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The Independent Variable Problem by Katharina Kunißen Pdf
This open access publication deals with the operationalisation of the welfare state as an independent variable. To study how welfare states affect social inequality, individual behaviour, attitudes and more in different countries, an empirical operationalisation of the welfare state or specific elements of social policy is required. However, this operationalisation is fraught with some important problems. These problems essentially relate to one point: while there are a large number of contributions dealing with the measurement of differences between welfare states per se and as a dependent variable, there is a lack of feasible recommendations for a standardised operationalisation of welfare stateness as an independent variable. So far, there has been no systematic investigation of how such different approaches may affect the results and their comparability. Also missing is an in-depth conceptual discussion of which features of the welfare state are particularly relevant for explaining certain effects. This book fills both gaps. First, it exposes the pitfalls of existing approaches and shows how much empirical results can vary depending on the operationalisation chosen. Second, it proposes a framework for a standardised conceptualisation and operationalisation of social policies as independent variables that constrains operational decisions in a theoretically meaningful way.
Language Complexity as an Evolving Variable by Geoffrey Sampson,David Gil,Peter Trudgill Pdf
This fascinating book challenges the idea that languages are equally complex. Eighteen scholars look at evidence from a wide range of times and places. They consider the links between linguistic structure and change and social complexity. Their conclusions challenge conventional ideas about the nature of language and contemporary theory.
Author : John C. Loehlin,A. Alexander Beaujean Publisher : Taylor & Francis Page : 376 pages File Size : 46,9 Mb Release : 2016-12-07 Category : Psychology ISBN : 9781317285281
Latent Variable Models by John C. Loehlin,A. Alexander Beaujean Pdf
Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing path diagrams to explain the relationships in the models. This approach helps less mathematically-inclined readers to grasp the underlying relations among path analysis, factor analysis, and structural equation modeling, and to set up and carry out such analyses. This revised and expanded fifth edition again contains key chapters on path analysis, structural equation models, and exploratory factor analysis. In addition, it contains new material on composite reliability, models with categorical data, the minimum average partial procedure, bi-factor models, and communicating about latent variable models. The informal writing style and the numerous illustrative examples make the book accessible to readers of varying backgrounds. Notes at the end of each chapter expand the discussion and provide additional technical detail and references. Moreover, most chapters contain an extended example in which the authors work through one of the chapter’s examples in detail to aid readers in conducting similar analyses with their own data. The book and accompanying website provide all of the data for the book’s examples as well as syntax from latent variable programs so readers can replicate the analyses. The book can be used with any of a variety of computer programs, but special attention is paid to LISREL and R. An important resource for advanced students and researchers in numerous disciplines in the behavioral sciences, education, business, and health sciences, Latent Variable Models is a practical and readable reference for those seeking to understand or conduct an analysis using latent variables.
Latent Variable and Latent Structure Models by George A. Marcoulides,Irini Moustaki Pdf
This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two traditions: psychometrics and structural equation modeling. Latent Variable and Latent Structure Models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come. This volume will be of interest to researchers and practitioners from a wide variety of disciplines, including biology, business, economics, education, medicine, psychology, sociology, and other social and behavioral sciences. A working knowledge of basic multivariate statistics and measurement theory is assumed.
Variable Takt Principle by Peter Bebersdorf,Arnd Huchzermeier Pdf
This book describes how the implementation of variable rate launching in the context of a mixed-model assembly line with unlimited customization flexibility promotes customer-centric production. Variable rate launching (VRL) – as opposed to fixed rate launching (FRL) – eliminates idle time and utility work in mixed-model assembly lines. Even in the context of limitless customization, which runs the risk of concentrated peak workloads and possible longer assembly lines, a variable takt proves to be more economical than a fixed takt. On the technical side, a fixed takt requires both a healthy balance of process times and optimal scheduling to create feasible production schedules, as using average takt does not allow workers enough time to complete their tasks for more complex products. Because the variable takt relies on similar process times for a given product across the entire line for one product, and all products are given exactly the time they need for assembly, optimal series-sequencing and regular task rescheduling can be eliminated. This book is intended as a reference guide for experienced executives in manufacturing and academics teaching operational effectiveness for customer-centric production, and makes extensive references to AGCO Fendt’s modern tractor line in Marktoberdorf, Germany. The book also offers application case studies from companies in related industries such as automotive, electronics, and machine tools. The book’s key message is that variation should not be universally eliminated from manufacturing. As such, the book represents a counterpoint to the philosophy advocated by the Toyota Production System.
Dynamics of Mechanical Systems with Variable Mass by Hans Irschik,Alexander K. Belyaev Pdf
The book presents up-to-date and unifying formulations for treating dynamics of different types of mechanical systems with variable mass. The starting point is overview of the continuum mechanics relations of balance and jump for open systems from which extended Lagrange and Hamiltonian formulations are derived. Corresponding approaches are stated at the level of analytical mechanics with emphasis on systems with a position-dependent mass and at the level of structural mechanics. Special emphasis is laid upon axially moving structures like belts and chains and on pipes with an axial flow of fluid. Constitutive relations in the dynamics of systems with variable mass are studied with particular reference to modeling of multi-component mixtures. The dynamics of machines with a variable mass are treated in detail and conservation laws and the stability of motion will be analyzed. Novel finite element formulations for open systems in coupled fluid and structural dynamics are presented.
Average Treatment Effect Bounds with an Instrumental Variable: Theory and Practice by Carlos A. Flores,Xuan Chen Pdf
This book reviews recent approaches for partial identification of average treatment effects with instrumental variables in the program evaluation literature, including Manski’s bounds, bounds based on threshold crossing models, and bounds based on the Local Average Treatment Effect (LATE) framework. It compares these bounds across different sets of assumptions, surveys relevant methods to assess the validity of these assumptions, and discusses estimation and inference methods for the bounds. The book also reviews some empirical applications employing bounds in the program evaluation literature. It aims to bridge the gap between the econometric theory on which the different bounds are based and their empirical application to program evaluation.
Temporal Quantum Correlations and Hidden Variable Models by Costantino Budroni Pdf
In this thesis, the main approach to the characterization of the set of classical probabilities, the correlation polytope approach, is reviewed for different scenarios, namely, hidden variable models discussed by Bell (local), Kochen and Specker (non-contextual), and Leggett and Garg (macrorealist). Computational difficulties associated with the method are described and a method to overcome them in several nontrivial cases is presented. For the quantum case, a general method to analyze quantum correlations in the sequential measurement scenario is provided, which allows computation of the maximal correlations. Such a method has a direct application for computation of maximal quantum violations of Leggett-Garg inequalities and it is relevant in the analysis of non-contextuality tests. Finally, possible applications of the results for quantum information tasks are discussed.
The Cointegrated VAR Model by Katarina Juselius Pdf
This valuable text provides a comprehensive introduction to VAR modelling and how it can be applied. In particular, the author focuses on the properties of the Cointegrated VAR model and its implications for macroeconomic inference when data are non-stationary. The text provides a number of insights into the links between statistical econometric modelling and economic theory and gives a thorough treatment of identification of the long-run and short-run structure as well as of thecommon stochastic trends and the impulse response functions, providing in each case illustrations of applicability.This book presents the main ingredients of the Copenhagen School of Time-Series Econometrics in a transparent and coherent framework. The distinguishing feature of this school is that econometric theory and applications have been developed in close cooperation. The guiding principle is that good econometric work should take econometrics, institutions, and economics seriously. The author uses a single data set throughout most of the book to guide the reader through the econometric theory whilealso revealing the full implications for the underlying economic model. To test ensure full understanding the book concludes with the introduction of two new data sets to combine readers understanding of econometric theory and economic models, with economic reality.
Handbook of Bayesian Variable Selection by Mahlet G. Tadesse,Marina Vannucci Pdf
Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, partitioning methods, as well as variable selection in decision trees and edge selection in graphical models. The handbook targets graduate students and established researchers who seek to understand the latest developments in the field. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions. Features: Provides a comprehensive review of methods and applications of Bayesian variable selection. Divided into four parts: Spike-and-Slab Priors; Continuous Shrinkage Priors; Extensions to various Modeling; Other Approaches to Bayesian Variable Selection. Covers theoretical and methodological aspects, as well as worked out examples with R code provided in the online supplement. Includes contributions by experts in the field. Supported by a website with code, data, and other supplementary material
Latent Variable Modeling Using R by A. Alexander Beaujean Pdf
This step-by-step guide is written for R and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the lavaan package, this book is designed to help readers quickly understand LVMs and their analysis in R. The author reviews the reasoning behind the syntax selected and provides examples that demonstrate how to analyze data for a variety of LVMs. Featuring examples applicable to psychology, education, business, and other social and health sciences, minimal text is devoted to theoretical underpinnings. The material is presented without the use of matrix algebra. As a whole the book prepares readers to write about and interpret LVM results they obtain in R. Each chapter features background information, boldfaced key terms defined in the glossary, detailed interpretations of R output, descriptions of how to write the analysis of results for publication, a summary, R based practice exercises (with solutions included in the back of the book), and references and related readings. Margin notes help readers better understand LVMs and write their own R syntax. Examples using data from published work across a variety of disciplines demonstrate how to use R syntax for analyzing and interpreting results. R functions, syntax, and the corresponding results appear in gray boxes to help readers quickly locate this material. A unique index helps readers quickly locate R functions, packages, and datasets. The book and accompanying website at http://blogs.baylor.edu/rlatentvariable/ provides all of the data for the book’s examples and exercises as well as R syntax so readers can replicate the analyses. The book reviews how to enter the data into R, specify the LVMs, and obtain and interpret the estimated parameter values. The book opens with the fundamentals of using R including how to download the program, use functions, and enter and manipulate data. Chapters 2 and 3 introduce and then extend path models to include latent variables. Chapter 4 shows readers how to analyze a latent variable model with data from more than one group, while Chapter 5 shows how to analyze a latent variable model with data from more than one time period. Chapter 6 demonstrates the analysis of dichotomous variables, while Chapter 7 demonstrates how to analyze LVMs with missing data. Chapter 8 focuses on sample size determination using Monte Carlo methods, which can be used with a wide range of statistical models and account for missing data. The final chapter examines hierarchical LVMs, demonstrating both higher-order and bi-factor approaches. The book concludes with three Appendices: a review of common measures of model fit including their formulae and interpretation; syntax for other R latent variable models packages; and solutions for each chapter’s exercises. Intended as a supplementary text for graduate and/or advanced undergraduate courses on latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, business, economics, and social and health sciences, this book also appeals to researchers in these fields. Prerequisites include familiarity with basic statistical concepts, but knowledge of R is not assumed.
Variable Phase Approach to Potential Scattering by F Calogero by Anonim Pdf
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. - Best operator approximation, - Non-Lagrange interpolation, - Generic Karhunen-Loeve transform - Generalised low-rank matrix approximation - Optimal data compression - Optimal nonlinear filtering
Latent Variable Modeling and Applications to Causality by Maia Berkane Pdf
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.
Variable Quality in Consumer Theory by W.M. Wadman Pdf
Examines consumer decision-making on products and services of variable quality at the level of retail markets. Addresses for the first time consumer-producer interaction at the level of the individual consumer; issues of quality, consumption experience, and willingness-to-pay, as exhibited by individual consumers; and how these issues affect the decision-making process.
