Cases and methods

2021 ◽  
pp. 43-71
Author(s):  
Emiliano Grossman ◽  
Isabelle Guinaudeau
Keyword(s):  
Fixed Effects ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Party Systems ◽  
Data Availability ◽  
Specific Structure ◽  
Far Right ◽  
Contemporary Politics ◽  
Binomial Regression ◽  
Legislative Agenda

This chapter presents the five cases. They are all wealthy Western European democracies. They feature quite a few similarities, concerning the evolution of turnout, electoral volatility, or the growing success of far-right parties. Yet, they also allow for some variation regarding their electoral systems, ranging from very majoritarian to very proportional and a resulting variety of party systems. Each country’s institutional setup and contemporary politics is briefly presented in turn. For each of the five countries, the chapter briefly discusses data availability and sources. For the five countries, we present the legislative agenda and the party agenda from the early 1980s to the late 2000s at least. Finally, we explain the choice of fixed-effects negative binomial regression, given the specific structure of the data and the goals of the study.

scholarly journals NATO between Exclusivity and Inclusivity: Measuring NATO’s Partnerships

2021 ◽  
pp. 1-32
Author(s):  
Branislav Mičko
Keyword(s):  
Human Rights ◽  
Fixed Effects ◽  
International Institutions ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Original Dataset ◽  
Strategic Approach ◽  
Binomial Regression ◽  
The Given ◽  
The Relationship

Building on an original dataset, this article focuses on the interactions between NATO and its declared worldwide partners. It argues that the analysis of these interactions can reveal NATO’s strategic approach to partnerships, but it can also provide a tool for its classification as an organisation that is either exclusive – defined by the focus on defence of its members, or inclusive – emphasising the global protection of democracies and human rights. The relationship between types of interactions and NATO categorisation is estimated using an unconditional negative binomial regression with fixed effects as well as a within-between (hybrid) model. Furthermore, they are illustrated on two brief case studies of Sweden and Japan. The results of the study suggest that NATO engages primarily with countries that are powerful relative to their neighbourhood, even though they are not the most powerful among the partners. The given country’s level of democracy, integration into the international institutions, and stability, do not seem to play any overarching role here.

scholarly journals Retracted: Analysis of the Strength of Legal Firearms Restrictions for Perpetrators of Domestic Violence and Their Associations With Intimate Partner Homicide

2017 ◽  
Vol 187 (7) ◽  
pp. 1449-1455 ◽  
Author(s):  
April M Zeoli ◽  
Alexander McCourt ◽  
Shani Buggs ◽  
Shannon Frattaroli ◽  
David Lilley ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Fixed Effects ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Intimate Partner ◽  
Restraining Order ◽  
Level Data ◽  
Binomial Regression ◽  
Firearms Restrictions

Abstract In this research, we estimate the association of firearm restrictions for domestic violence offenders with intimate partner homicides (IPHs) on the basis of the strength of the policies. We posit that the association of firearm laws with IPHs depends on the following characteristics of the laws: 1) breadth of coverage of high-risk individuals and situations restricted; 2) power to compel firearm surrender or removal from persons prohibited from having firearms; and 3) systems of accountability that prevent those prohibited from doing so from obtaining guns. We conducted a quantitative policy evaluation using annual state-level data from 1980 through 2013 for 45 US states. Based on the results of a series of robust, negative binomial regression models with state fixed effects, domestic violence restraining order firearm-prohibition laws are associated with 10% reductions in IPH. Statistically significant protective associations were evident only when restraining order prohibitions covered dating partners (−11%) and ex parte orders (−12%). Laws prohibiting access to those convicted of nonspecific violent misdemeanors were associated with a 24% reduction in IPH rates; there was no association when prohibitions were limited to domestic violence. Permit-to-purchase laws were associated with 10% reductions in IPHs. These findings should inform policymakers considering laws to maximize protections against IPH.

scholarly journals 7. Fixed-Effects Negative Binomial Regression Models

2002 ◽  
Vol 32 (1) ◽  
pp. 247-265 ◽  
Author(s):  
Paul D. Allison ◽  
Richard P. Waterman
Keyword(s):  
Fixed Effects ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Alternative Methods ◽  
Incidental Parameters ◽  
Binomial Regression ◽  
Downward Bias ◽  
Over Dispersion ◽  
Poisson Estimator ◽  
Deviance Statistic

This paper demonstrates that the conditional negative binomial model for panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true fixed-effects method. This method—which has been implemented in both Stata and LIMDEP—does not in fact control for all stable covariates. Three alternative methods are explored. A negative multinomial model yields the same estimator as the conditional Poisson estimator and hence does not provide any additional leverage for dealing with over-dispersion. On the other hand, a simulation study yields good results from applying an unconditional negative binomial regression estimator with dummy variables to represent the fixed effects. There is no evidence for any incidental parameters bias in the coefficients, and downward bias in the standard error estimates can be easily and effectively corrected using the deviance statistic. Finally, an approximate conditional method is found to perform at about the same level as the unconditional estimator.

scholarly journals Foreign Aid Allocation from a Network Perspective: The Effect of Global Ties

2016 ◽  
Author(s):  
Liam Swiss
Keyword(s):  
Foreign Aid ◽  
Fixed Effects ◽  
Negative Binomial ◽  
Recipient Country ◽  
Negative Binomial Regression ◽  
World Society ◽  
Aid Allocation ◽  
Bilateral Aid ◽  
New Approach ◽  
Binomial Regression

This article examines competing explanations for foreign aid allocation on the global level and argues for a new approach to understanding aid from an institutionalist perspective. Using network data on all official bilateral aid relationships between countries in the period from 1975 through 2006 and data on recipient country ties to world society, the article offers an alternative explanation for the allocation of global foreign aid. Fixed effects negative binomial regression models on a panel sample of 117 developing countries reveal that global ties to world society in the form of non-governmental memberships and treaty ratifications are strong determinants of the network centrality of recipient countries in the global foreign aid network. Countries with a higher level of adherence and connection to world society norms and organizations are shown to be the beneficiaries of an increased number of aid relationships with wealthy donor countries. The findings also suggest that prior explanations of aid allocation grounded in altruist or realist motivations are insufficient to account for the patterns of aid allocation seen globally in recent years.

Homeland Violence and Diaspora Insecurity: An Analysis of Israel and American Jewry

2019 ◽  
Vol 13 (1) ◽  
pp. 1-27
Author(s):  
Ayal K. Feinberg
Keyword(s):  
United States ◽  
Fixed Effects ◽  
Hate Crime ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Hate Crimes ◽  
Federal Bureau Of Investigation ◽  
American Jewry ◽  
Binomial Regression

AbstractJews and Jewish institutions have suffered the majority of reported religion-motivated hate crimes in the United States for nearly two decades. According to the Federal Bureau of Investigation (FBI), in 2014 the 609 reported anti-Semitic incidents made up 59% of all religious bias hate crimes alone. Rates of reported anti-Semitic hate crimes vary considerably over the course of a year. Yet, little scholarly attention has been given to what factors cause reported anti-Semitic hate crimes to fluctuate so substantially in the United States. This paper hypothesizes that violent Israeli military engagements are critical in explaining weekly surges of reported anti-Semitic hate crimes. Utilizing FBI hate crime data from 2001 to 2014 and fixed effects negative binomial regression models, consistent findings underscore that violent Israeli military engagements significantly increase the likelihood of a state reporting anti-Semitic hate crime. Most dramatically, their occurrence increases the likelihood of reported hate crime intimidating individuals or characterized as violent by nearly 35%. This paper underscores that homeland perpetrated violence can directly impact the security of diaspora communities.

scholarly journals Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths

2021 ◽  
pp. jech-2020-215039 ◽  
Author(s):  
Anders Malthe Bach-Mortensen ◽  
Michelle Degli Esposti
Keyword(s):  
Negative Binomial ◽  
Care Home ◽  
Negative Binomial Regression ◽  
Population Characteristics ◽  
Care Homes ◽  
Future Research ◽  
Area Deprivation ◽  
Binomial Regression ◽  
Index Of Multiple Deprivation

IntroductionThe COVID-19 pandemic has disproportionately impacted care homes and vulnerable populations, exacerbating existing health inequalities. However, the role of area deprivation in shaping the impacts of COVID-19 in care homes is poorly understood. We examine whether area deprivation is linked to higher rates of COVID-19 outbreaks and deaths among care home residents across upper tier local authorities in England (n=149).MethodsWe constructed a novel dataset from publicly available data. Using negative binomial regression models, we analysed the associations between area deprivation (Income Deprivation Affecting Older People Index (IDAOPI) and Index of Multiple Deprivation (IMD) extent) as the exposure and COVID-19 outbreaks, COVID-19-related deaths and all-cause deaths among care home residents as three separate outcomes—adjusting for population characteristics (size, age composition, ethnicity).ResultsCOVID-19 outbreaks in care homes did not vary by area deprivation. However, COVID-19-related deaths were more common in the most deprived quartiles of IDAOPI (incidence rate ratio (IRR): 1.23, 95% CI 1.04 to 1.47) and IMD extent (IRR: 1.16, 95% CI 1.00 to 1.34), compared with the least deprived quartiles.DiscussionThese findings suggest that area deprivation is a key risk factor in COVID-19 deaths among care home residents. Future research should look to replicate these results when more complete data become available.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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