scholarly journals Behavior under Extreme Conditions: The Titanic Disaster

2011 ◽  
Vol 25 (1) ◽  
pp. 209-222 ◽  
Author(s):  
Bruno S Frey ◽  
David A Savage ◽  
Benno Torgler
Keyword(s):  
Extreme Conditions ◽  
Social Scientists ◽  
Physical Strength ◽  
Individual Level ◽  
Life And Death ◽  
Probability Of Survival ◽  
The People ◽  
Level Data ◽  
Children First ◽  
Other Regarding

During the night of April 14, 1912, the RMS Titanic collided with an iceberg on her maiden voyage. Two hours and 40 minutes later she sank, resulting in the loss of 1,501 lives—more than two-thirds of her 2,207 passengers and crew. This remains one of the deadliest peacetime maritime disasters in history and by far the most famous. For social scientists, evidence about how people behaved as the Titanic sunk offers a quasi-natural field experiment to explore behavior under extreme conditions of life and death. A common assumption is that in such situations, self-interested reactions will predominate and social cohesion is expected to disappear. However, empirical evidence on the extent to which people in the throes of a disaster react with self-regarding or with other-regarding behavior is scanty. The sinking of the Titanic posed a life-or-death situation for its passengers. The Titanic carried only 20 lifeboats, which could accommodate about half the people aboard, and deck officers exacerbated the shortage by launching lifeboats that were partially empty. Failure to secure a seat in a lifeboat virtually guaranteed death. We have collected individual-level data on the passengers and crew on the Titanic, which allow us to analyze some specific questions: Did physical strength (being male and in prime age) or social status (being a first- or second-class passenger) raise the survival chance? Was it favorable for survival to travel alone or in company? Does one's role or function (being a crew member or a passenger) affect the probability of survival? Do social norms, such as “Women and children first!” have any effect? Does nationality affect the chance of survival? We also explore whether the time from impact to sinking might matter by comparing the sinking of the Titanic over nearly three hours to the sinking of the Lusitania in 1915, which took only 18 minutes from when the torpedo hit the ship.

scholarly journals Income disparities in life expectancy in the City of Toronto and Region of Peel, Ontario

2012 ◽  
Vol 32 (4) ◽  
pp. 208-215 ◽  
Author(s):  
J. Stratton ◽  
D.L. Mowat ◽  
R. Wilkins ◽  
M. Tjepkema
Keyword(s):  
Life Expectancy ◽  
Immigrant Status ◽  
Individual Level ◽  
Probability Of Survival ◽  
Income Disparities ◽  
Income Adequacy ◽  
Level Data ◽  
Calculated Period ◽  
The City

Introduction To understand the lack of a gradient in mortality by neighbourhood income in a previous study, we used individual-level data from the 1991–2001 Canadian census mortality follow-up study to examine income-related disparities in life expectancy and probability of survival to age 75 years in the City of Toronto and Region of Peel. Methods We calculated period life tables for each sex and income adequacy quintile, overall and separately for immigrants and non-immigrants. Results For all cohort members of both sexes, including both immigrants and non-immigrants, there was a clear gradient across the income quintiles, with higher life expectancy in each successively richer quintile. However, the disparities by income were much greater when the analysis was restricted to non-immigrants. The lesser gradient for immigrants appeared to reflect the higher proportion of recent immigrants in the lower income quintiles. Conclusion These findings highlight the importance of using individual-level ascertainment of income whenever possible, and of including immigrant status and period of immigration in assessments of health outcomes, especially for areas with a high proportion of immigrants.

scholarly journals Introduction

2021 ◽  
pp. 1-9
Author(s):  
Guillaume Marois ◽  
Samir KC
Keyword(s):  
Social Sciences ◽  
Good Option ◽  
Population Projections ◽  
Social Scientists ◽  
Statistical Software ◽  
Individual Level ◽  
Random Experiment ◽  
Level Data ◽  
Microsimulation Models

AbstractThis chapter introduces the purpose of the book. When a researcher needs to perform microsimulation for population projections, building its own model with a common statistical software such as SAS might a good option, because this software is widely used among scholars and is taught in most social sciences departments. We define what is microsimulation: a modelling based on individual-level data rather than aggregated level data, in which transitions between the states are determined stochastically with a random experiment. We finally provide some examples of microsimulation models used by social scientists.

scholarly journals Models of Other-Regarding Preferences, Inequality, and Redistribution

2018 ◽  
Vol 21 (1) ◽  
pp. 441-460 ◽  
Author(s):  
Matthew Dimick ◽  
David Rueda ◽  
Daniel Stegmueller
Keyword(s):  
European Social Survey ◽  
Social Survey ◽  
Individual Level ◽  
Theoretical Approaches ◽  
European Data ◽  
Level Data ◽  
Western European ◽  
Other Regarding ◽  
Other Regarding Preferences

Despite the increasing popularity of comparative work on other-regarding preferences, the implications of different models of altruism are not always fully understood. This article analyzes different theoretical approaches to altruism and explores what empirical conclusions we should draw from them, paying particular attention to models of redistribution preferences where inequality explicitly triggers other-regarding motives for redistribution. While the main contribution of this article is to clarify the conclusions of these models, we also illustrate the importance of their distinct implications by analyzing Western European data to compare among them. We draw on individual-level data from the European Social Survey fielded between September 2002 and December 2013.

scholarly journals Using Electromagnetic Signal Propagation Models for Radio and Television Broadcasts: An Introduction

2018 ◽  
Vol 26 (3) ◽  
pp. 348-355 ◽  
Author(s):  
Charles Crabtree ◽  
Holger L. Kern
Keyword(s):  
Media Exposure ◽  
Signal Strength ◽  
Signal Propagation ◽  
Electromagnetic Signal ◽  
Social Scientists ◽  
Propagation Models ◽  
Radio Stations ◽  
Individual Level ◽  
Level Data ◽  
Television Signal

This note offers an introduction to electromagnetic signal propagation models, which can be used to model terrestrial radio and television signal strength across space. Such data are useful to social scientists interested in identifying the effects of mass media broadcasts when (i) individual-level data on media exposure do not exist or when (ii) media exposure, while observed, is not exogenous. We illustrate the use of electromagnetic signal propagation models by creating a signal strength measure of military-controlled radio stations during the 2012 coup in Mali.

scholarly journals Alternatives to Social Science One

2020 ◽  
Vol 53 (4) ◽  
pp. 710-711
Author(s):  
Margaret Levi ◽  
Betsy Rajala
Keyword(s):  
Social Science ◽  
Administrative Data ◽  
Third Party ◽  
Social Scientists ◽  
Social Good ◽  
Individual Level ◽  
Level Data ◽  
Highly Sensitive ◽  
Research Facilities ◽  
Sensitive Individual

ABSTRACTThis article responds to King and Persily’s (2019) proposal for a new model of industry–academic partnership using an independent third party to mediate between firms and academics. We believe this is a reasonable proposal for highly sensitive individual-level data, but it may not be appropriate for all types of data. We explore alternative options to their proposal, including Administrative Data Research Facilities, Data Collaboratives at GovLab, and Tech Data for Social Good Initiative at the Center for Advanced Study in the Behavioral Sciences. We believe social scientists should continue to explore, evaluate, and scale a variety of industry–academic data-sharing models.

scholarly journals Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk

2021 ◽  
Vol 18 (14) ◽  
pp. 7561
Author(s):  
Jingjing Wang ◽  
Xueying Wu ◽  
Ruoyu Wang ◽  
Dongsheng He ◽  
Dongying Li ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Built Environment ◽  
Longitudinal Research ◽  
Empirical Studies ◽  
Infection Risk ◽  
Design Guidelines ◽  
Individual Level ◽  
Transit Accessibility ◽  
Level Data ◽  
Transmission Pathways

The coronavirus disease 2019 pandemic has stimulated intensive research interest in its transmission pathways and infection factors, e.g., socioeconomic and demographic characteristics, climatology, baseline health conditions or pre-existing diseases, and government policies. Meanwhile, some empirical studies suggested that built environment attributes may be associated with the transmission mechanism and infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, no review has been conducted to explore the effect of built environment characteristics on the infection risk. This research gap prevents government officials and urban planners from creating effective urban design guidelines to contain SARS-CoV-2 infections and face future pandemic challenges. This review summarizes evidence from 25 empirical studies and provides an overview of the effect of built environment on SARS-CoV-2 infection risk. Virus infection risk was positively associated with the density of commercial facilities, roads, and schools and with public transit accessibility, whereas it was negatively associated with the availability of green spaces. This review recommends several directions for future studies, namely using longitudinal research design and individual-level data, considering multilevel factors and extending to diversified geographic areas.

scholarly journals The Role of Snack Choices, Body Weight Stereotypes and Smoking Behavior in Assessing Risk Factors for Adolescent Overweight and Obesity

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 557
Author(s):  
Elena Raptou
Keyword(s):  
Body Weight ◽  
Smoking Behavior ◽  
Negative Influence ◽  
Overweight And Obesity ◽  
Individual Level ◽  
Level Data ◽  
Prevention Interventions ◽  
Frequent Consumption ◽  
Adolescent Overweight ◽  
Relationship Of

This study investigated the relationship of behavioral factors, such as snack choices, obesity stereotypes and smoking with adolescents’ body weight. Individual-level data for 1254 Greek youths were selected via a formal questionnaire. Snack choices seem to be gender specific with girls showing a stronger preference for healthier snacks. Frequent consumption of high-calorie and more filling snacks was found to increase Body Mass Index (BMI) in both genders. Fruit/vegetable snacks were associated with lower body weight in females, whereas cereal/nut snacks had a negative influence in males’ BMI. The majority of participants expressed anti-fat attitudes and more boys than girls assigned positive attributes to lean peers. The endorsement of the thin-ideal was positively associated with the BMI of both adolescent boys and girls. This study also revealed that neglecting potential endogeneity issues can lead to biased estimates of smoking. Gender may be a crucial moderator of smoking–BMI relationships. Male smokers presented a higher obesity risk, whereas female smokers were more likely to be underweight. Nutrition professionals should pay attention to increase the acceptance of healthy snack options. Gender differences in the influence of weight stereotypes and smoking on BMI should be considered in order to enhance the efficacy of obesity prevention interventions.

When Marriage Gets Hard: Intra-Coalition Conflict and Electoral Accountability

2021 ◽  
pp. 001041402110243
Author(s):  
Carolina Plescia ◽  
Sylvia Kritzinger
Keyword(s):  
Economic Performance ◽  
Political Representation ◽  
Economic Evaluations ◽  
Responsibility Attribution ◽  
Electoral Accountability ◽  
Individual Level ◽  
Coalition Governments ◽  
Level Data ◽  
European Election ◽  
Election Studies

Combining individual-level with event-level data across 25 European countries and three sets of European Election Studies, this study examines the effect of conflict between parties in coalition government on electoral accountability and responsibility attribution. We find that conflict increases punishment for poor economic performance precisely because it helps clarify to voters parties’ actions and responsibilities while in office. The results indicate that under conditions of conflict, the punishment is equal for all coalition partners when they share responsibility for poor economic performance. When there is no conflict within a government, the effect of poor economic evaluations on vote choice is rather low, with slightly more punishment targeted to the prime minister’s party. These findings have important implications for our understanding of electoral accountability and political representation in coalition governments.

Missing Data in Research on Youth and Family Programs

2021 ◽  
pp. 003329412110268
Author(s):  
Jaime Ballard ◽  
Adeya Richmond ◽  
Suzanne van den Hoogenhof ◽  
Lynne Borden ◽  
Daniel Francis Perkins
Keyword(s):  
Missing Data ◽  
Statistical Methods ◽  
Self Report ◽  
Program Participation ◽  
Multilevel Data ◽  
Individual Level ◽  
Level Data ◽  
Before And After ◽  
Post Test ◽  
The Individual

Background Multilevel data can be missing at the individual level or at a nested level, such as family, classroom, or program site. Increased knowledge of higher-level missing data is necessary to develop evaluation design and statistical methods to address it. Methods Participants included 9,514 individuals participating in 47 youth and family programs nationwide who completed multiple self-report measures before and after program participation. Data were marked as missing or not missing at the item, scale, and wave levels for both individuals and program sites. Results Site-level missing data represented a substantial portion of missing data, ranging from 0–46% of missing data at pre-test and 35–71% of missing data at post-test. Youth were the most likely to be missing data, although site-level data did not differ by the age of participants served. In this dataset youth had the most surveys to complete, so their missing data could be due to survey fatigue. Conclusions Much of the missing data for individuals can be explained by the site not administering those questions or scales. These results suggest a need for statistical methods that account for site-level missing data, and for research design methods to reduce the prevalence of site-level missing data or reduce its impact. Researchers can generate buy-in with sites during the community collaboration stage, assessing problematic items for revision or removal and need for ongoing site support, particularly at post-test. We recommend that researchers conducting multilevel data report the amount and mechanism of missing data at each level.

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