapted from different studies and to fit the context of the respondents of this study. The instrument was divided into four parts such as Innovative Work Behavior, Work Engagement, Organization learning and Work Performance. Results revealed that there is a significant relationship between innovative work behavior, organizational learning, work engagement and work performance of print media employees. Among the three independent variables, only the organizational learning and work engagement significantly predict employees work performance while the innovative work behavior does significantly predict employees work performance. Since organizational learning and work engagement were significant predictors of employees work performance, it is recommended that the management of print media will consider these variables in formulating training like advance multimedia, seminars on capacity building, and rewards and incentives program to improve employees work performance.

Listening to music can cause experiences of nostalgia and melancholia. Although both concepts are theoretically related, to date they have not been analyzed together regarding their emotional and cognitive profiles. In this study, we identify their theoretical underpinnings and determine how they can be measured empirically. We analyze how listening to music causes nostalgia and melancholia, and whether both experiences are related to different behavioral intentions. To this end, we conducted an online experiment with 359 participants who listened to music they considered either nostalgic, melancholic, or neutral. Afterward, participants answered 122 questionnaire items related to nostalgia and melancholia. Using Structural Equation Modeling, and more specifically Multiple Indicators and Multiple Causes Modeling, we first developed two new scales: the Formative Nostalgia Scale and the Formative Melancholia Scale. Both scales consist of five items each. Results showed that listening to music indeed increased nostalgia and melancholia. Although considerably different, the concepts are related. Listening to nostalgic music increases melancholia, whereas listening to melancholic music does not increase nostalgia. Also, both experiences are related to different behavioral intentions. Whereas experiencing nostalgia was associated with a stronger intention to share the music and to listen to it again, experiencing melancholia revealed the exact opposite relation.

Predicting outcomes of educational interventions before investing in large-scale implementation efforts in school settings is essential for educational policy-making. However, due to time and resource limitations, conducting longitudinal, large-scale experiments testing outcomes of interventions in authentic settings is difficult. Here, we introduce the deep learning method as a way to address this issue and illustrate the use of the deep learning method for the prediction of intervention outcomes through a MATLAB implementation. The presented deep learning method extracts predictable patterns from an empirical dataset to simulate large-scale intervention outcomes. Findings from our simulations suggest that the deep learning applied simulation model can predict intervention outcomes significantly more accurately compared to the traditional regression analysis methods.

Purpose. To examine the co-occurrence of mental health (depression, anxiety, nonsuicidal self-injury, suicide ideation) and substance use outcomes among female, male, and transgender–gender nonbinary (TNB) college students exposed to sexual assault (SA).Methods. Data were drawn from a 2018 U.S. national survey of college student wellbeing (N = 50,438). Inverse propensity-weighted three-step latent class analysis was used to examine co-occurrence of outcomes while adjusting for 31 potential confounders of the relation between SA exposure and outcomes.Results. Four latent classes were identified for female and male participants, and two for TNB participants, reflecting a range from low to high outcome risk. Exposure to SA was associated with significantly and substantially increased odds (ORs: 2.03–3.64) of membership to the highest-risk outcome class compared to the lowest risk class.Conclusions. Exposure to SA in the college setting is associated with substantially increased risk of co-occurrence of depression, anxiety, non-suicidal self-injury, and suicide ideation regardless of gender identity.

Objective: Dating violence (DV) is a psychosocial problem which impacts the health and wellbeing of affected individuals. Many studies have reported a high prevalence and various determinants of DV; however, there is a gap of consolidated evidence on this problem. This umbrella review aimed to synthesize the current evidence on the prevalence and determinants of DV from systematic reviews and meta-analyses. Methods: We systematically searched ten major databases and additional sources to retrieve systematic reviews or meta-analyses reporting prevalence or determinants of DV, published as peer-reviewed journal articles in English language till October 13, 2019. We extracted and synthesized the findings, reported the prevalence of DV, and categorized the determinants of DV using the socioecological model. Results: We found 16 eligible systematic reviews and meta-analyses. Most of these studies were conducted in developed countries with a majority of adolescent and young adult participants. Studies reported varying prevalence of physical, sexual, psychological/emotional, and technology-assisted DV ranging from 0.1% to 57.5%, 0.1% to 64.6%, 4.2% to 97%, and 5.8% to 92%, respectively. The determinants of DV at different socioecological levels, including individual behavior, substance abuse, psychiatric conditions, experiencing violence and maltreatment; interpersonal factors like family and peer relationships; community and neighborhood characteristics; patriarchy, culture, and socioeconomic equalities were reported. Conclusions: These findings suggest a high burden of DV among young populations, who are vulnerable to various socioecological determinants of DV. Future research should examine how these factors influence DV and multi-level interventions should be adopted to address the same.

NOTE: A heavily revised version of this paper was later published under a different title. The revised paper is available at this link: https://journals.sagepub.com/doi/full/10.1177/0049124120986178 ABSTRACT: Statistical network methods have grown increasingly popular in the social sciences. However, like other nonlinear probability models, statistical network model parameters can only be identified to a scale and cannot be compared between groups or models fit to the same network. This study addresses these issues by developing methods for mediation and moderation analyses in exponential random graph models (ERGM). It first discusses ERGM as an autologistic regression to illustrate that ERGM estimates can be affected by unobserved heterogeneity. Second, it develops methods for mediation analysis for both discrete and continuous mediators. Third, it provides recommendations and methods for interpreting interactions in ERGM. Finally, it considers scenarios where interactions are implicated in mediation analysis. The methodological discussion is accompanied with empirical applications and extensions to other classes of statistical network models are discussed.

This study provides an extension of the social value orientation model and a tool, other-other Decomposed Games, to quantify the influence of social identity on social value orientations. Social identity is induced experimentally using the minimal group paradigm. Subsequently, the weights subjects add to the outcomes of outgroup others relative to ingroup others and to the absolute difference between the outcomes of ingroup and outgroup others are estimated. Results are compared to a control condition in which social identity is not induced. Results show that when the outgroup is better off than the ingroup, the average subject is spiteful: they derive negative utility from the outcomes of the outgroup other. When the ougroup is worse off than the ingroup, the average subject attaches similar weights to the outcomes of outgroup and ingroup others. There is also significant variation across subjects with respect to the level of ingroup bias.

Subjective emptiness is a pervasive transdiagnostic construct. The aim of this series of studies was to use construct validation procedures to better understand the nature and correlates of subjective emptiness. A literature review led to 88 items which were then trimmed to 53 via qualitative interviews with patients and experts. This preliminary scale was administered to 543 university students and 1,067 clinical participants along with measures of depression, meaning in life, borderline personality disorder, and impulsivity. The scale was reduced to 17 items following item analysis, factor and item response theory analyses, extreme groups comparisons, and examinations of criterion validity. This 17-item scale, along with measures of identity disturbance and maladaptive personality traits, was then administered to 1,016 clinical participants. The scale was trimmed to 7 items based on results from these data. Results strongly supported the unidimensionality of subjective emptiness and its transdiagnostic clinical value.

It is very difficult to know how international social linkages affect domestic ideological polarization because we can never observe polarization occurring both with and without international connections. To estimate this missing counterfactual, we employ a new statistical method based on Bayesian item-response theory that permits us to disaggregate polarization after the Arab Uprisings into domestic and transnational components. We collected a dataset of Twitter accounts in Egypt and Tunisia during the critical year of 2013, when the Egyptian military overthrew the Islamist President Mohamed Morsi. We find that the coup increased retweets among Egyptian ideological allies by 50 percent each day following the coup and decreased cross-ideological retweets by 25 percent. Tunisian Twitter communities also showed stronger intra-group retweeting although at lower levels than in Egypt. Counter-intuitively, our model shows that the additional polarization in Tunisia after the coup appears to have dampened further polarization among Islamists in Egypt.

Experimental philosophy (x-phi) is a young field of research in the intersection of philosophy and psychology. It aims to make progress on philosophical questions by using experimental methods traditionally associated with the psychological and behavioral sciences, such as null hypothesis significance testing (NHST). Motivated by recent discussions about a methodological crisis in the behavioral sciences, questions have been raised about the methodological standards of x-phi. Here, we focus on one aspect of this question, namely the rate of inconsistencies in statistical reporting. Previous research has examined the extent to which published articles in psychology and other behavioral sciences present statistical inconsistencies in reporting the results of NHST. In this study, we used the R package statcheck to detect statistical inconsistencies in x-phi, and compared rates of inconsistencies in psychology and philosophy. We found that rates of inconsistencies in x-phi are lower than in the psychological and behavioral sciences. From the point of view of statistical reporting consistency, x-phi seems to do no worse, and perhaps even better, than psychological science.

This paper introduces a statistical technique known as “posterior passing” in which the results of past studies can be used to inform the analyses carried out by subsequent studies. We first describe the technique in detail and show how it can be implemented by individual researchers on an experiment by experiment basis. We then use a simulation to explore its success in identifying true parameter values compared to current statistical norms (ANOVAs and GLMMs). We find that posterior passing allows the true effect in the population to be found with greater accuracy and consistency than the other analysis types considered. Furthermore, posterior passing performs almost identically to a data analysis in which all data from all simulated studies are combined and analysed as one dataset. On this basis, we suggest that posterior passing is a viable means of implementing cumulative science. Furthermore, because it prevents the accumulation of large bodies of conflicting literature, it alleviates the need for traditional meta-analyses. Instead, posterior passing cumulatively and collaboratively provides clarity in real time as each new study is produced and is thus a strong candidate for a new, cumulative approach to scientific analyses and publishing.

Social influence plays an important role in human behavior and decisions. Sources of influence can be divided as external, which are independent of social context, or as originating from peers, such as family and friends. An important question is how to disentangle the social contagion by peers from external influences. While a variety of experimental and observational studies provided insight into this problem, identifying the extent of contagion based on large-scale observational data with an unknown network structure remains largely unexplored. By bridging the gap between the large-scale complex systems perspective of collective human dynamics and the detailed approach of social sciences, we present a parsimonious model of social influence, and apply it to a central topic in political science--elections and voting behavior. We provide an analytical expression of the county vote-share distribution, which is in excellent agreement with almost a century of observed U.S. presidential election data. Analyzing the social influence topography over this period reveals an abrupt phase transition from low to high levels of social contagion, and robust differences among regions. These results suggest that social contagion effects are becoming more instrumental in shaping large-scale collective political behavior, with implications on democratic electoral processes and policies.

Comparative political science has long worried about the performance of multilevel models when the number of upper-level units is small. Exacerbating these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that frequentist methods yield biased estimates and severely anti-conservative inference with small upper-level samples. Stegmueller recommends Bayesian techniques, which he claims to be superior in terms of both bias and inferential accuracy. In this paper, we reassess and refute these results. First, we formally prove that frequentist maximum likelihood estimators of coefficients are unbiased. The apparent bias found by Stegmueller is simply a manifestation of Monte Carlo Error. Second, we show how inferential problems can be overcome by using restricted maximum likelihood estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible without turning to Bayesian methods, even if the number of upper-level units is small.

Introduction: The use of psychometric tools such as tests or inventories comes with an agreement and acceptance that psychological characteristics, such as abilities, attitudes or personality traits, can be represented numerically and manipulated according to mathematical principles. Psychometrics and its close relation with statistics provides the scientific foundations and the standards that guide the development and use of psychological instruments, some of which are tests or inventories. This field has its own historic foundations and its particular analytical specificities and, while some are widely used analytical methods among psychologists and educational researchers, the history of psychometrics is either widely unknown or only partially known by these researchers or other students.Objectives: With that being said, this paper provides a succinct review of the history of psychometrics and its methods. From a theoretical approach, this study explores and describes the Classical Test Theory (CTT) and the Item Response Theory (IRT) frameworks and its models to deal with questions such as validity and reliability. Different aspects that gravitate around the field, in addition to recent developments are also discussed, including Goodness-of-Fit and Differential Item Functioning and Differential Test Functioning.Conclusions:This theoretical article helps to enhance the body of knowledge on psychometrics, it is especially addressed to social and educational researchers, and also contributes to training these scientists. To a lesser degree, the present article serves as a brief tutorial on the topic.