Bayesian Modeling Of Single Case Research Design Data In The Presence Of Autocorrelation

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Bayesian Modeling of Single Case Research Design Data in the Presence of Autocorrelation

Author : Erkan Sayilir
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Bayesian Modeling of Single Case Research Design Data in the Presence of Autocorrelation by : Erkan Sayilir

Download or read book Bayesian Modeling of Single Case Research Design Data in the Presence of Autocorrelation written by Erkan Sayilir and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current study, I intended to simulate single case research design (SCRD) data to investigate the impact of the presence of autocorrelation on analysis of SCRD for Bayesian method under a variety of simulation conditions. The purpose of this study is to analyze the results of data obtained in the context of an individual single-case study, for a variety of simulation conditions such as varying sample sizes, varying numbers of measurement observations, and the presence of varied levels of autocorrelation. Then I compared performance of both methods using Bias, Percentage Bias (PB), Mean Square Error (MSE), Power, and DIC statistics. Analysis of variance (ANOVA) was implemented to analyze the summary statistics and compare performance of both approaches. Another goal of this study was to develop and fit several Bayesian models from simple model to a complex model to evaluate the significance of the model parameters in a SCRD in the presence of autocorrelation. Also, the effect of using a scaled-inverse Wishart distribution over parameter estimation as a prior distribution for covariance matrix of parameters was explored. It was found that the performance of the Bayesian models in this study was satisfactory, except that of the Scaled inverse-Wishart distribution with autocorrelated error (Model 3B). It produced extremely large biased estimates, percentage bias (PB), standardized bias (SB) and mean square error (MSE) for most of the simulation conditions. On the other hand, the Scaled-inverse Wishart distribution performed well if serial dependency was not taken into account across simulation conditions. Also, the SB was around 5%, bias still had an adverse effect on the coverage rates of the models. A poor convergence rate was observed for SIW regardless of modeling autocorrelation and convergence was not reached for Random Slopes Model (RSM) for some of the simulation conditions when autocorrelation was considered. Although mean standardized biases of the models were around 5%, it is likely that having larger standard error for the models had effects on the power and the coverage rates of the models. In most cases, coverage rate under 95% were observed, which means under-coverage and indicates higher Type- I error rate should be expected.

Bayesian Estimates of Autocorrelations in Single-Case Designs

Author : William R. Shadish
Publisher :
ISBN 13 :
Total Pages : 11 pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis Bayesian Estimates of Autocorrelations in Single-Case Designs by : William R. Shadish

Download or read book Bayesian Estimates of Autocorrelations in Single-Case Designs written by William R. Shadish and published by . This book was released on 2012 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers in the single-case design tradition have debated the size and importance of the observed autocorrelations in those designs. All of the past estimates of the autocorrelation in that literature have taken the observed autocorrelation estimates as the data to be used in the debate. However, estimates of the autocorrelation are subject to great sampling error when the design has a small number of time points, as is typically the situation in single-case designs. Thus, a given observed autocorrelation may greatly over- or underestimate the corresponding population parameter. This article presents Bayesian estimates of the autocorrelation that greatly reduce the role of sampling error, as compared to past estimators. Simpler empirical Bayes estimates are presented first, in order to illustrate the fundamental notions of autocorrelation sampling error and shrinkage, followed by fully Bayesian estimates, and the difference between the two is explained. Scripts to do the analyses are available as supplemental materials. The analyses are illustrated using two examples from the single-case design literature. Bayesian estimation warrants wider use, not only in debates about the size of autocorrelations, but also in statistical methods that require an independent estimate of the autocorrelation to analyze the data. (Contains 2 figures and 1 table.).

Single Case Experimental Designs

Author : David H. Barlow
Publisher : Allyn & Bacon
ISBN 13 : 9780205142712
Total Pages : 0 pages
Book Rating : 4.1/5 (427 download)

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Book Synopsis Single Case Experimental Designs by : David H. Barlow

Download or read book Single Case Experimental Designs written by David H. Barlow and published by Allyn & Bacon. This book was released on 1984 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Case Studies in Bayesian Statistical Modelling and Analysis

Author : Clair L. Alston
Publisher : John Wiley & Sons
ISBN 13 : 1118394321
Total Pages : 411 pages
Book Rating : 4.1/5 (183 download)

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Book Synopsis Case Studies in Bayesian Statistical Modelling and Analysis by : Clair L. Alston

Download or read book Case Studies in Bayesian Statistical Modelling and Analysis written by Clair L. Alston and published by John Wiley & Sons. This book was released on 2012-10-10 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.

Frontiers of Statistical Decision Making and Bayesian Analysis

Author : Ming-Hui Chen
Publisher : Springer Science & Business Media
ISBN 13 : 1441969446
Total Pages : 631 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Frontiers of Statistical Decision Making and Bayesian Analysis by : Ming-Hui Chen

Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

The Routledge Reviewer’s Guide to Mixed Methods Analysis

Author : Anthony J. Onwuegbuzie
Publisher : Routledge
ISBN 13 : 1351395521
Total Pages : 407 pages
Book Rating : 4.3/5 (513 download)

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Book Synopsis The Routledge Reviewer’s Guide to Mixed Methods Analysis by : Anthony J. Onwuegbuzie

Download or read book The Routledge Reviewer’s Guide to Mixed Methods Analysis written by Anthony J. Onwuegbuzie and published by Routledge. This book was released on 2021-07-12 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Routledge Reviewer’s Guide to Mixed Methods Analysis is a groundbreaking edited book – the first devoted solely to mixed methods research analyses, or mixed analyses. Each of the 30 seminal chapters, authored by internationally renowned scholars, provides a simple and practical introduction to a method of mixed analysis. Each chapter demonstrates "how to conduct the analysis" in easy-to-understand language. Many of the chapters present new topics that have never been written before, and all chapters offer cutting-edge approaches to analysis. The book contains the following four sections: Part I Quantitative Approaches to Qualitative Data (e.g., factor analysis of text, multidimensional scaling of qualitative data); Part II Qualitative Approaches to Quantitative Data (e.g., qualitizing data, mixed methodological discourse analysis); Part III "Inherently" Mixed Analysis Approaches (e.g., qualitative comparative analysis, mixed methods social network analysis, social media analytics as mixed analysis, GIS as mixed analysis); and Part IV Use of Software for Mixed Data Analysis (e.g., QDA Miner, WordStat, MAXQDA, NVivo, SPSS). The audience for this book includes (a) researchers, evaluators, and practitioners who conduct a variety of research projects and who are interested in using innovative analyses that will allow them to extract more from their data; (b) academics, including faculty who would use this book in their scholarship, as well as in their graduate-level courses, and graduate students who need access to a comprehensive set of mixed analysis tools for their dissertations/theses and other research assignments and projects; and (c) computer-assisted data analysis software developers who are seeking additional mixed analyses to include within their software programs. Chapter 24 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.

Bayesian Estimation and Experimental Design in Linear Regression Models

Author : Jürgen Pilz
Publisher :
ISBN 13 :
Total Pages : 316 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Bayesian Estimation and Experimental Design in Linear Regression Models by : Jürgen Pilz

Download or read book Bayesian Estimation and Experimental Design in Linear Regression Models written by Jürgen Pilz and published by . This book was released on 1991-07-09 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a clear treatment of the design and analysis of linear regression experiments in the presence of prior knowledge about the model parameters. Develops a unified approach to estimation and design; provides a Bayesian alternative to the least squares estimator; and indicates methods for the construction of optimal designs for the Bayes estimator. Material is also applicable to some well-known estimators using prior knowledge that is not available in the form of a prior distribution for the model parameters; such as mixed linear, minimax linear and ridge-type estimators.

Missing Data in Longitudinal Studies

Author : Michael J. Daniels
Publisher : Chapman and Hall/CRC
ISBN 13 : 9781584886099
Total Pages : 328 pages
Book Rating : 4.8/5 (86 download)

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Book Synopsis Missing Data in Longitudinal Studies by : Michael J. Daniels

Download or read book Missing Data in Longitudinal Studies written by Michael J. Daniels and published by Chapman and Hall/CRC. This book was released on 2008-03-11 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing from the authors’ own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ several data sets throughout that cover a range of study designs, variable types, and missing data issues. The book first reviews modern approaches to formulate and interpret regression models for longitudinal data. It then discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The book carefully describes the assumptions needed to make inferences about a full-data distribution from incompletely observed data. For settings with ignorable dropout, it emphasizes the importance of covariance models for inference about the mean while for nonignorable dropout, the book studies a variety of models in detail. It concludes with three case studies that highlight important features of the Bayesian approach for handling nonignorable missingness. With suggestions for further reading at the end of most chapters as well as many applications to the health sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.

Fully Bayesian Estimation of Data from Single Case Designs

Author : David Rindskopf
Publisher :
ISBN 13 :
Total Pages : 4 pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis Fully Bayesian Estimation of Data from Single Case Designs by : David Rindskopf

Download or read book Fully Bayesian Estimation of Data from Single Case Designs written by David Rindskopf and published by . This book was released on 2013 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: Single case designs (SCDs) generally consist of a small number of short time series in two or more phases. The analysis of SCDs statistically fits in the framework of a multilevel model, or hierarchical model. The usual analysis does not take into account the uncertainty in the estimation of the random effects. This not only has an effect on the estimates of the fixed effects, but also on their standard error. This research investigates this phenomenon on a selected SCD, and compares the results with the analyses of other people on the panel. The authors presume the results, which are not presented, will show the advantages of fully Bayesian estimates.

Bayesian Structural Equation Modeling

Author : Sarah Depaoli
Publisher : Guilford Publications
ISBN 13 : 1462547745
Total Pages : 549 pages
Book Rating : 4.4/5 (625 download)

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Book Synopsis Bayesian Structural Equation Modeling by : Sarah Depaoli

Download or read book Bayesian Structural Equation Modeling written by Sarah Depaoli and published by Guilford Publications. This book was released on 2021-08-16 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.

Research Synthesis and Meta-Analysis

Author : Harris Cooper
Publisher : SAGE Publications
ISBN 13 : 1483347044
Total Pages : 189 pages
Book Rating : 4.4/5 (833 download)

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Book Synopsis Research Synthesis and Meta-Analysis by : Harris Cooper

Download or read book Research Synthesis and Meta-Analysis written by Harris Cooper and published by SAGE Publications. This book was released on 2015-12-24 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Fifth Edition of Harris Cooper′s bestselling text offers practical advice on how to conduct a synthesis of research in the social, behavioral, and health sciences. The book is written in plain language with four running examples drawn from psychology, education, and health science. With ample coverage of literature searching and the technical aspects of meta-analysis, this one-of-a-kind book applies the basic principles of sound data gathering to the task of producing a comprehensive assessment of existing research.

Methodological Quality of Interventions in Psychology

Author : Salvador Chacón-Moscoso
Publisher : Frontiers Media SA
ISBN 13 : 2889452492
Total Pages : 120 pages
Book Rating : 4.8/5 (894 download)

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Book Synopsis Methodological Quality of Interventions in Psychology by : Salvador Chacón-Moscoso

Download or read book Methodological Quality of Interventions in Psychology written by Salvador Chacón-Moscoso and published by Frontiers Media SA. This book was released on 2017-08-16 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evaluations of intervention programs seek to present high-quality design, measures and data to assess their merit and worth. While evaluations differ in their purpose, theoretical framework and methodology, their collective aim is to obtain relevant and meaningful information to inform practice, research, and policy. As such, evaluation findings serve to build a body of knowledge on effective approaches to promote designated psychological outcomes, critical to an individual's overall health and well-being. However, as examined in this e-book, methodological weaknesses directly limit the potential of evaluations of intervention programs. As discussed by Chacón-Moscoso and Sanduvete-Chaves, methodological weaknesses can be attributed to how to define and measure methodological quality and the contextual dependency of instruments designed to measure this quality. In response, this e-book provides a collection of studies on methodological approaches to promote the quality of psychological interventions. Specifically, 10 original works published in the Research Topic Methodological Quality of Interventions in Psychology are included. The papers are organized into two chapters. Concretely, Chapter 1 includes studies pertaining to methodological approaches to enhance the quality of psychological intervention, being context independent solutions. Furthermore, Chapter 2 presents original work in different areas (health, education, sport and social welfare) where methodological quality has been better assessed. Collectively, the papers in this e-book serve to expand the awareness of practitioners and researchers interested in psychological interventions of the critical role of methodological quality in this work. This research was funded by the projects 1150096 (Chilean National Fund of Scientific and Technological Development, FONDECYT); and PSI2015-71947-REDT (Spain’s Ministry of Economy and Competitiveness).

Optimal Bayesian Experimental Design in the Presence of Model Error

Author :
Publisher :
ISBN 13 :
Total Pages : 90 pages
Book Rating : 4.:/5 (911 download)

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Book Synopsis Optimal Bayesian Experimental Design in the Presence of Model Error by :

Download or read book Optimal Bayesian Experimental Design in the Presence of Model Error written by and published by . This book was released on 2015 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction. We propose an information theoretic framework and algorithms for robust optimal experimental design with simulation-based models, with the goal of maximizing information gain in targeted subsets of model parameters, particularly in situations where experiments are costly. Our framework employs a Bayesian statistical setting, which naturally incorporates heterogeneous sources of information. An objective function reflects expected information gain from proposed experimental designs. Monte Carlo sampling is used to evaluate the expected information gain, and stochastic approximation algorithms make optimization feasible for computationally intensive and high-dimensional problems. A key aspect of our framework is the introduction of model calibration discrepancy terms that are used to "relax" the model so that proposed optimal experiments are more robust to model error or inadequacy. We illustrate the approach via several model problems and misspecification scenarios. In particular, we show how optimal designs are modified by allowing for model error, and we evaluate the performance of various designs by simulating "real-world" data from models not considered explicitly in the optimization objective.

Forecasting with Univariate Box - Jenkins Models

Author : Alan Pankratz
Publisher : John Wiley & Sons
ISBN 13 :
Total Pages : 584 pages
Book Rating : 4.:/5 (319 download)

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Book Synopsis Forecasting with Univariate Box - Jenkins Models by : Alan Pankratz

Download or read book Forecasting with Univariate Box - Jenkins Models written by Alan Pankratz and published by John Wiley & Sons. This book was released on 1983-08-30 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using real data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another.

Spatial Analysis Methods and Practice

Author : George Grekousis
Publisher : Cambridge University Press
ISBN 13 : 1108498981
Total Pages : 535 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Spatial Analysis Methods and Practice by : George Grekousis

Download or read book Spatial Analysis Methods and Practice written by George Grekousis and published by Cambridge University Press. This book was released on 2020-06-11 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results.

Bayesian Modeling of Spatio-Temporal Data with R

Author : Sujit Sahu
Publisher : CRC Press
ISBN 13 : 1000543692
Total Pages : 385 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Bayesian Modeling of Spatio-Temporal Data with R by : Sujit Sahu

Download or read book Bayesian Modeling of Spatio-Temporal Data with R written by Sujit Sahu and published by CRC Press. This book was released on 2022-02-23 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modelling, which aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modelling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems. Key features of the book: • Accessible detailed discussion of a majority of all aspects of Bayesian methods and computations with worked examples, numerical illustrations and exercises • A spatial statistics jargon buster chapter that enables the reader to build up a vocabulary without getting clouded in modeling and technicalities • Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr, allowing the reader to use well-known packages and platforms, such as rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, CARBayesST, etc • Included are R code notes detailing the algorithms used to produce all the tables and figures, with data and code available via an online supplement • Two dedicated chapters discuss practical examples of spatio-temporal modeling of point referenced and areal unit data • Throughout, the emphasis has been on validating models by splitting data into test and training sets following on the philosophy of machine learning and data science This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience of students and researchers, from mathematicians and statisticians to practitioners in the applied sciences. It presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. It does not compromise on rigour, as it presents the underlying theories of Bayesian inference and computation in standalone chapters, which would be appeal those interested in the theoretical details. By avoiding hard core mathematics and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.