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: 1980-08
Genre: Social Science

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

Spatial Regression Models

Author: Michael D. Ward
Publisher: SAGE
ISBN: 9781412954150
Release Date: 2008-02-29
Genre: Mathematics

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.

Event History Analysis

Author: Paul D. Allison
Publisher: SAGE
ISBN: 0803920555
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.

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.

Time Series Analysis for the Social Sciences

Author: Janet M. Box-Steffensmeier
Publisher: Cambridge University Press
ISBN: 9780521871167
Release Date: 2014-12-22
Genre: Political Science

This book provides instruction and examples of the core methods in time series econometrics, drawing from several main fields of the social sciences.

Principal Components Analysis

Author: George H. Dunteman
Publisher: SAGE
ISBN: 0803931042
Release Date: 1989-05
Genre: Mathematics

For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra -- the reader can quickly master and put this technique to immediate use.

Calculus

Author: Gudmund R. Iversen
Publisher: SAGE
ISBN: 0803971109
Release Date: 1996-01-18
Genre: Mathematics

This book offers an overview of the central ideas in calculus and gives examples of how calculus is used to translate many real-world phenomena into mathematical functions. Beginning with an explanation of the two major parts of calculus - differentiation and integration - Gudmund R Iversen illustrates how calculus is used in statistics: to distinguish between the mean and the median; to derive the least squares formulas for regression co-efficients; to find values of parameters from theoretical distributions; and to find a statistical p-value when using one of the continuous test variables such as the t-variable.

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.

Design and Analysis of Time Series Experiments

Author: Richard McCleary
Publisher: Oxford University Press
ISBN: 9780190661564
Release Date: 2017
Genre: Medical

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.

Time Series Analysis

Author: John M. Gottman
Publisher: Cambridge University Press
ISBN: 9780521235976
Release Date: 1981
Genre: Mathematics

This book is a comprehensive introduction to all the major time-series techniques, both time-domain and frequency-domain. It includes work on linear models that simplify the solution of univariate and multivariate problems. The author begins with a non-mathematical overview and provides throughout, easy-to-understand, fully worked examples drawn from real studies in psychology and sociology.

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.

Pooled Time Series Analysis

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

Analysis of Variance

Author: Gudmund R. Iversen
Publisher: SAGE
ISBN: 0803930011
Release Date: 1987
Genre: Mathematics

The second edition of this book provides a conceptual understanding of analysis of variance. It outlines methods for analysing variance that are used to study the effect of one or more nominal variables on a dependent, interval level variable. The book presumes only elementary background in significance testing and data analysis.

Regression Diagnostics

Author: John Fox
Publisher: SAGE
ISBN: 080393971X
Release Date: 1991-08-14
Genre: Mathematics

With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The book covers such topics as the problem of collinearity in multiple regression, dealing with outlying and influential data, non-normality of errors, non-constant error variance and the problems and opportunities presented by discrete data. In addition, sophisticated diagnostics based on maximum-likelihood methods, scores tests, and constructed variables are introduced.