Nonrecursive Models is a clear and concise introduction to the estimation and assessment of nonrecursive simultaneous equation models. This unique monograph gives practical advice on the specification and identification of simultaneous equation models, how to assess the quality of the estimates, and how to correctly interpret results.
Combining insights from two distinct research traditions—the communities and crime tradition that focuses on why some neighborhoods have more crime than others, and the burgeoning crime and place literature that focuses on crime in micro-geographic units—this book explores the spatial scale of crime. Criminologist John Hipp articulates a new theoretical perspective that provides an individual- and household-level theory to underpin existing ecological models of neighborhoods and crime. A focus is maintained on the agents of change within neighborhoods and communities, and how households nested in neighborhoods might come to perceive problems in the neighborhood and then have a choice of exit, voice, loyalty, or neglect (EVLN). A characteristic of many crime incidents is that they happen at a particular spatial location and a point in time. These two simple insights suggest the need for both a spatial and a longitudinal perspective in studying crime events. The spatial question focuses on why crime seems to occur more frequently in some locations than others, and the consequences of this for certain areas of cities, or neighborhoods. The longitudinal component focuses on how crime impacts, and is impacted by, characteristics of the environment. This book looks at where offenders, targets, and guardians might live, and where they might spatially travel throughout the environment, exploring how vibrant neighborhoods are generated, how neighborhoods change, and what determines why some neighborhoods decline over time while others avoid this fate. Hipp’s theoretical model provides a cohesive response to the general question of the spatial scale of crime and articulates necessary future directions for the field. This book is essential for students and scholars interested in spatial-temporal criminology.
Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material, the author explains how to estimate simple and multiple LRMs in R, including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model, adjusting for measurement error, understanding the effects of influential observations, and using the model with multilevel data. The concluding chapter presents an alternative model—logistic regression—designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions. Features Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied. Uses numerous graphs in R to illustrate the model’s results, assumptions, and other features. Does not assume a background in calculus or linear algebra, rather, an introductory statistics course and familiarity with elementary algebra are sufficient. Provides many examples using real-world datasets relevant to various academic disciplines. Fully integrates the R software environment in its numerous examples. The book is aimed primarily at advanced undergraduate and graduate students in social, behavioral, health sciences, and related disciplines, taking a first course in linear regression. It could also be used for self-study and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena. John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior.
This brief describes the theory and evidence of a form of social control known as place management. Created by property owners, place management is an alternative to the two other domains of social control: formally created by the state and informally created by residents. It helps explain the high concentration of crime and disorder at a relatively small proportion of addresses and facilities. This volume examines the specifics of place management and extends it in three ways: to show how high crime places may radiate crime into their surroundings; to reveal networks of places that create crime hotspot spanning blocks; to demonstrate how networks of place managers influence crime throughout neighborhoods. Finally, it shows that the policy implications of place management extend far beyond the police and should include regulatory policies.
Nonrecursive Models is a clear and concise introduction to the estimation and assessment of nonrecursive simultaneous equation models. This unique monograph gives practical advice on the specification and identification of simultaneous equation models, how to assess the quality of the estimates, and how to correctly interpret results.
Combining insights from two distinct research traditions—the communities and crime tradition that focuses on why some neighborhoods have more crime than others, and the burgeoning crime and place literature that focuses on crime in micro-geographic units—this book explores the spatial scale of crime. Criminologist John Hipp articulates a new theoretical perspective that provides an individual- and household-level theory to underpin existing ecological models of neighborhoods and crime. A focus is maintained on the agents of change within neighborhoods and communities, and how households nested in neighborhoods might come to perceive problems in the neighborhood and then have a choice of exit, voice, loyalty, or neglect (EVLN). A characteristic of many crime incidents is that they happen at a particular spatial location and a point in time. These two simple insights suggest the need for both a spatial and a longitudinal perspective in studying crime events. The spatial question focuses on why crime seems to occur more frequently in some locations than others, and the consequences of this for certain areas of cities, or neighborhoods. The longitudinal component focuses on how crime impacts, and is impacted by, characteristics of the environment. This book looks at where offenders, targets, and guardians might live, and where they might spatially travel throughout the environment, exploring how vibrant neighborhoods are generated, how neighborhoods change, and what determines why some neighborhoods decline over time while others avoid this fate. Hipp’s theoretical model provides a cohesive response to the general question of the spatial scale of crime and articulates necessary future directions for the field. This book is essential for students and scholars interested in spatial-temporal criminology.
This will help us customize your experience to showcase the most relevant content to your age group
Please select from below
Login
Not registered?
Sign up
Already registered?
Success – Your message will goes here
We'd love to hear from you!
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.