Measuring Threat and Violence

2018 ◽  
pp. 43-56
Author(s):  
Jeffrey S. Kopstein ◽  
Jason Wittenberg
Keyword(s):  
Statistical Models ◽  
Census Data ◽  
Location Information ◽  
Ecological Inference ◽  
Multivariate Statistical ◽  
Original Dataset ◽  
Geographical Unit ◽  
Nationalist Parties ◽  
Electoral Data

This chapter describes our data and methods. Our analysis is based on an original dataset of census returns, electoral results, and pogrom location information. We gathered these data at the lowest geographical unit for which they could be merged, yielding observations for over 2,000 localities. We use census data on religion and electoral data on support for Jewish and non-Jewish nationalist parties to measure the degree of perceived political threat prior to the outbreak of war. We establish the characteristics of those localities where pogroms occurred using a variety of methods, including multivariate statistical models and ecological inference.

Another Game with Nature: An Ecological Regression Model of the British Two-Party Vote Ratio in 1970

1976 ◽  
Vol 6 (1) ◽  
pp. 43-81 ◽  
Author(s):  
Ivor Crewe ◽  
Clive Payne
Keyword(s):  
Predictive Models ◽  
Census Data ◽  
Statistical Techniques ◽  
Aggregate Level ◽  
Ecological Inference ◽  
Multivariate Statistical ◽  
Ecological Regression ◽  
Level Data ◽  
Electoral Behaviour ◽  
First Time

This article develops a number of themes first raised in an earlier paper where we attempted to publicize the existence of Census data based, for the first time, on British parliamentary constituencies, and where we briefly described the potential and limits of a variety of available statistical techniques of analysis. Until the earlier paper was published, studies of British electoral behaviour using aggregate data were largely historical, generally used only the simplest statistical techniques such as cross-tabulations, and usually proceeded blithely unaware of the snares of ecological inference. A small number of more advanced analyses had appeared but none focused on Britain or even on England as a whole. Since our earlier article appeared, there have been two attempts to construct predictive models of Labour support by applying multivariate statistical analysis to aggregate-level data. As we show in this paper, both Barnett and Rasmussen produce models that are statistically less powerful than our own and are subject to various weaknesses, of which the most important is the failure to tackle the problem of ecological inference.

Engagement Through the Diaspora Channel

2017 ◽  
Author(s):  
Michael S. Danielson
Keyword(s):  
Civil Society ◽  
Statistical Models ◽  
State Level ◽  
The State ◽  
Multivariate Statistical ◽  
Political Factors ◽  
Data Set ◽  
Level Data ◽  
Municipal Level

The first empirical task is to identify the characteristics of municipalities which US-based migrants have come together to support financially. Using a nationwide, municipal-level data set compiled by the author, the chapter estimates several multivariate statistical models to compare municipalities that did not benefit from the 3x1 Program for Migrants with those that did, and seeks to explain variation in the number and value of 3x1 projects. The analysis shows that migrants are more likely to contribute where migrant civil society has become more deeply institutionalized at the state level and in places with longer histories as migrant-sending places. Furthermore, the results suggest that political factors are at play, as projects have disproportionately benefited states and municipalities where the PAN had a stronger presence, with fewer occurring elsewhere.

scholarly journals Dynamic model updating (DMU) approach for statistical learning model building with missing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rahi Jain ◽  
Wei Xu
Keyword(s):  
Missing Data ◽  
Dynamic Model ◽  
Statistical Models ◽  
Missing Values ◽  
Model Building ◽  
Model Updating ◽  
Biological Data ◽  
Bayesian Regression ◽  
Biological Research ◽  
Original Dataset

Abstract Background Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models. Method and results This study proposes a dynamic model updating (DMU) approach, a different strategy to develop statistical models with missing data. DMU uses only the information available in the dataset to prepare the statistical models. DMU segments the original dataset into small complete datasets. The study uses hierarchical clustering to segment the original dataset into small complete datasets followed by Bayesian regression on each of the small complete datasets. Predictor estimates are updated using the posterior estimates from each dataset. The performance of DMU is evaluated by using both simulated data and real studies and show better results or at par with other approaches like CCA and PMM. Conclusion DMU approach provides an alternative to the existing approaches of information elimination and imputation in processing the datasets with missing values. While the study applied the approach for continuous cross-sectional data, the approach can be applied to longitudinal, categorical and time-to-event biological data.

DECISION MAKING BY VARIABLE CONTRIBUTION IN DISCRIMINANT, LOGIT, AND REGRESSION ANALYSES

2004 ◽  
Vol 03 (02) ◽  
pp. 265-279 ◽  
Author(s):  
STAN LIPOVETSKY ◽  
MICHAEL CONKLIN
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Statistical Models ◽  
Regression Analyses ◽  
Discriminant Functions ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Variable Contribution ◽  
Linear Aggregate

Comparative contribution of predictors in multivariate statistical models is widely used for decision making on the importance of the variables for the aims of analysis and prediction. However, the analysis can be made difficult because of the predictors' multicollinearity that distorts estimates for coefficients in the linear aggregate. To solve the problem of the robust evaluation of the predictors' contribution, we apply the Shapley Value regression analysis that provides consistent results in the presence of multicollinearity both for regression and discriminant functions. We also show how the linear discriminant function can be constructed as a multiple regression, and how the logistic regression can be approximated by linear regression that helps to obtain the variables contribution in the linear aggregate.

The Electoral Geography of Weimar Germany: Exploratory Spatial Data Analyses (ESDA) of Protestant Support for the Nazi Party

2002 ◽  
Vol 10 (3) ◽  
pp. 217-243 ◽  
Author(s):  
John O'Loughlin
Keyword(s):  
Spatial Data ◽  
Information Science ◽  
Census Data ◽  
Spatial Data Analysis ◽  
Nazi Party ◽  
Ecological Inference ◽  
Inference Procedure ◽  
Individual Level ◽  
Weimar Germany ◽  
Strong Attachment

For more than half a century, social scientists have probed the aggregate correlates of the vote for the Nazi party (NSDAP) in Weimar Germany. Since individual-level data are not available for this time period, aggregate census data for small geographic units have been heavily used to infer the support of the Nazi party by various compositional groups. Many of these studies hint at a complex geographic patterning. Recent developments in geographic methodologies, based on Geographic Information Science (GIS) and spatial statistics, allow a deeper probing of these regional and local contextual elements. In this paper, a suite of geographic methods—global and local measures of spatial autocorrelation, variography, distance-based correlation, directional spatial correlograms, vector mapping, and barrier definition (wombling)—are used in an exploratory spatial data analysis of the NSDAP vote. The support for the NSDAP by Protestant voters (estimated using King's ecological inference procedure) is the key correlate examined. The results from the various methods are consistent in showing a voting surface of great complexity, with many local clusters that differ from the regional trend. The Weimar German electoral map does not show much evidence of a nationalized electorate, but is better characterized as a mosaic of support for “milieu parties,” mixed across class and other social lines, and defined by a strong attachment to local traditions, beliefs, and practices.

scholarly journals A multiplicative process for generating a beta-like survival function with application to the UK 2016 EU referendum results

2017 ◽  
Vol 28 (11) ◽  
pp. 1750132 ◽  
Author(s):  
Trevor Fenner ◽  
Eric Kaufmann ◽  
Mark Levene ◽  
George Loizou
Keyword(s):  
Linear Models ◽  
Census Data ◽  
Extreme Values ◽  
Rank Order ◽  
Survival Function ◽  
Ecological Inference ◽  
Social Phenomena ◽  
Election Forecasting ◽  
Eu Referendum ◽  
The Uk

Human dynamics and sociophysics suggest statistical models that may explain and provide us with better insight into social phenomena. Contextual and selection effects tend to produce extreme values in the tails of rank-ordered distributions of both census data and district-level election outcomes. Models that account for this nonlinearity generally outperform linear models. Fitting nonlinear functions based on rank-ordering census and election data therefore improves the fit of aggregate voting models. This may help improve ecological inference, as well as election forecasting in majoritarian systems. We propose a generative multiplicative decrease model that gives rise to a rank-order distribution and facilitates the analysis of the recent UK EU referendum results. We supply empirical evidence that the beta-like survival function, which can be generated directly from our model, is a close fit to the referendum results, and also may have predictive value when covariate data are available.

Hate crime against older people in England and Wales – an econometric enquiry

2014 ◽  
Vol 16 (3) ◽  
pp. 152-165 ◽  
Author(s):  
José Iparraguirre
Keyword(s):  
Older People ◽  
Latent Variable ◽  
Hate Crime ◽  
Census Data ◽  
Criminal Activity ◽  
Econometric Analysis ◽  
Data Availability ◽  
England And Wales ◽  
Content Type ◽  
Geographical Unit

Purpose – The purpose of this paper is to present an econometric analysis of hate crime against older people based on data for England and Wales for 2010-2011 disaggregated by Crown Prosecution Service area – a geographical unit which is co-terminus with local authorities. Design/methodology/approach – The authors ran different specifications of structural regression models including one latent variable and accounting for a number of interactions between the covariates. Findings – The paper suggests that the higher the level of other types of hate crime is in an area, the higher the level of hate crime against older people. Demographics are also significant: a higher concentration of older and young people partially explains hate crime levels against the former. Employment, income and educational deprivation are also associated with biased-crime against older people. Conviction rates seem to reduce hate crime against older people, and one indicator of intergenerational contact is not significant. Research limitations/implications – Due to data availability and quality, the paper only studies one years worth of data. Consequently, the research results may lack generalisability. Furthermore, the proxy variable for intergenerational contact may not be the most suitable indicator; however, there will not be any other indicators available until Census data come out. Practical implications – The paper suggests that factors underlying hate crime would also influence hate crime against older people. Besides, the results would not support the “generational clash” view. Tackling income, educational and employment deprivation would help significantly reduce the number of episodes of biased criminal activity against older people. Improving conviction rates of all types of hate crime would also contribute to the reduction of hate crime against older people. Originality/value – This paper presents the first econometric analysis of hate crime against older people.

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