statistical power analysis with missing data a structural equation modeling approach

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Statistical Power Analysis With Missing Data

Author : Adam Davey
ISBN : 9781135269302
Genre : Psychology
File Size : 46. 91 MB
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Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types of missing data How to increase the power of a design in the presence of missing data, and How to identify the most powerful design in the presence of missing data. Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test one’s ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the book’s application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (SAS, SPSS, Stata, LISREL, AMOS, and MPlus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and links to student versions of software packages are found at www.psypress.com/davey. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions. Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book’s applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.

A Beginner S Guide To Structural Equation Modeling

Author : Randall E. Schumacker
ISBN : 9781317608080
Genre : Psychology
File Size : 35. 55 MB
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Noted for its crystal clear explanations, this book is considered the most comprehensive introductory text to structural equation modeling (SEM). Noted for its thorough review of basic concepts and a wide variety of models, this book better prepares readers to apply SEM to a variety of research questions. Programming details and the use of algebra are kept to a minimum to help readers easily grasp the concepts so they can conduct their own analysis and critique related research. Featuring a greater emphasis on statistical power and model validation than other texts, each chapter features key concepts, examples from various disciplines, tables and figures, a summary, and exercises. Highlights of the extensively revised 4th edition include: -Uses different SEM software (not just Lisrel) including Amos, EQS, LISREL, Mplus, and R to demonstrate applications. -Detailed introduction to the statistical methods related to SEM including correlation, regression, and factor analysis to maximize understanding (Chs. 1 – 6). -The 5 step approach to modeling data (specification, identification, estimation, testing, and modification) is now covered in more detail and prior to the modeling chapters to provide a more coherent view of how to create models and interpret results (ch. 7). -More discussion of hypothesis testing, power, sampling, effect sizes, and model fit, critical topics for beginning modelers (ch. 7). - Each model chapter now focuses on one technique to enhance understanding by providing more description, assumptions, and interpretation of results, and an exercise related to analysis and output (Chs. 8 -15). -The use of SPSS AMOS diagrams to describe the theoretical models. -The key features of each of the software packages (Ch. 1). -Guidelines for reporting SEM research (Ch. 16). -www.routledge.com/9781138811935 which provides access to data sets that can be used with any program, links to other SEM examples, related readings, and journal articles, and more. Reorganized, the new edition begins with a more detailed introduction to SEM including the various software packages available, followed by chapters on data entry and editing, and correlation which is critical to understanding how missing data, non-normality, measurement, and restriction of range in scores affects SEM analysis. Multiple regression, path, and factor models are then reviewed and exploratory and confirmatory factor analysis is introduced. These chapters demonstrate how observed variables share variance in defining a latent variables and introduce how measurement error can be removed from observed variables. Chapter 7 details the 5 SEM modeling steps including model specification, identification, estimation, testing, and modification along with a discussion of hypothesis testing and the related issues of power, and sample and effect sizes.Chapters 8 to 15 provide comprehensive introductions to different SEM models including Multiple Group, Second-Order CFA, Dynamic Factor, Multiple-Indicator Multiple-Cause, Mixed Variable and Mixture, Multi-Level, Latent Growth, and SEM Interaction Models. Each of the 5 SEM modeling steps is explained for each model along with an application. Chapter exercises provide practice with and enhance understanding of the analysis of each model. The book concludes with a review of SEM guidelines for reporting research. Designed for introductory graduate courses in structural equation modeling, factor analysis, advanced, multivariate, or applied statistics, quantitative techniques, or statistics II taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines. Prerequisites include an introduction to intermediate statistics that covers correlation and regression principles.

Genetic Counseling Research A Practical Guide

Author : Ian MacFarlane
ISBN : 9780199359110
Genre : Medical
File Size : 72. 30 MB
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Genetic Counseling Research: A Practical Guide is the first text devoted to research methodology in genetic counseling. This text offers step-by-step guidance for conducting research, from the development of a question to the publication of findings. Genetic counseling examples, user-friendly worksheets, and practical tips guide readers through the research and publication processes. With a highly accessible, pedagogical approach, this book will help promote quality research by genetic counselors and research supervisors--and in turn, increase the knowledge base for genetic counseling practice, other aspects of genetic counseling service delivery, and professional education. It will be an invaluable resource to the next generation of genetic counseling and its surrounding disciplines.

Structural Equation Modeling

Author : David Kaplan
ISBN : UOM:39015064117479
Genre : Psychology
File Size : 88. 82 MB
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"This is a thorough and sophisticated treatment of structural equation modeling (SEM). The book assumes a strong background in statistics and matrix algebra. The author clearly is knowledgeable and has put much thought into the pros and cons of standard applications of SEM. The chapters on multilevel and growth modeling are an excellent feature of the book." --Rick H. Hoyle, University of Kentucky Through the use of detailed, empirical examples, this exciting book presents an advanced treatment of the foundations of structural equation modeling (SEM) and demonstrates how SEM can provide a unique lens on problems in the social and behavioral sciences. The author begins with an introduction to recursive and non-recursive models, estimation, testing, and the problem of measurement in observed variables. Kaplan then explores the issue of group differences in structural models, statistical assumptions in structural modeling (from sampling to missing data and specification error), the assessment of statistical power and model modification in the context of model evaluation, and SEM applied to complex data structures such as those obtained from clustered random sampling. The book concludes with a discussion of recent developments in latent variable growth curve modeling and a critique of the conventional practice of structural modeling in light of recent developments in econometric modeling. Features/Benefits: - Shows how SEM can be used to answer substantive questions by weaving a small set of substantive examples throughout the book - Explains recent developments in structural equation modeling applied to complex sampling, such as multilevel SEM and latent variable growth - Provides a critique of the standard practice of structural equation modeling throughout the book and discusses an alternative approach in light of recent developments in econometric modeling.

Essential Statistical Methods For Medical Statistics

Author : J. Philip Miller
ISBN : 0444537384
Genre : Mathematics
File Size : 36. 26 MB
Format : PDF
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Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas. · Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis

Statistical Strategies For Small Sample Research

Author : Rick H. Hoyle
ISBN : 0761908862
Genre : Reference
File Size : 43. 60 MB
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This book provides encouragement and strategies for researchers who routinely address research questions using data from small samples. Chapters cover such topics as: using multiple imputation software with small sets; computing and combining effect sizes; bootstrap hypothesis testing; application of latent variable modeling; time-series data from small numbers of individuals; and sample size, reliability and tests of statistical mediation.

An Introduction To Latent Variable Growth Curve Modeling

Author : Terry E. Duncan
ISBN : 9781135601249
Genre : Business & Economics
File Size : 25. 73 MB
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This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples. Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research. This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

The Oxford Handbook Of Research Strategies For Clinical Psychology

Author : Jonathan S. Comer
ISBN : 9780199793655
Genre : Psychology
File Size : 84. 39 MB
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Mental health problems impose a staggering worldwide public health burden. Regrettably, whereas many sciences have been progressing for centuries (e.g., biology, chemistry) it is only recently that the strategies of science have been applied to the field of clinical psychology. At this relatively early stage in the science of clinical psychology, the majority of work is ahead of us, and as such the prepared investigator must be familiar with the full portfolio of modern research strategies-a set of 'directions' for getting from 'here' to 'there.' To continue to move the science of clinical psychology forward, investigators benefit when they systematically rely on research strategy "routes" that achieve favorable balances between scientific rigor and clinical relevance. With this need in mind, The Oxford Handbook of Research Strategies for Clinical Psychology has recruited some of the field's foremost experts to explicate the essential research strategies currently used across the modern clinical psychology landscape that maximize both precision and significance. Chapters in this volume address design, measurement, and analytic strategies for clinical psychology, including comprehensive coverage of: - effective laboratory methods in experimental psychopathology, single-case experimental designs, small pilot trials, the randomized controlled trial, adaptive and modular treatment designs, and dissemination methods and models - change measurement, observational coding, measurement of process variables across treatment, structural and functional brain imagining, and experience sampling data collection methods - statistical power, correlation and regression, randomized clinical trial data analysis, conventions in mediation and moderation analysis, structural equation modeling, meta-analytic techniques, item-response theory, and the appropriate handling of missing data. The book concludes with an integrative summary of research strategies addressed across the volume, and guidelines for future directions in research methodology, design, and analysis that will keep our young science moving forward in a manner that maximizes scientific rigor and clinical relevance.

Missing Data

Author : John W. Graham
ISBN : 9781461440185
Genre : Social Science
File Size : 75. 90 MB
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Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking. The objective of Missing Data: Analysis and Design is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power. Missing Data: Analysis and Design contains essential information for both beginners and advanced readers. For researchers with limited missing data analysis experience, this book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data; provides clear, step-by-step instructions for performing state-of-the-art multiple imputation analyses; and offers practical advice, based on over 20 years' experience, for avoiding and troubleshooting problems. For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effective planned missing data designs are provided. The author lays out missing data theory in a plain English style that is accessible and precise. Most analysis described in the book are conducted using the well-known statistical software packages SAS and SPSS, supplemented by Norm 2.03 and associated Java-based automation utilities. A related web site contains free downloads of the supplementary software, as well as sample empirical data sets and a variety of practical exercises described in the book to enhance and reinforce the reader’s learning experience. Missing Data: Analysis and Design and its web site work together to enable beginners to gain confidence in their ability to conduct missing data analysis, and more advanced readers to expand their skill set.

Handbook Of Complementary Methods In Education Research

Author : Judith L. Green
ISBN : UCSC:32106018589116
Genre : Education
File Size : 28. 30 MB
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Published for the American Educational Research Association by Routledge. The Handbook of Complementary Methods in Education Research is a successor volume to AERA's earlier and highly acclaimed editions of Complementary Methods for Research in Education. More than any book to date (including its predecessors), this new volume brings together the wide range of research methods used to study education and makes the logic of inquiry for each method clear and accessible. Each method is described in detail, including its history, its research design, the questions that it addresses, ways of using the method, and ways of analyzing and reporting outcomes. Key features of this indispensable book include the following: Foundations Section-Part I is unique among research books. Its three chapters examine common philosophical, epistemological, and ethical issues facing researchers from all traditions, and frames ways of understanding the similarities and differences among traditions. Together they provide a tripartite lens through which to view and compare all research methods. Comprehensive Coverage-Part II (the heart of the book) presents 35 chapters on research design and analysis. Each chapter includes a brief historical overview of the research tradition, examines the questions that it addresses, and presents an example of how the approach can be used. Programs of Research-Part III examines how research programs connected to eight specific lines of inquiry have evolved over time. These chapters examine phenomena such as classroom interaction; language research; issues of race, culture, and difference; policy analysis; program evaluation; student learning; and teacher education. Complementary Methods-As the title suggests, a central mission of this book is to explore the compatibility of different research methods. Which methods can be productively brought together and for what purposes? How and on what scale can they be made compatible and what phenomena are they best suited to explore? Flexibility-The chapters in Parts II and III are largely independent. Therefore, selected portions of the book can be used in courses devoted to specific research methods and perspectives or to particular areas of education. Likewise, established researchers interested in acquiring new techniques or greater expertise in a given methodology will find this an indispensable reference volume. This handbook is appropriate for any of the following audiences: faculty teaching and graduate students studying education research, education researchers and other scholars seeking an accessible overview of state-of-the-art knowledge about specific methods, policy analysts and other professionals needing to better understand research methods, and academic and research libraries serving these audiences.

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