Bayesian Methods

Bayesian Methods
Author: Jeff Gill
Publsiher: Chapman and Hall/CRC
Total Pages: 724
Release: 2014-12-17
ISBN: 9781439862483
Category: Mathematics
Language: EN, FR, DE, ES & NL

Bayesian Methods Book Excerpt:

An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of the procedures and less on justifying procedures. The expanded examples reflect this updated approach. New to the Third Edition A chapter on Bayesian decision theory, covering Bayesian and frequentist decision theory as well as the connection of empirical Bayes with James–Stein estimation A chapter on the practical implementation of MCMC methods using the BUGS software Greatly expanded chapter on hierarchical models that shows how this area is well suited to the Bayesian paradigm Many new applications from a variety of social science disciplines Double the number of exercises, with 20 now in each chapter Updated BaM package in R, including new datasets, code, and procedures for calling BUGS packages from R This bestselling, highly praised text continues to be suitable for a range of courses, including an introductory course or a computing-centered course. It shows students in the social and behavioral sciences how to use Bayesian methods in practice, preparing them for sophisticated, real-world work in the field.

Bayesian Methods

Bayesian Methods
Author: Jeff Gill
Publsiher: CRC Press
Total Pages: 724
Release: 2014-12-11
ISBN: 1439862494
Category: Mathematics
Language: EN, FR, DE, ES & NL

Bayesian Methods Book Excerpt:

An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of the procedures and less on justifying procedures. The expanded examples reflect this updated approach. New to the Third Edition A chapter on Bayesian decision theory, covering Bayesian and frequentist decision theory as well as the connection of empirical Bayes with James–Stein estimation A chapter on the practical implementation of MCMC methods using the BUGS software Greatly expanded chapter on hierarchical models that shows how this area is well suited to the Bayesian paradigm Many new applications from a variety of social science disciplines Double the number of exercises, with 20 now in each chapter Updated BaM package in R, including new datasets, code, and procedures for calling BUGS packages from R This bestselling, highly praised text continues to be suitable for a range of courses, including an introductory course or a computing-centered course. It shows students in the social and behavioral sciences how to use Bayesian methods in practice, preparing them for sophisticated, real-world work in the field.

The Oxford Handbook of Quantitative Methods Volume 1 Foundations

The Oxford Handbook of Quantitative Methods  Volume 1  Foundations
Author: Todd D. Little
Publsiher: Oxford University Press
Total Pages: 506
Release: 2013-01-15
ISBN: 0199934886
Category: Psychology
Language: EN, FR, DE, ES & NL

The Oxford Handbook of Quantitative Methods Volume 1 Foundations Book Excerpt:

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.

The Oxford Handbook of Quantitative Methods in Psychology

The Oxford Handbook of Quantitative Methods in Psychology
Author: Todd D. Little
Publsiher: Oxford University Press, USA
Total Pages: 515
Release: 2014
ISBN: 019937015X
Category: Psychology
Language: EN, FR, DE, ES & NL

The Oxford Handbook of Quantitative Methods in Psychology Book Excerpt:

This two-volume handbook on current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences covers philosophical and ethical issues, theory construction, model building and types of models, survey and experiment design, measurement issues, observational methods, statistical methods, types of analysis, types of data, and common research fallacies.

Understanding Computational Bayesian Statistics

Understanding Computational Bayesian Statistics
Author: William M. Bolstad
Publsiher: John Wiley & Sons
Total Pages: 336
Release: 2011-09-20
ISBN: 1118209923
Category: Mathematics
Language: EN, FR, DE, ES & NL

Understanding Computational Bayesian Statistics Book Excerpt:

A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model. The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include: Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distribution The distributions from the one-dimensional exponential family Markov chains and their long-run behavior The Metropolis-Hastings algorithm Gibbs sampling algorithm and methods for speeding up convergence Markov chain Monte Carlo sampling Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages. Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.

Bayesian Essentials with R

Bayesian Essentials with R
Author: Jean-Michel Marin,Christian P. Robert
Publsiher: Springer Science & Business Media
Total Pages: 296
Release: 2013-10-28
ISBN: 1461486874
Category: Computers
Language: EN, FR, DE, ES & NL

Bayesian Essentials with R Book Excerpt:

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.

Advanced Research Methods for the Social and Behavioral Sciences

Advanced Research Methods for the Social and Behavioral Sciences
Author: John E. Edlund,Austin Lee Nichols
Publsiher: Cambridge University Press
Total Pages: 300
Release: 2019-03-14
ISBN: 1108425933
Category: Psychology
Language: EN, FR, DE, ES & NL

Advanced Research Methods for the Social and Behavioral Sciences Book Excerpt:

Written by an interdisciplinary team of global experts, this book is an invaluable tool for anyone learning about research methods.

Monte Carlo Simulation and Resampling Methods for Social Science

Monte Carlo Simulation and Resampling Methods for Social Science
Author: Thomas M. Carsey,Jeffrey J. Harden
Publsiher: SAGE Publications
Total Pages: 304
Release: 2013-08-05
ISBN: 1483313476
Category: Social Science
Language: EN, FR, DE, ES & NL

Monte Carlo Simulation and Resampling Methods for Social Science Book Excerpt:

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, Monte Carlo Simulation and Resampling Methods for Social Science, by Thomas M. Carsey and Jeffrey J. Harden, examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.

Collective Political Rationality

Collective Political Rationality
Author: Gregory E. McAvoy
Publsiher: Routledge
Total Pages: 134
Release: 2015-05-15
ISBN: 1317504658
Category: Political Science
Language: EN, FR, DE, ES & NL

Collective Political Rationality Book Excerpt:

Amidst the polarization of contemporary politics, partisan loyalties among citizens are regarded as one contributor to political stalemate. Partisan loyalties lead Democrats and Republicans to look at the same economic information but to come to strikingly different conclusions about the state of the economy and the performance of the president in managing it. As a result, many observers argue that democratic politics would work better if citizens would shed their party loyalty and more dispassionately assess political and economic news. In this book, Gregory E. McAvoy argues—contra this conventional wisdom; that partisanship is a necessary feature of modern politics, making it feasible for citizens to make some sense of the vast number of issues that make their way onto the political agenda. Using unique data, he shows that the biases and distortions that partisanship introduces to collective opinion are real, but despite them, collective opinion changes meaningfully in response to economic and political news. In a comparison of the public’s assessment of the economy to those of economic experts, he finds a close correspondence between the two over time, and that in modern democracies an informed public will also necessarily be partisan. Modernizing the study of collective opinion, McAvoy's book is essential reading for scholars of American Public Opinion and Political Behavior.

Modern Statistics for the Social and Behavioral Sciences

Modern Statistics for the Social and Behavioral Sciences
Author: Rand Wilcox
Publsiher: CRC Press
Total Pages: 730
Release: 2017-08-15
ISBN: 1498796796
Category: Mathematics
Language: EN, FR, DE, ES & NL

Modern Statistics for the Social and Behavioral Sciences Book Excerpt:

Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a two-semester, graduate-level introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in an introductory course. Hundreds of journal articles make it clear that basic techniques, routinely taught and used, can perform poorly when dealing with skewed distributions, outliers, heteroscedasticity (unequal variances) and curvature. Methods for dealing with these concerns have been derived and can provide a deeper, more accurate and more nuanced understanding of data. A conceptual basis is provided for understanding when and why standard methods can have poor power and yield misleading measures of effect size. Modern techniques for dealing with known concerns are described and illustrated. Features: Presents an in-depth description of both classic and modern methods Explains and illustrates why recent advances can provide more power and a deeper understanding of data Provides numerous illustrations using the software R Includes an R package with over 1300 functions Includes a solution manual giving detailed answers to all of the exercises This second edition describes many recent advances relevant to basic techniques. For example, a vast array of new and improved methods is now available for dealing with regression, including substantially improved ANCOVA techniques. The coverage of multiple comparison procedures has been expanded and new ANOVA techniques are described. Rand Wilcox is a professor of psychology at the University of Southern California. He is the author of 13 other statistics books and the creator of the R package WRS. He currently serves as an associate editor for five statistics journals. He is a fellow of the Association for Psychological Science and an elected member of the International Statistical Institute.

Stevens Handbook of Experimental Psychology and Cognitive Neuroscience Methodology

Stevens  Handbook of Experimental Psychology and Cognitive Neuroscience  Methodology
Author: John T. Wixted
Publsiher: John Wiley & Sons
Total Pages: 848
Release: 2018-03-13
ISBN: 1119170125
Category: Psychology
Language: EN, FR, DE, ES & NL

Stevens Handbook of Experimental Psychology and Cognitive Neuroscience Methodology Book Excerpt:

V. Methodology: E. J. Wagenmakers (Volume Editor) Topics covered include methods and models in categorization; cultural consensus theory; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning; convergent methods of memory research; theories for discriminating signal from noise; bayesian cognitive modeling; mathematical modeling in cognition and cognitive neuroscience; the stop-signal paradigm; hypothesis testing and statistical inference; model comparison in psychology; fmri; neural recordings; open science; neural networks and neurocomputational modeling; serial versus parallel processing; methods in psychophysics.

Handbook of International Large Scale Assessment

Handbook of International Large Scale Assessment
Author: Leslie Rutkowski,Matthias von Davier,David Rutkowski
Publsiher: CRC Press
Total Pages: 647
Release: 2013-11-12
ISBN: 1439895147
Category: Mathematics
Language: EN, FR, DE, ES & NL

Handbook of International Large Scale Assessment Book Excerpt:

Technological and statistical advances, along with a strong interest in gathering more information about the state of our educational systems, have made it possible to assess more students, in more countries, more often, and in more subject domains. The Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis brings together recognized scholars in the field of ILSA, behavioral statistics, and policy to develop a detailed guide that goes beyond database user manuals. After highlighting the importance of ILSA data to policy and research, the book reviews methodological aspects and features of the studies based on operational considerations, analytics, and reporting. The book then describes methods of interest to advanced graduate students, researchers, and policy analysts who have a good grounding in quantitative methods, but who are not necessarily quantitative methodologists. In addition, it provides a detailed exposition of the technical details behind these assessments, including the test design, the sampling framework, and estimation methods, with a focus on how these issues impact analysis choices.

Categorical Data Analysis and Multilevel Modeling Using R

Categorical Data Analysis and Multilevel Modeling Using R
Author: Xing Liu
Publsiher: SAGE Publications
Total Pages: 744
Release: 2022-02-25
ISBN: 154432488X
Category: Social Science
Language: EN, FR, DE, ES & NL

Categorical Data Analysis and Multilevel Modeling Using R Book Excerpt:

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Author Xing Liu offers a unified framework for both single-level and multilevel modeling of categorical and count response variables with both frequentist and Bayesian approaches. Each chapter demonstrates how to conduct the analysis using R, how to interpret the models, and how to present the results for publication. A companion website for this book at https://edge.sagepub.com/liu1e contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.

Multilevel Structural Equation Modeling

Multilevel Structural Equation Modeling
Author: Bruno Castanho Silva,Constantin Manuel Bosancianu,Levente Littvay
Publsiher: SAGE Publications
Total Pages: 144
Release: 2019-02-28
ISBN: 1544323069
Category: Social Science
Language: EN, FR, DE, ES & NL

Multilevel Structural Equation Modeling Book Excerpt:

Multilevel Structural Equation Modeling by Bruno Castanho Silva, Constantin Manuel Bosancianu, and Levente Littvay serves as a minimally technical overview of multilevel structural equation modeling (MSEM) for applied researchers and advanced graduate students in the social sciences. As the first book of its kind, this title is an accessible, hands-on introduction for beginners of the topic. The authors predict a growth in this area, fueled by both data availability and also the availability of new and improved software to run these models. The applied approach, combined with a graphical presentation style and minimal reliance on complex matrix algebra guarantee that this volume will be useful to social science graduate students wanting to utilize such models.

Distributions for Modeling Location Scale and Shape

Distributions for Modeling Location  Scale  and Shape
Author: Robert A. Rigby,Mikis D. Stasinopoulos,Gillian Z. Heller,Fernanda De Bastiani
Publsiher: CRC Press
Total Pages: 560
Release: 2019-10-08
ISBN: 100069996X
Category: Mathematics
Language: EN, FR, DE, ES & NL

Distributions for Modeling Location Scale and Shape Book Excerpt:

This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the distribution of a response variable on explanatory variables. It will be especially useful to applied statisticians and data scientists in a wide range of application areas, and also to those interested in the theoretical properties of distributions. This book follows the earlier book ‘Flexible Regression and Smoothing: Using GAMLSS in R’, [Stasinopoulos et al., 2017], which focused on the GAMLSS model and software. GAMLSS (the Generalized Additive Model for Location, Scale, and Shape, [Rigby and Stasinopoulos, 2005]), is a regression framework in which the response variable can have any parametric distribution and all the distribution parameters can be modelled as linear or smooth functions of explanatory variables. The current book focuses on distributions and their application. Key features: Describes over 100 distributions, (implemented in the GAMLSS packages in R), including continuous, discrete and mixed distributions. Comprehensive summary tables of the properties of the distributions. Discusses properties of distributions, including skewness, kurtosis, robustness and an important classification of tail heaviness. Includes mixed distributions which are continuous distributions with additional specific values with point probabilities. Includes many real data examples, with R code integrated in the text for ease of understanding and replication. Supplemented by the gamlss website. This book will be useful for applied statisticians and data scientists in selecting a distribution for a univariate response variable and modelling its dependence on explanatory variables, and to those interested in the properties of distributions.

Latent Variable Models

Latent Variable Models
Author: John C. Loehlin,A. Alexander Beaujean
Publsiher: Taylor & Francis
Total Pages: 376
Release: 2016-12-07
ISBN: 131728528X
Category: Psychology
Language: EN, FR, DE, ES & NL

Latent Variable Models Book Excerpt:

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing path diagrams to explain the relationships in the models. This approach helps less mathematically-inclined readers to grasp the underlying relations among path analysis, factor analysis, and structural equation modeling, and to set up and carry out such analyses. This revised and expanded fifth edition again contains key chapters on path analysis, structural equation models, and exploratory factor analysis. In addition, it contains new material on composite reliability, models with categorical data, the minimum average partial procedure, bi-factor models, and communicating about latent variable models. The informal writing style and the numerous illustrative examples make the book accessible to readers of varying backgrounds. Notes at the end of each chapter expand the discussion and provide additional technical detail and references. Moreover, most chapters contain an extended example in which the authors work through one of the chapter’s examples in detail to aid readers in conducting similar analyses with their own data. The book and accompanying website provide all of the data for the book’s examples as well as syntax from latent variable programs so readers can replicate the analyses. The book can be used with any of a variety of computer programs, but special attention is paid to LISREL and R. An important resource for advanced students and researchers in numerous disciplines in the behavioral sciences, education, business, and health sciences, Latent Variable Models is a practical and readable reference for those seeking to understand or conduct an analysis using latent variables.

Bayesian Psychometric Modeling

Bayesian Psychometric Modeling
Author: Roy Levy,Robert J. Mislevy
Publsiher: CRC Press
Total Pages: 466
Release: 2017-07-28
ISBN: 131535697X
Category: Mathematics
Language: EN, FR, DE, ES & NL

Bayesian Psychometric Modeling Book Excerpt:

A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.

Best Practices in Quantitative Methods

Best Practices in Quantitative Methods
Author: Jason W. Osborne
Publsiher: SAGE
Total Pages: 596
Release: 2008
ISBN: 1412940656
Category: Social Science
Language: EN, FR, DE, ES & NL

Best Practices in Quantitative Methods Book Excerpt:

The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.

Bayesian Econometric Methods

Bayesian Econometric Methods
Author: Joshua Chan,Gary Koop,Dale J. Poirier,Justin L. Tobias
Publsiher: Cambridge University Press
Total Pages: 480
Release: 2019-08-31
ISBN: 1108423388
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Bayesian Econometric Methods Book Excerpt:

Illustrates Bayesian theory and application through a series of exercises in question and answer format.

Quantitative Psychological Research

Quantitative Psychological Research
Author: David Clark-Carter
Publsiher: Routledge
Total Pages: 718
Release: 2018-12-07
ISBN: 1315398133
Category: Psychology
Language: EN, FR, DE, ES & NL

Quantitative Psychological Research Book Excerpt:

Quantitative Psychological Research: The Complete Student's Companion expertly guides the reader through all the stages involved in undertaking quantitative psychological research: designing a study, choosing a sample of people, undertaking the study, analysing the data, and reporting the research. Accessibly written and clearly presented, the book is designed for anyone learning to conduct quantitative psychological research. It covers the full research process, from the original idea to reporting the completed study, emphasising the importance of looking beyond statistical significance in evaluating data. The book provides step-by-step guidance on choosing, interpreting and reporting the appropriate analysis, featuring worked examples and extended calculations as appendices for advanced readers. This edition features new chapters on exploratory factor analysis, logistic regression and Bayesian statistics, and has been thoroughly updated throughout to reflect the latest research practices. Care has been taken to avoid tying the book to any specific statistical software, providing readers with a thorough grounding in the basics no matter which package they go on to use. Whether you’re at the beginning of your undergraduate degree or working towards your masters or doctorate, this book will be invaluable for anyone looking to understand how to conduct quantitative psychological research.