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Author : Paul W. Holland
Publisher : Unknown
Page : 50 pages
File Size : 55,5 Mb
Release : 1988
Category : Causation
ISBN : OCLC:21853767
Author : Rick H. Hoyle
Publisher : Guilford Publications
Page : 801 pages
File Size : 54,7 Mb
Release : 2023-02-17
Category : Business & Economics
ISBN : 9781462544646
"This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--
Author : Bill Shipley
Publisher : Cambridge University Press
Page : 315 pages
File Size : 42,6 Mb
Release : 2016-04-18
Category : Mathematics
ISBN : 9781107442597
A complete user's guide to structural equations explaining the underlying principals and practical implementation of these methods.
Author : Bill Shipley
Publisher : Cambridge University Press
Page : 330 pages
File Size : 52,6 Mb
Release : 2002-08
Category : Mathematics
ISBN : 0521529212
This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.
Author : Maia Berkane
Publisher : Springer Science & Business Media
Page : 285 pages
File Size : 51,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461218425
This volume gathers refereed papers presented at the 1994 UCLA conference on "La tent Variable Modeling and Application to Causality. " The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data.
Author : Kees van Montfort,Johan Oud,Albert Satorra
Publisher : Springer Science & Business Media
Page : 364 pages
File Size : 40,9 Mb
Release : 2004-03-31
Category : Psychology
ISBN : 9781402019586
After Karl Jöreskog's first presentation in 1970, Structural Equation Modelling or SEM has become a main statistical tool in many fields of science. It is the standard approach of factor analytic and causal modelling in such diverse fields as sociology, education, psychology, economics, management and medical sciences. In addition to an extension of its application area, Structural Equation Modelling also features a continual renewal and extension of its theoretical background. The sixteen contributions to this book, written by experts from many countries, present important new developments and interesting applications in Structural Equation Modelling. The book addresses methodologists and statisticians professionally dealing with Structural Equation Modelling to enhance their knowledge of the type of models covered and the technical problems involved in their formulation. In addition, the book offers applied researchers new ideas about the use of Structural Equation Modeling in solving their problems. Finally, methodologists, mathematicians and applied researchers alike are addressed, who simply want to update their knowledge of recent approaches in data analysis and mathematical modelling.
Author : Delbert C. Miller,Neil J. Salkind
Publisher : SAGE
Page : 812 pages
File Size : 49,5 Mb
Release : 2002-01-16
Category : Psychology
ISBN : 0761920463
"If a student researcher had only one handbook on their bookshelf, Miller and Salkind′s Handbook would certainly have to be it. With the updated material, the addition of the section on ethical issues (which is so well done that I′m recommending it to the departmental representative to the university IRB), and a new Part 4 on "Qualitative Methods", the new Handbook is an indispensable resource for researchers." --Dan Cover, Department of Sociology, Furman University " I have observed that most instructors want to teach methodology "their way" to imbue the course with their own approach; Miller-Salkind allows one to do this easily. The book is both conceptually strong (e.g., very good coverage of epistemology, research design and statistics) and at the same time provides a wealth of practical knowledge (scales, indices, professional organizations, computer applications, etc.) In addition, it covers the waterfront of methodology." --Michael L. Vasu, Director of Information Technology, North Carolina State University "A unique and excellent reference tool for all social science researchers, and a good textbook for graduate students and senior year undergraduate classes. These students are about to enter the real life of research, and need a handy and comprehensive tool as a starting point that offers shortcuts for getting into real research projects. For a small project, the book offers enough information to get the project started. For big projects, the book is ideal for information on where to look for things and examples." --Jianhong Liu, Department of Sociology, Rhode Island College The book considered a "necessity" by many social science researchers and their students has been revised and updated while retaining the features that made it so useful. The emphasis in this new edition is on the tools graduate students and more advanced researchers need to conduct high quality research. Features/Benefits: Provides step-by-step instruction for students′ research training by beginning with how to find a creative idea, a middle-range theory, and initial hypothesis and proceeds through design, proposal, collection and analysis of data followed by writing, reporting and publication Section on scales and indices are organized so that readers can quickly locate and find the type of scale or index in which they may be interested All sections are now followed by useful and well-considered reference sections so that readers can read more about each topic Includes updated coverage on new scales, internal and external validity, and new analytic techniques with extensive references on each Presents extensive coverage of how to prepare manuscripts for publication, including a list of all journals covered by Sociological Abstracts along with the editorial office address and URL for each entry Discusses the importance of policy research with presentation and discussion of specific models as an adjunct to both applied and basic research techniques Provides extensive coverage of funding opportunities including those offered by the National Institutes of Health, the National Science Foundation, and a directory of private funding sources including relevant contact information New to this edition: New Part 4 by John Creswell and Ray Maietta provides a comprehensive introduction to qualitative methods including a review of existing computer applications for collecting and analyzing data New and more current reviews and commentaries have replaced dated or no longer relevant excerpts Thousands of new references on the assessment of important sociological variables as well as references to such topics as statistical analysis, computer applications, and specific topics Thoroughly updated information on the use of computers and online research techniques, including beginning and intermediate material about the Internet and its use by the modern research scientist Coherent and thoughtful review of the most popular statistical analysis software packages New guidelines and discussion of ethical practices in social and behavioral science research, including extensive coverage of institutional review board procedures and activities Expansion of social indicators to include international coverage Plus, there is an extensive and well-organized table of contents with four levels of headings; and, for the first time in the history of the book, a comprehensive index.
Author : Donald W. K. Andrews,James H. Stock
Publisher : Cambridge University Press
Page : 589 pages
File Size : 42,9 Mb
Release : 2005-07-04
Category : Business & Economics
ISBN : 9781139444606
This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.
Author : Hector Geffner,Rina Dechter,Joseph Halpern
Publisher : Morgan & Claypool
Page : 946 pages
File Size : 47,6 Mb
Release : 2022-03-10
Category : Computers
ISBN : 9781450395892
Professor Judea Pearl won the 2011 Turing Award “for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.” This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988–2001), and causality, recent period (2002–2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl’s work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.
Author : Dawn Iacobucci
Publisher : SAGE
Page : 105 pages
File Size : 44,5 Mb
Release : 2008-04
Category : Mathematics
ISBN : 9781412925693
Explores even the fundamental assumptions underlying mediation analysis
Author : Guido W. Imbens,Donald B. Rubin
Publisher : Cambridge University Press
Page : 647 pages
File Size : 42,7 Mb
Release : 2015-04-06
Category : Business & Economics
ISBN : 9780521885881
This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Author : Hua He,Pan Wu,Ding-Geng (Din) Chen
Publisher : Springer
Page : 321 pages
File Size : 49,8 Mb
Release : 2016-10-26
Category : Medical
ISBN : 9783319412597
This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in statistics, biostatistics, and computational biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
Author : Jonas Peters,Dominik Janzing,Bernhard Scholkopf
Publisher : MIT Press
Page : 289 pages
File Size : 48,8 Mb
Release : 2017-11-29
Category : Computers
ISBN : 9780262037310
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Author : Phyllis Illari,Federica Russo,Jon Williamson
Publisher : OUP Oxford
Page : 952 pages
File Size : 49,8 Mb
Release : 2011-03-17
Category : Science
ISBN : 9780191060328
There is a need for integrated thinking about causality, probability and mechanisms in scientific methodology. Causality and probability are long-established central concepts in the sciences, with a corresponding philosophical literature examining their problems. On the other hand, the philosophical literature examining mechanisms is not long-established, and there is no clear idea of how mechanisms relate to causality and probability. But we need some idea if we are to understand causal inference in the sciences: a panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, routinely make use of probability, statistics, theory and mechanisms to infer causal relationships. These disciplines have developed very different methods, where causality and probability often seem to have different understandings, and where the mechanisms involved often look very different. This variegated situation raises the question of whether the different sciences are really using different concepts, or whether progress in understanding the tools of causal inference in some sciences can lead to progress in other sciences. The book tackles these questions as well as others concerning the use of causality in the sciences.
Author : José R. Zubizarreta,Elizabeth A. Stuart,Dylan S. Small,Paul R. Rosenbaum
Publisher : CRC Press
Page : 634 pages
File Size : 46,9 Mb
Release : 2023-04-11
Category : Mathematics
ISBN : 9781000850819
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.