Download spatial regression models quantitative applications in the social sciences in pdf or read spatial regression models quantitative applications in the social sciences in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get spatial regression models quantitative applications in the social sciences in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Spatial Regression Models

Author: Michael D. Ward
Publisher: SAGE Publications
ISBN: 1544328818
Size: 52.89 MB
Format: PDF, Kindle
View: 5863
Download and Read
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.

Modeling And Interpreting Interactive Hypotheses In Regression Analysis

Author: Robert Franzese
Publisher: University of Michigan Press
ISBN: 9780472022991
Size: 80.43 MB
Format: PDF, Kindle
View: 7196
Download and Read
Social scientists study complex phenomena about which they often propose intricate hypotheses tested with linear-interactive or multiplicative terms. While interaction terms are hardly new to social science research, researchers have yet to develop a common methodology for using and interpreting them. Modeling and Interpreting Interactive Hypotheses in Regression Analysis provides step-by-step guidance on how to connect substantive theories to statistical models and how to interpret and present the results. "Kam and Franzese is a must-have for all empirical social scientists interested in teasing out the complexities of their data." ---Janet M. Box-Steffensmeier, Ohio State University "Kam and Franzese have written what will become the definitive source on dealing with interaction terms and testing interactive hypotheses. It will serve as the standard reference for political scientists and will be one of those books that everyone will turn to when helping our students or doing our work. But more than that, this book is the best text I have seen for getting students to really think about the importance of careful specification and testing of their hypotheses." ---David A. M. Peterson, Texas A&M University "Kam and Franzese have given scholars and teachers of regression models something they've needed for years: a clear, concise guide to understanding multiplicative interactions. Motivated by real substantive examples and packed with valuable examples and graphs, their book belongs on the shelf of every working social scientist." ---Christopher Zorn, University of South Carolina "Kam and Franzese make it easy to model what good researchers have known for a long time: many important and interesting causal effects depend on the presence of other conditions. Their book shows how to explore interactive hypotheses in your own research and how to present your results. The book is straightforward yet technically sophisticated. There are no more excuses for misunderstanding, misrepresenting, or simply missing out on interaction effects!" ---Andrew Gould, University of Notre Dame Cindy D. Kam is Assistant Professor, Department of Political Science, University of California, Davis. Robert J. Franzese Jr. is Associate Professor, Department of Political Science, University of Michigan, and Research Associate Professor, Center for Political Studies, Institute for Social Research, University of Michigan. For datasets, syntax, and worksheets to help readers work through the examples covered in the book, visit: www.press.umich.edu/KamFranzese/Interactions.html

Quantitative Methods And Applications In Gis

Author: Fahui Wang
Publisher: CRC Press
ISBN: 142000428X
Size: 72.36 MB
Format: PDF, ePub
View: 6880
Download and Read
Quantitative Methods and Applications in GIS integrates GIS, spatial analysis, and quantitative methods to address various issues in socioeconomic studies and public policy. Methods range from basic regression analysis to advanced topics such as linear programming and system of equations. Applications vary from typical themes in urban and regional analysis - trade area analysis, accessibility measures, analysis of regional growth patterns, land use simulation - to issues related to crime and health analyses. The book covers common tasks such as distance and travel time estimation, spatial smoothing and interpolation, and accessibility measures. It also covers the major issues that are encountered in spatial analysis including modifiable areal unit problems, rate estimate of rare events in small populations, and spatial autocorrelation. Each chapter has one subject theme, introduces the method (or a group of related methods) most relevant to the theme, and then uses case studies to implement the method in a GIS environment.

Quantile Regression For Spatial Data

Author: Daniel P. McMillen
Publisher: Springer Science & Business Media
ISBN: 3642318150
Size: 24.30 MB
Format: PDF, Kindle
View: 7601
Download and Read
Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an explanatory variable, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Both parametric and nonparametric versions of spatial models are considered in detail.

Spatial Analysis For The Social Sciences

Author: David Darmofal
Publisher: Cambridge University Press
ISBN: 0521888263
Size: 27.50 MB
Format: PDF, Kindle
View: 2396
Download and Read
This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.

Quantitative Geography

Author: A Stewart Fotheringham
Publisher: SAGE
ISBN: 1446228339
Size: 23.76 MB
Format: PDF, ePub
View: 6525
Download and Read
Integrating a discussion of the application of quantitative methods with practical examples, this book explains the philosophy of the new quantitative methodologies and contrasts them with the methods associated with geography's `Quantitative Revolution' of the 1960s. Key issues discussed include: the nature of modern quantitative geography; spatial data; geographical information systems; visualization; local analysis; point pattern analysis; spatial regression; and statistical inference. Concluding with a review of models used in spatial theory, the authors discuss the current challenges to spatial data analysis. Written to be accessible, to communicate the diversity and excitement of recent thinking, Quantitative Geography will be required reading for students and researchers in any discipline where quantitative methods are used to analyse spatial data. `This is a veritable tour de force of everything that is exciting about quantitative geography and GIS. It is a timely, thorough and exciting account of the state of the art and science of spatial analysis' - Paul Longley, University of Bristol `A highly innovative and up-to-date text. It is unique in its coverage of the many developments that have taken place in the field over the past few years. The book is one that is highly readable and stimulating for those with some background in the field, and its expositional style and many examples will make it stimulating to newcomers as well' - Peter Rogerson, State University of New York at Buffalo `Brings the field thoroughly up to date, integrating modern methods of GIS with a comprehensive and easy-to-read overview of the most recent and powerful techniques of spatial analysis. The book will be valuable to students and researchers in any discipline that seeks to explore or explain phenomena in geographical context, and will make excellent reading for geographers, political scientists, criminologists, anthropologists, geologists, epidemiologists, ecologists, and many others. It offers a spirited challenge to critics of a scientific approach to social science, and demonstrates the value of its subject matter through abundant examples' - Michael Goodchild, National Center for Geographic Information and Analysis, University of California, Santa Barbara `There is a view within some parts of academic geography that what used to be called "quantitative geography" is dead, having been subsumed within "geographical information systems" or else of no continuing interest. This book should correct this view. First, it shows that quantitative methods have remained an exciting area of development and, second, it shows that, if anything, they have more relevance to substantive problems of interest than they have ever had. Although not specifically about GIS, it is a book that should be read by everyone concerned with the analysis of geographical information' - David Unwin, Birkbeck College, University of London

Spatial Data Analysis

Author: Christopher Lloyd
Publisher: Oxford University Press
ISBN: 0199554323
Size: 58.91 MB
Format: PDF, Mobi
View: 2495
Download and Read
Spatial Data Analysis introduces key principles about spatial data and provides guidance on methods for their exploration; it provides a set of key ideas or frameworks that will give the reader knowledge of the kinds of problems that can be tackled using the tools that are widely available for the analysis of spatial data.

Exploring Spatial Scale In Geography

Author: Christopher D. Lloyd
Publisher: John Wiley & Sons
ISBN: 1119971357
Size: 12.10 MB
Format: PDF, ePub, Mobi
View: 4279
Download and Read
This book offers alternative definitions of spatial scale, presents approaches for exploring spatial scale, and makes use of a wide variety of case studies in the physical and social sciences to demonstrate key concepts. It consists of three integrated strands. The first is conceptual, outlining some definitions of spatial scale and debating the meaning and value of concepts of scale. The second strand outlines methods for exploration of spatial scale including standard measures of spatial autocorrelation, fractals, wavelets, multilevel models and geostatistical measures. The third and final strand demonstrates the application of these concepts and methods to real world case studies. --

Introduction To Time Series Analysis

Author: Mark Pickup
Publisher: SAGE Publications
ISBN: 1483324540
Size: 54.91 MB
Format: PDF, Docs
View: 4850
Download and Read
Introducing time series methods and their application in social science research, this practical guide to time series models is the first in the field written for a non-econometrics audience. Giving readers the tools they need to apply models to their own research, Introduction to Time Series Analysis, by Mark Pickup, demonstrates the use of—and the assumptions underlying—common models of time series data including finite distributed lag; autoregressive distributed lag; moving average; differenced data; and GARCH, ARMA, ARIMA, and error correction models. “This volume does an excellent job of introducing modern time series analysis to social scientists who are already familiar with basic statistics and the general linear model.” —William G. Jacoby, Michigan State University

Applied Statistical Modeling

Author: Salvatore Babones
Publisher: SAGE Publications Limited
ISBN: 9781446208397
Size: 27.90 MB
Format: PDF, Kindle
View: 1880
Download and Read
This new four-volume set on Applied Statistical Modeling brings together seminal articles in the field, selected for their exemplification of the specific model type used, their clarity of exposition and their importance to the development of their respective disciplines. The set as a whole is designed to serve as a master class in how to apply the most commonly used statistical models with the highest level of methodological sophistication. It is in essence a user's guide to statistical best-practice in the social sciences. This truly multi-disciplinary collection covers the most important statistical methods used in sociology, social psychology, political science, management science, media studies, anthropology and human geography. The articles are organised by model type into thematic sections that include selections from multiple disciplines. There are a total of thirteen sections, each with a brief introduction summarising common applications: Volume One: Control variables; Multicolinearity and variance inflation; Interaction models; Multilevel models Volume Two: Models for panel data; Time series cross-sectional analysis; Spatial models; Logistic regression Volume Three: Multinomial logit; Poisson regression; Instrumental variables Volume Four: Structural equation models; Latent variable models