The hierarchies of age-period-cohort research: Political context and the development of generational turnout patterns

Voters that come of age at roughly the same time share common influences because of the specific political context during their formative years. We can therefore assume the errors in a model explaining their political behavior to be dependent. Recent advances in social statistical analysis of age-period-cohort (APC) effects propose the use of hierarchical modeling in combination with repeated cross-sectional survey design to solve this problem. We apply these random-intercept models to assess the impact of the political context on the development of generational turnout patterns, assuming that cohorts that grew up in a highly-politicized context have a higher propensity to turnout to vote despite of any age or period effects.     

THE MULTILEVEL CONTEXT OF CRIMINAL SENTENCING: INTEGRATING JUDGE‐ AND COUNTY‐LEVEL INFLUENCES*

This study extends recent inquiries of contextual effects in sentencing by jointly examining the influence of judge and courtroom social contexts. It combines two recent years of individual sentencing data from the Pennsylvania Commission on Sentencing (PCS) with data on judicial background characteristics and county court social contexts. Three‐level hierarchical models are estimated to investigate the influence of judge and county contexts on individual variations in sentencing. Results indicate that nontrivial sentencing variations are associated with both individual judge characteristics and county court contexts. Judicial background factors also condition the influence of individual offender characteristics in important ways. These and other findings are discussed in relation to contemporary theoretical perspectives on courtroom decision making that highlight the importance of both judge and court contexts in sentencing. The study concludes with suggestions for future research on contextual disparities in criminal sentencing.     

Area characteristics and regional variates as determinants of area property crime levels

This article seeks to examine the area characteristics that act as determinants of area property crime levels, namely, incidence and prevalence. The crime figures are extracted from the 1984 British Crime Survey. Area characteristics are taken from the 1981 UK census. Initial exploratory analysis considers the non-Gaussian nature of the crime data, the statistical implications of this, and the transformations used to overcome these problems. In addition, possible regional and inner-city/non-inner-city variations are considered. The later stages move from simple individual correlations to multiple regression models. Three regression models are considered and the reasons for refining these are explored, with the results indicating that both area characteristics and regional influences play a role as determinants of the area crime level. In particular, population density and the area population age profile have significant roles to play. The conclusions support the recent revival of the application of ecological concepts in the analysis of crime levels.     

Predictive Risk Modelling to Prevent Child Maltreatment: Insights and Implications from Aotearoa/New Zealand

Predictive risk modeling to identify children at risk of maltreatment has attracted considerable interest internationally. Using the example of the development of the predictive risk model in Aotearoa/New Zealand, the potential of such approaches to provide new insights into the causes of child maltreatment is explored with reference to what were identified as the main predictors of child maltreatment. The implications of these main predictors are considered both for the reform of child protection services and the future use and development of predictive risk modeling.     

A Knowledge Management and Organizational Intelligence Model for Public Administration

This study explores the development of a KM-OI model and empirically tests, using structure equation modeling (SEM), nine hypotheses related to the following research questions: RQ1: To what extent does organizational culture impact strategy structure learning with environment and information technology?; RQ2: To what extent does information technology impact learning with environment?; RQ3: To what extent does strategy structure, learning with environment impact policies and practices of KM?; RQ4: To what extent do policies and practices of KM impact organizational intelligence? Based on the results of a web survey applied in two of the six largest economies, Germany and Brazil, the article shows the importance of KM and OI for public administration and concludes that the KM-OI model is useful to identify influential factors that must be taken into consideration to improve the processes of creation (KM) and application of knowledge (OI).     

An analysis of spatial correlates of terrorism using risk terrain modeling

ABSTRACT

The purpose of this study is to identify correlates of terrorism in space. It examines whether places with terrorist incidents show similar patterns with respect to the physical features across landscape, and tests the spatial influence of various features of environment on the incidence of terrorism. Drawing on the locations of violent terrorist offenses committed between 2008 and 2012, the study in Istanbul applies the Risk Terrain Modeling framework to terrorism. It uses data on police incidents and infrastructure (e.g., government buildings or parks). The analysis employs GIS techniques and an event count model, and combines all risky layers in a composite map to understand where the risk is higher. The study suggests a concentration of 1153 violent terrorist incidents relative to key physical factors by identifying seventeen potential risk factors, eight of which were significantly correlated in the model. Regardless of terrorists’ intent, the significantly associated establishments increase the risk in the surrounding areas where these features are located. The coexistence of leisure places such as bakeries, religious facilities, or eateries results in higher risks. While the environmental backcloth may constitute a risk for terrorism, its components may also help forecast the locations of terrorist incidents in the future.     

Job satisfaction: validating Dantzker’s four-factor model using structural equation modeling

The purpose of this study was to examine the validity of the four-factor structure from a job satisfaction instrument. Dantzker’s job satisfaction instrument was designed to capture information on four factors: general administration, extras, job, and equipment. Using data from police officers that are middle management (n?=?136), we performed confirmatory factor analysis via structural equation modeling to validate that the four factors are actually capturing job satisfaction. The results of the study show that the four-factor solution fit the model satisfactorily. All of the factor loadings were large expect for one. The results suggest that the four-factor solution has proper validity to use with middle manager police officers. Post hoc simulation analyses (n?=?1000 replications) show that no bias in the factor loadings or standard errors was present in this analysis, and the study had sufficient statistical power.     

Structural Equation Modeling of School Violence Data

SUMMARY

Methodological challenges associated with structural equation modeling (SEM) and structured means modeling (SMM) in research on school violence and related topics in the social and behavioral sciences are examined. Problems associated with multiyear implementations of large-scale surveys are discussed. Complex sample designs, part of any large-scale survey, introduce multiple methodological challenges into SEM analysis. Problems with missing data and data imputation procedures are explored, along with commonly occurring departures from normality. Hypothesis testing of models and associated fit indices are explicated. Issues surrounding the use of Lagrange Multiplier tests of constraint validity in SMM are examined. Finally, basic problems associated with developing viable models of complex social phenomena with SEM are considered.