scholarly journals Does Self-Assessed Health Reflect the True Health State?

2021 ◽  
Vol 18 (21) ◽  
pp. 11153
Author(s):  
Pavitra PAUL ◽  
Ulrich NGUEMDJO ◽  
Natalia KOVTUN ◽  
Bruno VENTELOU
Keyword(s):  
Population Health ◽  
Policy Development ◽  
Social Science Research ◽  
Probit Model ◽  
Science Research ◽  
Perceived Health ◽  
Health State ◽  
Repeated Response ◽  
Using Data ◽  
Self Assessed Health

Self-assessed health (SAH) is a widely used tool to estimate population health. However, the debate continues as to what exactly this ubiquitous measure of social science research means for policy conclusions. This study is aimed at understanding the tenability of the construct of SAH by simultaneously modelling SAH and clinical morbidity. Using data from 17 waves (2001–2017) of the Russian Longitudinal Monitoring Survey, which captures repeated response for SAH and frequently updates information on clinical morbidity, we operationalise a recursive semi-ordered probit model. Our approach allows for the estimation of the distributional effect of clinical morbidity on perceived health. This study establishes the superiority of inferences from the recursive model. We illustrated the model use for examining the endogeneity problem of perceived health for SAH, contributing to population health research and public policy development, in particular, towards the organisation of health systems.

scholarly journals Causality and Pleiotropy in the Association Between Bullying Victimization in Adolescence and Depressive Episodes in Adulthood

2018 ◽  
Vol 21 (1) ◽  
pp. 33-41 ◽  
Author(s):  
Tina Kretschmer ◽  
Felix C. Tropf ◽  
Nynke M. D. Niezink
Keyword(s):  
Causal Effect ◽  
Social Science Research ◽  
Science Research ◽  
Cholesky Decomposition ◽  
Elevated Risk ◽  
Bullying Victimization ◽  
Long Run ◽  
Using Data ◽  
Depressive Episodes ◽  
Shared Genetic

Children and adolescents who are victims or perpetrators of bullying victimization are at elevated risk for maladjustment problems, concurrently and in the long run. Previous studies suggest that this correlation is partly explained by genetic influence. However, whether the genetic correlation is independent of a causal effect of victimization on maladjustment remains unclear. Using data from 2,510 females from the TwinsUK registry, we applied an innovative extension of the Cholesky decomposition to investigate to what extent the association between victimization in adolescence and self-reported depressive episodes in adulthood is caused by shared genetic effects (pleiotropy), and to what extent it is due to a phenotypic causal relationship. We find that around 60% of the association between victimization and self-reported depressive episodes is due to a causal effect of victimization on depressive episodes, and 40% is due to pleiotropic effects. These findings underline the importance of integrating genetic information into social science research and demonstrate a neat strategy to elucidate causal mechanisms in the absence of experimental designs.

scholarly journals Predicting Layoff among Fragile Families

2019 ◽  
Vol 5 ◽  
pp. 237802311880975 ◽  
Author(s):  
Caitlin E. Ahearn ◽  
Jennie E. Brand
Keyword(s):  
Data Science ◽  
Prediction Models ◽  
Social Process ◽  
Social Science Research ◽  
Science Research ◽  
Fragile Families ◽  
Psychological Consequences ◽  
Exogenous Shock ◽  
Economic Role ◽  
Using Data

The loss of a job is the loss of a major social and economic role and is associated with long-term negative economic and psychological consequences for workers and families. Modeling the causal effects of a social process like layoff with observational data depends crucially on the degree to which the model accounts for the characteristics that predict loss. We report analyses predicting layoff in the Fragile Families data as part of the Fragile Families Challenge. Our model, grounded in empirical social science research on layoff, did not perform substantially worse than the best-performing model using data science techniques. This result is not fully unforeseen, given that layoff functions as a relatively exogenous shock. Future work using the results of the Challenge should attend to whether small improvements in prediction models, like those we observe across models of layoff, nevertheless significantly increase the validity of subsequent models for causal inference.

A social science data-fusion tool and the Data Management through e-Social Science (DAMES) infrastructure

2010 ◽  
Vol 368 (1925) ◽  
pp. 3859-3873 ◽  
Author(s):  
Guy C. Warner ◽  
Jesse M. Blum ◽  
Simon B. Jones ◽  
Paul S. Lambert ◽  
Kenneth J. Turner ◽  
...  
Keyword(s):  
Social Science ◽  
Data Fusion ◽  
Data Management ◽  
Social Science Research ◽  
Science Research ◽  
Science Data ◽  
Project Approach ◽  
Computing Power ◽  
Social Science Data ◽  
Using Data

The last two decades have seen substantially increased potential for quantitative social science research. This has been made possible by the significant expansion of publicly available social science datasets, the development of new analytical methodologies, such as microsimulation, and increases in computing power. These rich resources do, however, bring with them substantial challenges associated with organizing and using data. These processes are often referred to as ‘data management’. The Data Management through e-Social Science (DAMES) project is working to support activities of data management for social science research. This paper describes the DAMES infrastructure, focusing on the data-fusion process that is central to the project approach. It covers: the background and requirements for provision of resources by DAMES; the use of grid technologies to provide easy-to-use tools and user front-ends for several common social science data-management tasks such as data fusion; the approach taken to solve problems related to data resources and metadata relevant to social science applications; and the implementation of the architecture that has been designed to achieve this infrastructure.

scholarly journals The Role of Individual Officer Characteristics in Police Shootings

2020 ◽  
Vol 687 (1) ◽  
pp. 58-66 ◽  
Author(s):  
Greg Ridgeway
Keyword(s):  
New York ◽  
New York City ◽  
Environmental Factors ◽  
Police Officers ◽  
Social Science Research ◽  
Police Department ◽  
Science Research ◽  
Individual Characteristics ◽  
Using Data

Assessing whether individual characteristics of police officers such as age, race, and prior performance influence police behavior has been a long-standing topic of social science research. The effect of officer characteristics on their risk of shooting people is confounded by police assignments and by the environmental factors associated with those assignments. This article provides a method to separate out the influence of individual officer characteristics from environmental factors. Using data from the New York City Police Department (NYPD) and the Major Cities’ Chiefs Association (MCCA), the analysis finds that police officers who join the NYPD later in their careers have a lower shooting risk: for each additional year of their recruitment age, the odds of being shooters declines by 10 percent. Both officer race and prior problem behavior (e.g., losing a firearm, crashing a department vehicle) predict up to three times greater odds of shooting, yet officers who made numerous misdemeanor arrests were four times less likely to shoot.

Diplomatic Relations in a Virtual World

2021 ◽  
pp. 1-22
Author(s):  
Aaron Bramson ◽  
Kevin Hoefman ◽  
Koen Schoors ◽  
Jan Ryckebusch
Keyword(s):  
Virtual Worlds ◽  
Virtual World ◽  
Explanatory Power ◽  
Social Science Research ◽  
Strong Support ◽  
Science Research ◽  
Balance Theory ◽  
Structural Balance ◽  
Political Alliances ◽  
Using Data

Abstract We apply variations and extensions of structural balance theory to analyze the dynamics of geopolitical relations using data from the virtual world Eve Online. The highly detailed data enable us to study the interplay of alliance size, power, and geographic proximity on the prevalence and conditional behavior of triads built from empirical political alliances. Through our analysis, we reveal the degree to which the behaviors of players conform to the predictions of structural balance theory and whether our augmentations of the theory improve these predictions. In addition to studying the time series of the proportions of triad types, we investigate the conditional changes in triad types and the formation of polarized political coalitions. We find that player behavior largely conforms to the predictions of a multipolar version of structural balance theory that separates strong and weak configurations of balanced and frustrated triads. The high degree of explanatory power of structural balance theory in this context provides strong support for both the theory and the use of virtual worlds in social science research.

Race, Socioeconomic Status, and the Subjective Well-Being of Older Americans

1987 ◽  
Vol 25 (1) ◽  
pp. 43-61 ◽  
Author(s):  
Brad McKenzie ◽  
James Campbell
Keyword(s):  
Life Satisfaction ◽  
Health Status ◽  
Policy Development ◽  
Well Being ◽  
Two Dimensions ◽  
Subjective Well Being ◽  
Older Americans ◽  
Using Data ◽  
Self Assessed Health ◽  
Intervening Variables

Causal examination of factors influencing life satisfaction among older Americans can provide knowledge important to social policy development. Using rotated factor analysis, this study isolates two dimensions of life satisfaction, labeled happiness and morale, using data from the 1981 Harris survey on aging. Race, SES characteristics, and the two intervening variables of self-assessed health status and problems experienced are tested through path analysis on the two attributes of life satisfaction. Most of the effects of race and SES are mediated by self-assessed health status and problems experienced, and these two intervening variables are the strongest direct predictors of happiness and morale. Of particular significance are results which demonstrate that racial background has a strong influence on problems experienced, and that education is more influential than income on the life satisfaction factors tested in this study.

scholarly journals Enhancing Rural Population Health Care Access and Outcomes Through the Telehealth EcoSystem™ Model

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Brenda Leath ◽  
Lucenia W. Dunn ◽  
Antwon Alsobrook ◽  
Madeline L Darden
Keyword(s):  
Population Health ◽  
Health Care Access ◽  
Health Informatics ◽  
Social Science Research ◽  
Science Research ◽  
Infrastructure Development ◽  
Policy Reforms ◽  
Public Health Services ◽  
Patient Portals ◽  
Public And Private

Abstract The article highlights the Telehealth Ecosystem™ model, a holistic cross sector approach for socioeconomic revitalization, connectivity, interoperability and technology infrastructure development to address health equity for rural underserved communities. Two guiding frameworks, Community & Economic Development (CED) and Collective Impact, provided the foundation for the Telehealth Ecosystem™ model. Public and private organizational capacities are addressed by comprehensive healthcare and social service delivery through stakeholder engagement and collaborative decision-making processes. A focus is maintained on economic recovery and policy reforms that enhance population health outcomes for individuals and families who have economic challenges. The Telehealth EcoSystem™ utilizes an Intranet mechanism that enables a range of technologies and electronic devices for health informatics and telemedicine initiatives. The relevance of the Intranet to the advancement of health informatics is highlighted. Best practices in digital connectivity, HIPPA requirements, EHRs, and eHealth applications, such as patient portals and mobile devices are emphasized. Collateral considerations include technology applications that expand public health services. The ongoing collaboration between a social science research corporation, a regional community foundation and an open access telecommunications carrier is a pivotal element in the sequential development and implementation of the Telehealth EcoSystem™ model in the rural southeastern region community.  

scholarly journals Attendance, Completion, and Heterogeneous Returns to College

2021 ◽  
Author(s):  
Xiang Zhou
Keyword(s):  
Social Science Research ◽  
College Education ◽  
Science Research ◽  
College Attendance ◽  
Total Effect ◽  
Socioeconomic Background ◽  
Socioeconomically Disadvantaged ◽  
Sequential Approach ◽  
Analytical Strategies ◽  
Using Data

A growing body of social science research has investigated whether the economic payoff to a college education is heterogeneous — in particular, whether socioeconomically disadvantaged youth can benefit more from attending and completing college relative to their more advantaged peers. Scholars, however, have employed different analytical strategies and reported mixed findings. To shed light on this literature, I propose a sequential approach to conceptualizing, evaluating, and unpacking the causal effects of college on earnings. By decomposing the total effect of attending a four-year college into several direct and indirect components, this approach not only clarifies the mechanisms through which college attendance boosts earnings, but illuminates the ways in which the postsecondary system may be both an equalizer and a disequalizer. The total effect of college attendance, its direct and indirect components, and their heterogeneity by socioeconomic background are all identified under the assumption of sequential ignorability. I introduce a debiased machine learning (DML) method for estimating all quantities of interest, along with a set of bias formulas for sensitivity analysis. I illustrate the proposed framework and methodology using data from the National Longitudinal Survey of Youth, 1997 cohort.

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