Multiple Indicators Multiple Causes Measurement Error Models

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Generalized Multiple Indicators, Multiple Causes Measurement Error Models

Author : Carmen Tekwe
Publisher :
ISBN 13 :
Total Pages : 285 pages
Book Rating : 4.:/5 (724 download)

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Book Synopsis Generalized Multiple Indicators, Multiple Causes Measurement Error Models by : Carmen Tekwe

Download or read book Generalized Multiple Indicators, Multiple Causes Measurement Error Models written by Carmen Tekwe and published by . This book was released on 2011 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple Indicators, Multiple Causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times however when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error.^The objectives of this dissertation are: (1) to extend the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model, (2) to extend the classical linear MIMIC models to allow both Berkson and classical measurement errors where the distributions of the manifest variables belong in the exponential family, defining the generalized linear MIMIC ME (G-MIMIC ME) model, (3) to develop likelihood based estimation methods using the EM algorithm with Monte Carlo approximation to the integral in the E-step for both the MIMIC ME and G-MIMIC ME models, and (4) to obtain data driven estimates of the variance of the classical measurement error associated with log(DS02), an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure. Participants of the Adult Health Study (AHS) cohort of the atomic bomb data who were exposed to radiation emitted from the detonation of the bombs^with complete data were studied. The defined MIMIC ME model was applied to study the effects of dyslipidemia, a latent construct and the effect of true radiation dose on the physical manifestations of dyslipidemia (triglycerides, bad cholesterol and HDL cholesterol). The G-MIMIC ME model is also applied to study the effect of genetic damage (a latent construct based on exposure to atomic bomb radiation) and the effect of true radiation dose on the physical manifestations of genetic damage (chromosome aberrations (CA) and the amount of hemizygous mutant fractions at the glycopherin A locus in red mature blood cells (GPA)). We find that radiation dose was positively related to triglycerides (p-value

Multiple Indicators, Multiple Causes Measurement Error Models

Author :
Publisher :
ISBN 13 :
Total Pages : 13 pages
Book Rating : 4.:/5 (982 download)

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Book Synopsis Multiple Indicators, Multiple Causes Measurement Error Models by :

Download or read book Multiple Indicators, Multiple Causes Measurement Error Models written by and published by . This book was released on 2014 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methods for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.

Measurement Error Models

Author : Wayne A. Fuller
Publisher : John Wiley & Sons
ISBN 13 : 0470317337
Total Pages : 474 pages
Book Rating : 4.4/5 (73 download)

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Book Synopsis Measurement Error Models by : Wayne A. Fuller

Download or read book Measurement Error Models written by Wayne A. Fuller and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The effort of Professor Fuller is commendable . . . [the book] provides a complete treatment of an important and frequently ignored topic. Those who work with measurement error models will find it valuable. It is the fundamental book on the subject, and statisticians will benefit from adding this book to their collection or to university or departmental libraries." -Biometrics "Given the large and diverse literature on measurement error/errors-in-variables problems, Fuller's book is most welcome. Anyone with an interest in the subject should certainly have this book." -Journal of the American Statistical Association "The author is to be commended for providing a complete presentation of a very important topic. Statisticians working with measurement error problems will benefit from adding this book to their collection." -Technometrics " . . . this book is a remarkable achievement and the product of impressive top-grade scholarly work." -Journal of Applied Econometrics Measurement Error Models offers coverage of estimation for situations where the model variables are observed subject to measurement error. Regression models are included with errors in the variables, latent variable models, and factor models. Results from several areas of application are discussed, including recent results for nonlinear models and for models with unequal variances. The estimation of true values for the fixed model, prediction of true values under the random model, model checks, and the analysis of residuals are addressed, and in addition, procedures are illustrated with data drawn from nearly twenty real data sets.

Multiple Indicators

Author : John L. Sullivan
Publisher : SAGE
ISBN 13 : 9780803913691
Total Pages : 92 pages
Book Rating : 4.9/5 (136 download)

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Book Synopsis Multiple Indicators by : John L. Sullivan

Download or read book Multiple Indicators written by John L. Sullivan and published by SAGE. This book was released on 1979 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: SAGE provides a presentation and critique of the use of multiple measures of theoretical concepts for the assessment of validity (using the multi-trait multi-method matrix) and reliability (using multiple indicators with a path analytic framework).

Measurement Error in Longitudinal Data

Author : Alexandru Cernat
Publisher : Oxford University Press
ISBN 13 : 0192603981
Total Pages : 304 pages
Book Rating : 4.1/5 (926 download)

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Book Synopsis Measurement Error in Longitudinal Data by : Alexandru Cernat

Download or read book Measurement Error in Longitudinal Data written by Alexandru Cernat and published by Oxford University Press. This book was released on 2021-03-18 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organizations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.

Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable

Author : Karl G. Jöreskog
Publisher :
ISBN 13 :
Total Pages : 9 pages
Book Rating : 4.:/5 (254 download)

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Book Synopsis Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable by : Karl G. Jöreskog

Download or read book Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable written by Karl G. Jöreskog and published by . This book was released on 1975 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Structural Equation Modeling

Author : Jichuan Wang
Publisher : John Wiley & Sons
ISBN 13 : 1119422728
Total Pages : 612 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis Structural Equation Modeling by : Jichuan Wang

Download or read book Structural Equation Modeling written by Jichuan Wang and published by John Wiley & Sons. This book was released on 2019-09-17 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using Mplus Focusing on the conceptual and practical aspects of Structural Equation Modeling (SEM), this book demonstrates basic concepts and examples of various SEM models, along with updates on many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA model, item response theory (IRT) model, graded response model (GRM), multiple imputation (MI) of missing values, plausible values of latent variables, moderated mediation model, Bayesian SEM, latent growth modeling (LGM) with individually varying times of observations, dynamic structural equation modeling (DSEM), residual dynamic structural equation modeling (RDSEM), testing measurement invariance of instrument with categorical variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal outcome, manual implementation of the BCH method and the three-step method for mixture modeling, Monte Carlo simulation power analysis for various SEM models, and estimate sample size for latent class analysis (LCA) model. The statistical modeling program Mplus Version 8.2 is featured with all models updated. It provides researchers with a flexible tool that allows them to analyze data with an easy-to-use interface and graphical displays of data and analysis results. Intended as both a teaching resource and a reference guide, and written in non-mathematical terms, Structural Equation Modeling: Applications Using Mplus, 2nd edition provides step-by-step instructions of model specification, estimation, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi-Group Models; Mixture Models; and Power Analysis and Sample Size Estimate for SEM. Presents a useful reference guide for applications of SEM while systematically demonstrating various advanced SEM models Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes Provides step-by-step instructions of model specification and estimation, as well as detailed interpretation of Mplus results using real data sets Introduces different methods for sample size estimate and statistical power analysis for SEM Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to understand the theory and learn how to build their own SEM models using Mplus.

Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable

Author : Karl Gustav Jöreskog
Publisher :
ISBN 13 :
Total Pages : 33 pages
Book Rating : 4.:/5 (7 download)

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Book Synopsis Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable by : Karl Gustav Jöreskog

Download or read book Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable written by Karl Gustav Jöreskog and published by . This book was released on 1973 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Composite-Based Structural Equation Modeling

Author : Jörg Henseler
Publisher : Guilford Publications
ISBN 13 : 1462545610
Total Pages : 387 pages
Book Rating : 4.4/5 (625 download)

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Book Synopsis Composite-Based Structural Equation Modeling by : Jörg Henseler

Download or read book Composite-Based Structural Equation Modeling written by Jörg Henseler and published by Guilford Publications. This book was released on 2020-12-24 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Jörg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importance–performance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides.

Measurement Error in Nonlinear Models

Author : Raymond J. Carroll
Publisher : CRC Press
ISBN 13 : 1420010131
Total Pages : 484 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Measurement Error in Nonlinear Models by : Raymond J. Carroll

Download or read book Measurement Error in Nonlinear Models written by Raymond J. Carroll and published by CRC Press. This book was released on 2006-06-21 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex

Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modelling Technique

Author : Clay Posey
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modelling Technique by : Clay Posey

Download or read book Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modelling Technique written by Clay Posey and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Formative modelling of latent constructs has produced great interest and discussion among scholars in recent years. However, confusion exists surrounding the ability of researchers to validate these models especially with covariance-based structural equation modelling (CB-SEM) techniques. This manuscript helps to clarify these issues and explains how formatively modelled constructs can be assessed rigorously by researchers using CB-SEM capabilities. In particular, we explain and provide an applied example of a mixed-modelling technique termed multiple indicators and multiple causes (MIMIC) models. Using this approach, researchers can assess formatively modelled constructs as the final, distal dependent variable in structural models, which modelling is traditionally impossible due to the mathematical identification rules of CB-SEM. Moreover, we assert that researchers can use MIMIC models to assess the content validity of a set of formative indicators quantitatively -- something considered conventionally only from a qualitative standpoint. Our research example used in this manuscript involving protection-motivated behaviors (PMBs) details the entire process of MIMIC modelling and provides a set of detailed guidelines for researchers to follow when developing new constructs modelled as MIMIC structures.

Measurement Error and Latent Variables in Econometrics

Author : T. Wansbeek
Publisher : North Holland
ISBN 13 :
Total Pages : 464 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Measurement Error and Latent Variables in Econometrics by : T. Wansbeek

Download or read book Measurement Error and Latent Variables in Econometrics written by T. Wansbeek and published by North Holland. This book was released on 2000-12-08 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book first discusses in depth various aspects of the well-known inconsistency that arises when explanatory variables in a linear regression model are measured with error. Despite this inconsistency, the region where the true regression coeffecients lies can sometimes be characterized in a useful way, especially when bounds are known on the measurement error variance but also when such information is absent. Wage discrimination with imperfect productivity measurement is discussed as an important special case. Next, it is shown that the inconsistency is not accidental but fundamental. Due to an identification problem, no consistent estimators may exist at all. Additional information is desirable. This information can be of various types. One type is exact prior knowledge about functions of the parameters. This leads to the CALS estimator. Another major type is in the form of instrumental variables. Many aspects of this are discussed, including heteroskedasticity, combination of data from different sources, construction of instruments from the available data, and the LIML estimator, which is especially relevant when the instruments are weak. The scope is then widened to an embedding of the regression equation with measurement error in a multiple equations setting, leading to the exploratory factor analysis (EFA) model. This marks the step from measurement error to latent variables. Estimation of the EFA model leads to an eigenvalue problem. A variety of models is reviewed that involve eignevalue problems as their common characteristic. EFA is extended to confirmatory factor analysis (CFA) by including restrictions on the parameters of the factor analysis model, and next by relating the factors to background variables. These models are all structural equation models (SEMs), a very general and important class of models, with the LISREL model as its best-known representation, encompassing almost all linear equation systems with latent variables. Estimation of SEMs can be viewed as an application of the generalized method of moments (GMM). GMM in general and for SEM in particular is discussed at great length, including the generality of GMM, optimal weighting, conditional moments, continuous updating, simulation estimation, the link with the method of maximum likelihood, and in particular testing and model evaluation for GMM. The discussion concludes with nonlinear models. The emphasis is on polynomial models and models that are nonlinear due to a filter on the dependent variables, like discrete choice models or models with ordered categorical variables.

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

Author : Joseph F. Hair Jr.
Publisher : Springer Nature
ISBN 13 : 3030805190
Total Pages : 208 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R by : Joseph F. Hair Jr.

Download or read book Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R written by Joseph F. Hair Jr. and published by Springer Nature. This book was released on 2021-11-03 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.

The Multiple Indicator, Multiple Cause Model with Several Latent Variables

Author : Johannes Ledolter
Publisher :
ISBN 13 :
Total Pages : 18 pages
Book Rating : 4.:/5 (253 download)

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Book Synopsis The Multiple Indicator, Multiple Cause Model with Several Latent Variables by : Johannes Ledolter

Download or read book The Multiple Indicator, Multiple Cause Model with Several Latent Variables written by Johannes Ledolter and published by . This book was released on 1976 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:

A Beginner's Guide to Structural Equation Modeling

Author : Randall E. Schumacker
Publisher : Routledge
ISBN 13 : 1317608097
Total Pages : 375 pages
Book Rating : 4.3/5 (176 download)

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Book Synopsis A Beginner's Guide to Structural Equation Modeling by : Randall E. Schumacker

Download or read book A Beginner's Guide to Structural Equation Modeling written by Randall E. Schumacker and published by Routledge. This book was released on 2015-12-22 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Analysis of Measurement Error Models and Applications

Author : Philip J. Brown
Publisher : American Mathematical Soc.
ISBN 13 : 0821851179
Total Pages : 262 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis Statistical Analysis of Measurement Error Models and Applications by : Philip J. Brown

Download or read book Statistical Analysis of Measurement Error Models and Applications written by Philip J. Brown and published by American Mathematical Soc.. This book was released on 1990 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Measurement error models describe functional relationships among variables observed, subject to random errors of measurement. This book treats general aspects of the measurement problem and features a discussion of the history of measurement error models.

Nonparametric Functional Estimation

Author : B. L. S. Prakasa Rao
Publisher : Academic Press
ISBN 13 : 148326923X
Total Pages : 539 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Nonparametric Functional Estimation by : B. L. S. Prakasa Rao

Download or read book Nonparametric Functional Estimation written by B. L. S. Prakasa Rao and published by Academic Press. This book was released on 2014-07-10 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both for specialists in the area as well as for students of statistics taking courses at the postgraduate level. The main emphasis throughout the book is on the discussion of several methods of estimation and on the study of their large sample properties. Chapters are devoted to topics on estimation of density and related functions, the application of density estimation to classification problems, and the different facets of estimation of distribution functions. Statisticians and students of statistics and engineering will find the text very useful.