Spatial Regression Models For The Social Sciences
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| Author | : Guangqing Chi |
| Publisher | : SAGE Publications |
| Total Pages | : 229 |
| Release | : 2019-03-06 |
| Genre | : Social Science |
| ISBN | : 1544302053 |
Download Spatial Regression Models for the Social Sciences Book in PDF, Epub and Kindle
Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.
| Author | : Michael D. Ward |
| Publisher | : SAGE Publications |
| Total Pages | : 129 |
| Release | : 2018-04-10 |
| Genre | : Social Science |
| ISBN | : 1544328842 |
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Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including: mapping data on spatial units, exploratory spatial data analysis, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social sciences examples based on real data, Michael D. Ward and Kristian Skrede Gleditsch illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.
| Author | : David Darmofal |
| Publisher | : Cambridge University Press |
| Total Pages | : 263 |
| Release | : 2015-11-12 |
| Genre | : Mathematics |
| ISBN | : 0521888263 |
Download Spatial Analysis for the Social Sciences Book in PDF, Epub and Kindle
This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.
| Author | : Rachel A. Gordon |
| Publisher | : Routledge |
| Total Pages | : 553 |
| Release | : 2015-03-17 |
| Genre | : Social Science |
| ISBN | : 1317607104 |
Download Regression Analysis for the Social Sciences Book in PDF, Epub and Kindle
Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: •interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. •thorough integration of teaching statistical theory with teaching data processing and analysis. •teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.
| Author | : Michael Don Ward |
| Publisher | : SAGE |
| Total Pages | : 113 |
| Release | : 2008-02-29 |
| Genre | : Mathematics |
| ISBN | : 1412954150 |
Download Spatial Regression Models Book in PDF, Epub and Kindle
Assuming no prior knowledge this book is geared toward social science readers, unlike other volumes on this topic. The text illustrates concepts using well known international, comparative, and national examples of spatial regression analysis. Each example is presented alongside relevant data and code, which is also available on a Web site maintained by the authors.
| Author | : Robert Nash Parker |
| Publisher | : Routledge |
| Total Pages | : 254 |
| Release | : 2009-09-10 |
| Genre | : Political Science |
| ISBN | : 1135857598 |
Download GIS and Spatial Analysis for the Social Sciences Book in PDF, Epub and Kindle
This is the first book to provide sociologists, criminologists, political scientists, and other social scientists with the methodological logic and techniques for doing spatial analysis in their chosen fields of inquiry. The book contains a wealth of examples as to why these techniques are worth doing, over and above conventional statistical techniques using SPSS or other statistical packages. GIS is a methodological and conceptual approach that allows for the linking together of spatial data, or data that is based on a physical space, with non-spatial data, which can be thought of as any data that contains no direct reference to physical locations.
| Author | : Guangqing Chi |
| Publisher | : SAGE Publications |
| Total Pages | : 273 |
| Release | : 2019-03-06 |
| Genre | : Social Science |
| ISBN | : 1544302088 |
Download Spatial Regression Models for the Social Sciences Book in PDF, Epub and Kindle
Space and geography are important aspects of social science research in fields such as criminology, sociology, political science, and public health. Many social scientists are interested in the spatial clustering of various behaviors and events. There has been a rapid development of interest in regression methods for analyzing spatial data over recent years, but little available on the topic that is aimed at graduate students and advanced undergraduate classes in the social sciences (most texts are for the natural sciences, or regional science, or economics, and require a good understanding of advanced statistics and probability theory). Spatial Regression Models for the Social Sciences fills the gap, and focuses on the methods that are commonly used by social scientists. Each spatial regression method is introduced in the same way. Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it, by connecting it to social science research topics. They try to avoid mathematical formulas and symbols as much as possible. Secondly, throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us. Spatial Regression Models for the Social Sciences provides comprehensive coverage of spatial regression methods for social scientists and introduces the methods in an easy-to-follow manner.
| Author | : David Darmofal |
| Publisher | : Cambridge University Press |
| Total Pages | : 263 |
| Release | : 2015-10-29 |
| Genre | : Political Science |
| ISBN | : 1316395278 |
Download Spatial Analysis for the Social Sciences Book in PDF, Epub and Kindle
Many theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. Spatial Analysis for the Social Sciences demonstrates how researchers can diagnose and model this spatial dependence and draw more valid inferences as a result. The book is structured around the well-known Galton's problem and presents a step-by-step guide to the application of spatial analysis. The book examines a variety of spatial diagnostics and models through a series of applied examples drawn from the social sciences. These include spatial lag models that capture behavioral diffusion between actors, spatial error models that account for spatial dependence in errors, and models that incorporate spatial heterogeneity in the effects of covariates. Spatial Analysis for the Social Sciences also examines advanced spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.
| Author | : Daniel Griffith |
| Publisher | : Academic Press |
| Total Pages | : 286 |
| Release | : 2019-09-14 |
| Genre | : Business & Economics |
| ISBN | : 0128156929 |
Download Spatial Regression Analysis Using Eigenvector Spatial Filtering Book in PDF, Epub and Kindle
Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre. Reviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient models Includes computer code and template datasets for further modeling Provides comprehensive coverage of related concepts in spatial data analysis and spatial statistics
| Author | : Lingxin Hao |
| Publisher | : SAGE Publications |
| Total Pages | : 142 |
| Release | : 2007-04-18 |
| Genre | : Social Science |
| ISBN | : 1483316904 |
Download Quantile Regression Book in PDF, Epub and Kindle
Quantile Regression, the first book of Hao and Naiman′s two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research
