Time Series Analysis in the Social Sciences

Author: Youseop Shin
Publisher: Univ of California Press
ISBN: 9780520966383
Release Date: 2017-01-31
Genre: Social Science

Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and residuals, to the evaluation and prediction of estimated models. The book also explains smoothing, multiple time series analysis, and interrupted time series analysis. With a wealth of practical advice and supplemental data sets wherein students can apply their knowledge, this flexible and friendly primer is suitable for all students in the social sciences.

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.

Longitudinal and Panel Data

Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 0521535387
Release Date: 2004-08-16
Genre: Business & Economics

An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

Regression Analysis for the Social Sciences

Author: Rachel A. Gordon
Publisher: Routledge
ISBN: 9781317607113
Release Date: 2015-03-17
Genre: Social Science

Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: •interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. •thorough integration of teaching statistical theory with teaching data processing and analysis. •teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

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.

Understanding Regression Analysis

Author: Larry D. Schroeder
Publisher: SAGE
ISBN: 0803927584
Release Date: 1986-04-01
Genre: Medical

The authors have provided beginners with a background to the frequently-used technique of linear regression. It is not intended to be a substitute for a course or textbook in statistics, but rather a stop-gap for students who encounter empirical work before undertaking a statistics course. It provides a heuristic explanation of the procedures and terms used in regression analysis and has been written at the most elementary level.

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.

Just Plain Data Analysis

Author: Gary M. Klass
Publisher: Rowman & Littlefield Publishers
ISBN: 9781442215092
Release Date: 2012-04-13
Genre: Political Science

Just Plain Data Analysis is designed to teach students statistical literacy skills that they can use to evaluate and construct arguments about public affairs issues grounded in numerical evidence. With a new chapter on statistical fallacies and updates throughout the text, the new edition teaches students how to find, interpret, and present commonly used social indicators in an even clearer and more practical way.

Fundamentals of Item Response Theory

Author: Ronald K. Hambleton
Publisher: SAGE
ISBN: 0803936478
Release Date: 1991
Genre: Psychology

By using familiar concepts from classical measurement methods and basic statistics, this book introduces the basics of item response theory (IRT) and explains the application of IRT methods to problems in test construction, identification of potentially biased test items, test equating and computerized-adaptive testing. The book also includes a thorough discussion of alternative procedures for estimating IRT parameters and concludes with an exploration of new directions in IRT research and development.

Data Analysis with Mplus

Author: Christian Geiser
Publisher: Guilford Press
ISBN: 9781462507825
Release Date: 2012-11-12
Genre: Psychology

A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips and solution strategies are presented in boxes in each chapter. The companion website (http://crmda.ku.edu/guilford/geiser) features data sets, annotated syntax files, and output for all of the examples. Of special utility to instructors and students, many of the examples can be run with the free demo version of Mplus.

Practical Time Series Analysis

Author: Dr. Avishek Pal
Publisher: Packt Publishing Ltd
ISBN: 9781788294195
Release Date: 2017-09-28
Genre: Computers

Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.

Qualitative Analysis for Social Scientists

Author: Anselm L. Strauss
Publisher: Cambridge University Press
ISBN: 0521338069
Release Date: 1987-06-26
Genre: Social Science

The teaching of qualitative analysis in the social sciences is rarely undertaken in a structured way. This handbook is designed to remedy that and to present students and researchers with a systematic method for interpreting qualitative data', whether derived from interviews, field notes, or documentary materials. The special emphasis of the book is on how to develop theory through qualitative analysis. The reader is provided with the tools for doing qualitative analysis, such as codes, memos, memo sequences, theoretical sampling and comparative analysis, and diagrams, all of which are abundantly illustrated by actual examples drawn from the author's own varied qualitative research and research consultations, as well as from his research seminars. Many of the procedural discussions are concluded with rules of thumb that can usefully guide the researchers' analytic operations. The difficulties that beginners encounter when doing qualitative analysis and the kinds of persistent questions they raise are also discussed, as is the problem of how to integrate analyses. In addition, there is a chapter on the teaching of qualitative analysis and the giving of useful advice during research consultations, and there is a discussion of the preparation of material for publication. The book has been written not only for sociologists but for all researchers in the social sciences and in such fields as education, public health, nursing, and administration who employ qualitative methods in their work.

Time Series Analysis and Its Applications

Author: Robert H. Shumway
Publisher: Springer
ISBN: 9783319524528
Release Date: 2017-04-25
Genre: Mathematics

The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.

Spatial Analysis for the Social Sciences

Author: David Darmofal
Publisher: Cambridge University Press
ISBN: 9780521888264
Release Date: 2015-10-31
Genre: Political Science

This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.

Basic Content Analysis

Author: Robert Philip Weber
Publisher: SAGE
ISBN: 0803938632
Release Date: 1990
Genre: Language Arts & Disciplines

This second edition of Basic Content Analysis is completely updated and offers a concise introduction to content analysis methods from a social science perspective. It includes new computer applications, new studies and an additional chapter on problems and issues that can arise when carrying out content analysis in four major areas: measurement, indication, representation and interpretation.