Introduction to Time Series Analysis

Author: Mark Pickup
Publisher: SAGE Publications
ISBN: 9781483324548
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

Interrupted Time Series Analysis

Author: David McDowall
Publisher: SAGE
ISBN: 0803914938
Release Date: 1980
Genre: Social Science

Describes ARIMA or Box Tiao models, widely used in the analysis of interupted time series quasi-experiments, assuming no statistical background beyond simple correlation. The principles and concepts of ARIMA time series analyses are developed and applied where a discrete intervention has impacted a social system. '...this is the kind of exposition I wished I had had some ten years ago when venturing into the world of autoregressive, moving-average (ARIMA) models of time-series analysis...This monograph nicely lays out a method for assessing the impact of a discrete policy or event of some importance on behavior which can be continuously observed...If widely used, as I hope, it will save a generation of social scientists fro

Spatial Regression Models

Author: Michael D. Ward
Publisher: SAGE Publications
ISBN: 9781544328812
Release Date: 2018-04-10
Genre: Social Science

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.

Event History Analysis

Author: Paul D. Allison
Publisher: SAGE
ISBN: 0803920555
Release Date: 1984-11-01
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.

Time Series Analysis for the Social Sciences

Author: Janet M. Box-Steffensmeier
Publisher: Cambridge University Press
ISBN: 9781316060506
Release Date: 2014-12-22
Genre: Political Science

Time series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. Janet M. Box-Steffensmeier, John R. Freeman, Jon C. Pevehouse and Matthew P. Hitt cover a wide range of topics including ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting. This book is aimed at researchers and graduate students who have taken at least one course in multivariate regression. Examples are drawn from several areas of social science, including political behavior, elections, international conflict, criminology, and comparative political economy.

Mediation Analysis

Author: Dawn Iacobucci
Publisher: SAGE
ISBN: 9781412925693
Release Date: 2008-04-01
Genre: Mathematics

Social science data analysts have long considered the mediation of intermediate variables of primary importance in understanding individuals' social, behavioural and other kinds of outcomes. In this book Dawn Iacobucci uses the method known as structural equation modeling (SEM) in modeling mediation in causal analysis. This approach offers the most flexibility and allows the researcher to deal with mediation in the presence of multiple measures, mediated moderation, and moderated mediation, among other variations on the mediation theme. The wide availability of software implementing SEM gives the reader necessary tools for modeling mediation so that a proper understanding of causal relationship is achieved.

Multilevel Modeling

Author: Douglas A. Luke
Publisher: SAGE
ISBN: 0761928790
Release Date: 2004-07-08
Genre: Mathematics

A practical introduction to multi-level modelling, this book offers an introduction to HLM & illustrations of how to use this technique to build models for hierarchical & longitudinal data.

Agent Based Models

Author: Nigel Gilbert
Publisher: SAGE
ISBN: 9781412949644
Release Date: 2008
Genre: Social Science

Aimed at readers with minimal experience in computer programming, this brief book provides a theoretical and methodological rationale for using ABM in the social sciences. It goes on to describe some carefully chosen examples from different disciplines, illustrating different approaches to ABM. It concludes with practical advice about how to design and create ABM, a discussion of validation procedures, and some guidelines about publishing articles based on ABM.

Fixed Effects Regression Models

Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 9781483389271
Release Date: 2009-04-20
Genre: Social Science

This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. Learn more about "The Little Green Book" - QASS Series! Click Here

Applied Logistic Regression Analysis

Author: Scott Menard
Publisher: SAGE
ISBN: 0761922083
Release Date: 2002
Genre: Mathematics

The focus in this Second Edition is on logistic regression models for individual level (but aggregate or grouped) data. Multiple cases for each possible combination of values of the predictors are considered in detail and examples using SAS and SPSS included. New to this edition: · More detailed consideration of grouped as opposed to casewise data throughout the book · Updated discussion of the properties and appropriate use of goodness of fit measures, R2 analogues, and indices of predictive efficiency · Discussion of the misuse of odds ratios to represent risk ratios, and of overdispersion and underdispersion for grouped data · Updated coverage of unordered and ordered polytomous logistic regression models.

Causal Analysis with Panel Data

Author: Steven E. Finkel
Publisher: SAGE
ISBN: 0803938969
Release Date: 1995-01-17
Genre: Medical

Panel data — information gathered from the same individuals or units at several different points in time — are commonly used in the social sciences to test theories of individual and social change. This book highlights the developments in this technique in a range of disciplines and analytic traditions.

Multiple Correspondence Analysis

Author: Brigitte Le Roux
Publisher: SAGE
ISBN: 9781412968973
Release Date: 2010
Genre: Mathematics

Requiring no prior knowledge of correspondence analysis, this text provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte LeRoux and Henry Rouanet, present thematerial in a practical manner, keeping the needs of researchers foremost in mind. Key Features Readers learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations. They also learn how to perform structured data analysis and to draw inferential conclusions from MCA. The text uses real examples to help explain concepts. The authors stress the distinctive capacity of MCA to handle full-scale research studies. This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers. Learn more about “The Little Green Book” - QASS Series! Click Here

Understanding Regression Assumptions

Author: William D. Berry
Publisher: SAGE Publications
ISBN: 9781506315829
Release Date: 1993-02-25
Genre: Social Science

Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation.

Pooled Time Series Analysis

Author: Lois W. Sayrs
Publisher: SAGE
ISBN: 0803931603
Release Date: 1989-05-01
Genre: Mathematics

Researchers have often been troubled with relevant data available from both temporal observations at regular intervals (time series) and from observations at single points of time (cross-sections). Pooled Time Series Analysis combines time series and cross-sectional data to provide the researcher with an efficient method of analysis and improved estimates of the population being studied. In addition, with more relevant data available this analysis technique allows the sample size to be increased, which ultimately yields a more effective study.

Logistic Regression

Author: Fred C. Pampel
Publisher: SAGE Publications
ISBN: 9781452207612
Release Date: 2000-05-26
Genre: Social Science

Pampel's book offers readers the first "nuts and bolts" approach to doing logistic regression through the use of careful explanations and worked-out examples. This book will enable readers to use and understand logistic regression techniques and will serve as a foundation for more advanced treatments of the topic.