The Oxford Handbook of Quantitative Methods, Vol. 2: Statistical Analysis
The Oxford Handbook of Quantitative Methods, Vol. 2: Statistical Analysis

Author: Todd D. Little

Publisher: Oxford University Press

Published: 2013-02-01

Total Pages: 784

ISBN-13: 0199934908

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Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods is the complete tool box to deliver the most valid and generalizable answers to todays complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences. Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.

Time Series in Psychology
Time Series in Psychology

Author: R. A.M. Gregson

Publisher: Psychology Press

Published: 2014-05-22

Total Pages: 456

ISBN-13: 1317769341

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First published in 1983. Psychological data are segments of life histories; as such they are ordered sequences of observations and by definition time series. Yet they are often anything but well behaved; what regularities and invariances they have are buried from all but the most persistent investigator. The most common methods of representing quantitative results in psychology are frozen outside time; thus they deliberately average out much of the sequential structure that holds any sparse clues to the nature of processes within the organism. This review, whose simple aim is to bring together in an illuminating juxtaposition on basic results in both time series analysis and in experimental psychology, thus. cuts across traditions within psychology.

Time Series in Psychology
Time Series in Psychology

Author: R. A.M. Gregson

Publisher: Psychology Press

Published: 2014-05-22

Total Pages: 462

ISBN-13: 1317769333

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First published in 1983. Psychological data are segments of life histories; as such they are ordered sequences of observations and by definition time series. Yet they are often anything but well behaved; what regularities and invariances they have are buried from all but the most persistent investigator. The most common methods of representing quantitative results in psychology are frozen outside time; thus they deliberately average out much of the sequential structure that holds any sparse clues to the nature of processes within the organism. This review, whose simple aim is to bring together in an illuminating juxtaposition on basic results in both time series analysis and in experimental psychology, thus. cuts across traditions within psychology.

Time Series Analysis for the Social Sciences
Time Series Analysis for the Social Sciences

Author: Janet M. Box-Steffensmeier

Publisher: Cambridge University Press

Published: 2014-12-22

Total Pages: 297

ISBN-13: 1316060500

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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.

Interrupted Time Series Analysis
Interrupted Time Series Analysis

Author: David McDowall

Publisher:

Published: 2019

Total Pages: 201

ISBN-13: 0190943947

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Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. It provides example analyses of social, behavioral, and biomedical time series to illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. Additionally, the book supplements the classic Box-Jenkins-Tiao model-building strategy with recent auxiliary tests for transformation, differencing, and model selection. Not only does the text discuss new developments, including the prospects for widespread adoption of Bayesian hypothesis testing and synthetic control group designs, but it makes optimal use of graphical illustrations in its examples. With forty completed example analyses that demonstrate the implications of model properties, Interrupted Time Series Analysis will be a key inter-disciplinary text in classrooms, workshops, and short-courses for researchers familiar with time series data or cross-sectional regression analysis but limited background in the structure of time series processes and experiments.

Introduction to Time Series Analysis
Introduction to Time Series Analysis

Author: Mark Pickup

Publisher: SAGE Publications

Published: 2014-10-15

Total Pages: 233

ISBN-13: 1483313115

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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

Time and Psychological Explanation
Time and Psychological Explanation

Author: Brent D. Slife

Publisher: SUNY Press

Published: 1993-01-01

Total Pages: 362

ISBN-13: 9780791414699

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Psychology has been captured by an assumption that is almost totally unrecognized. This assumption--the linearity of time--unduly restricts theory and therapy, yet this restriction is so common, so customary, that it is often completely ignored. This book traces the influence of this assumption and reveals the many overlooked "anomalies" to its dominance. Slife describes the many findings and explanations that are incompatible with linear time in several psychological specialties. He contends that these unnoticed anomalies point to alternative conceptions of time that offer innovative ideas for psychological explanation and treatment.

Multivariate Tests for Time Series Models
Multivariate Tests for Time Series Models

Author: Jeff B. Cromwell

Publisher: SAGE

Published: 1994

Total Pages: 116

ISBN-13: 9780803954403

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Which time series test should researchers choose to best describe the interactions among a set of time series variables? Providing guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests.

Design and Analysis of Time Series Experiments
Design and Analysis of Time Series Experiments

Author: Richard McCleary

Publisher: Oxford University Press

Published: 2017

Total Pages: 393

ISBN-13: 0190661569

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Design and Analysis of Time Series Experiments develops methods and models for analysis and interpretation of time series experiments while also addressing recent developments in causal modeling. Unlike other time series texts, it integrates the statistical issues of design, estimation, and interpretation with foundational validity issues. Drawing on examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, this text addresses researchers and graduate students in a wide range of the behavioral, biomedical, and social sciences.

Multiple Time Series Models
Multiple Time Series Models

Author: Patrick T. Brandt

Publisher: SAGE

Published: 2007

Total Pages: 121

ISBN-13: 1412906563

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Many analyses of time series data involve multiple, related variables. Modeling Multiple Time Series presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these models is discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available. Key Features: * Offers a detailed comparison of different time series methods and approaches. * Includes a self-contained introduction to vector autoregression modeling. * Situates multiple time series modeling as a natural extension of commonly taught statistical models.