Ecological Inference

Author: Gary King
Publisher: Cambridge University Press
ISBN: 9780521542807
Size: 68.80 MB
Format: PDF, ePub
View: 4989
Download
Drawing upon the explosion of research in the field, a diverse group of scholars surveys strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays, first published in 2004, offers many important contributions to the study of ecological inference.

Spatial Analysis For The Social Sciences

Author: David Darmofal
Publisher: Cambridge University Press
ISBN: 0521888263
Size: 50.43 MB
Format: PDF, Docs
View: 1328
Download
This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.

A Solution To The Ecological Inference Problem

Author: Gary King
Publisher: Princeton University Press
ISBN: 1400849209
Size: 54.30 MB
Format: PDF, Docs
View: 6820
Download
This book provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behavior from aggregate (ecological) data? In political science, this question arises when individual-level surveys are unavailable (for instance, local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). This ecological inference problem also confronts researchers in numerous areas of major significance in public policy, and other academic disciplines, ranging from epidemiology and marketing to sociology and quantitative history. Although many have attempted to make such cross-level inferences, scholars agree that all existing methods yield very inaccurate conclusions about the world. In this volume, Gary King lays out a unique--and reliable--solution to this venerable problem. King begins with a qualitative overview, readable even by those without a statistical background. He then unifies the apparently diverse findings in the methodological literature, so that only one aggregation problem remains to be solved. He then presents his solution, as well as empirical evaluations of the solution that include over 16,000 comparisons of his estimates from real aggregate data to the known individual-level answer. The method works in practice. King's solution to the ecological inference problem will enable empirical researchers to investigate substantive questions that have heretofore proved unanswerable, and move forward fields of inquiry in which progress has been stifled by this problem.

Statistical Modeling And Inference For Social Science

Author: Sean Gailmard
Publisher: Cambridge University Press
ISBN: 1107003148
Size: 73.75 MB
Format: PDF, Kindle
View: 1468
Download
Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students gain the ability to create, read and critique statistical applications in their fields of interest.

The Oxford Handbook Of Political Methodology

Author: Janet M. Box-Steffensmeier
Publisher: Oxford University Press on Demand
ISBN: 9780199286546
Size: 38.45 MB
Format: PDF, ePub
View: 7531
Download
The Oxford Handbooks of Political Science are the essential guide to the state of political science today. With engaging contributions from major international scholars The Oxford Handbook of Political Methodology provides the key point of reference for anyone working throughout the discipline.

Approaches And Methodologies In The Social Sciences

Author: Donatella Della Porta
Publisher: Cambridge University Press
ISBN: 1139474596
Size: 12.80 MB
Format: PDF, ePub
View: 7368
Download
A revolutionary textbook introducing masters and doctoral students to the major research approaches and methodologies in the social sciences. Written by an outstanding set of scholars, and derived from successful course teaching, this volume will empower students to choose their own approach to research, to justify this approach, and to situate it within the discipline. It addresses questions of ontology, epistemology and philosophy of social science, and proceeds to issues of methodology and research design essential for producing a good research proposal. It also introduces researchers to the main issues of debate and contention in the methodology of social sciences, identifying commonalities, historic continuities and genuine differences.

Missing Data

Author: Patrick E. McKnight
Publisher: Guilford Press
ISBN: 1606238205
Size: 22.16 MB
Format: PDF, Kindle
View: 2700
Download
While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study’s conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study results. When statistical remedies are needed--such as deletion procedures, augmentation methods, and single imputation and multiple imputation procedures--the book also explains how to make sound decisions about their use. Patrick E. McKnight's website offers a periodically updated annotated bibliography on missing data and links to other Web resources that address missing data.

Design And Analysis Of Time Series Experiments

Author: Richard McCleary
Publisher: Oxford University Press
ISBN: 0190661569
Size: 70.31 MB
Format: PDF, ePub, Mobi
View: 7284
Download
Design and Analysis of Time Series Experiments presents the elements of statistical time series analysis while also addressing recent developments in research design and causal modeling. A distinguishing feature of the book is its integration of design and analysis of time series experiments.Drawing examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, Design and Analysis of Time Series Experiments is addressed to researchers and graduate students in a wide range of behavioral, biomedical and social sciences.Readers learn not only how-to skills but, also the underlying rationales for the design features and the analytical methods. ARIMA algebra, Box-Jenkins-Tiao models and model-building strategies, forecasting, and Box-Tiao impact models are developed in separate chapters. The presentation of themodels and model-building assumes only exposure to an introductory statistics course, with more difficult mathematical material relegated to appendices. Separate chapters cover threats to statistical conclusion validity, internal validity, construct validity, and external validity with an emphasison how these threats arise in time series experiments. Design structures for controlling the threats are presented and illustrated through examples. The chapters on statistical conclusion validity and internal validity introduce Bayesian methods, counterfactual causality and synthetic control groupdesigns. Building on the earlier of the authors, Design and Analysis of Time Series Experiments includes more recent developments in modeling, and considers design issues in greater detail than any existing work. Additionally, the book appeals to those who want to conduct or interpret time seriesexperiments, as well as to those interested in research designs for causal inference.