Author: Mark Pickup
Publisher: SAGE Publications
Release Date: 2014-10-15
Genre: Social Science
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
Author: Youseop Shin
Publisher: Univ of California Press
Release Date: 2017-01-31
Genre: Social Science
"This book focuses on fundamental elements of time-series analysis that social scientists need to understand to employ time-series analysis for their research and practice. Avoiding extraordinary mathematical materials, this book explains univariate time-series analysis step-by-step, from the preliminary visual analysis through the modeling of seasonality, trends, and residuals to the prediction and the evaluation of estimated models. Then, this book explains smoothing, multiple time-series analysis, and interrupted time-series analysis. At the end of each step, this book coherently provides an analysis of the monthly violent-crime rates as an example."--Provided by publisher.
Author: Charles W. Ostrom
Release Date: 1990
Genre: Social Science
The great advantage of time series regression analysis is that it can both explain the past and predict the future behavior of variables. This volume explores the regression (or structural equation) approach to the analysis of time series data. It also introduces the Box-Jenkins time series method in an attempt to bridge partially the gap between the two approaches.
Describes ARIMA, or Box-Tiao models, widely used in the analysis of interrupted time series quasi-experiments. Assumes no statistical background beyond simple correlation.Learn more about "The Little Green Book" - QASS Series! Click Here
As one of the only texts introducing fractal analysis and the social processes involved to social science readers, this is a must-have book for those looking to gain an understanding of this area of analysis.
Providing basic foundations for measuring inequality from the perspective of distributional properties This monograpg reviews a set of widely used summary inequality measures, and the lesser known relative distribution method provides the basic rationale behind each measure and discusses their interconnections. It also introduces model-based decomposition of inequality over time using quantile regression. This approach enables researchers to estimate two different contributions to changes in inequality between two time points. Key Features Clear statistical explanations provide fundamental statistical basis for understanding the new modeling framework Straightforward empirical examples reinforce statistical knowledge and ready-to-use procedures Multiple approaches to assessing inequality are introduced by starting with the basic distributional property and providing connections among approaches This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as individual researchers. Learn more about "The Little Green Book" - QASS Series! Click Here
Author: Harry J. Khamis
Release Date: 2011-01-12
The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.
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.
Author: Richard F. Haase
Release Date: 2011-11-23
This book provides a graduate level introduction to multivariate multiple regression analysis. The book can be used as a sole text for that topic, or as a supplemental text in a course that addresses a larger number of multivariate topics. The text is divided into seven short chapters. Apart from the introductory chapter giving an overview of multivariate multiple regression models, the content outline follows the classic steps required to solve multivariate general linear model problems: (a) specifying the model (b)estimating the parameters of the model (c) establishing measures of goodness of fit of the model (d) establishing test statistics and testing hypotheses about the model (e) diagnosing the adequacy of the model.
Author: Courtney Brown
Release Date: 1995-06-28
What is chaos? How can it be measured? How are the models estimated? What is catastrophe? How is it modelled? How are the models estimated? These questions are the focus of this volume. Beginning with an explanation of the differences between deterministic and probabilistic models, Brown then introduces the reader to chaotic dynamics. Other topics covered are finding settings in which chaos can be measured, estimating chaos using nonlinear least squares and specifying catastrophe models. Finally a nonlinear system of equations that models catastrophe using real survey data is estimated.
Author: Samuel J. Best
Release Date: 2004-04-29
Designed for researchers and students alike, the volume describes how to perform each stage of the data collection process on the Internet, including sampling, instrument design, and administration. Through the use of non-technical prose and illustrations, it details the options available, describes potential dangers in choosing them, and provides guidelines for sidestepping them. In doing so, though, it does not simply reiterate the practices of traditional communication modes, but approaches the Internet as a unique medium that necessitates its own conventions.
Author: Joseph S. Wholey
Release Date: 1994-09-05
Experts in the field of program evaluation outline efficient and economical methods of assessing program results and identifying ways to improve program performance. Written for managers, administrators, and educators in nonprofit, government, and private institutions, this practical guide demystifies the assessment process. From simple evaluations to more thorough examinations, the authors describe the nuts-and-bolts of how to create an evaluation design and how to collect and analyze data in a way that will result in low cost and successful evaluations.
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
Author: Paul D. Allison
Release Date: 1984-11
Genre: Social Science
Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful.