Linear Causal Modeling With Structural Equations
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Linear Causal Modeling With Structural Equations
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Author : Stanley A. Mulaik
language : en
Publisher: CRC Press
Release Date : 2009-06-16
Linear Causal Modeling With Structural Equations written by Stanley A. Mulaik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-16 with Mathematics categories.
Emphasizing causation as a functional relationship between variables, this book provides comprehensive coverage on the basics of SEM. It takes readers through the process of identifying, estimating, analyzing, and evaluating a range of models. The author discusses the history and philosophy of causality and its place in science and presents graph theory as a tool for the design and analysis of causal models. He explains how the algorithms in SEM are derived and how they work, covers various indices and tests for evaluating the fit of structural equation models to data, and explores recent research in graph theory, path tracing rules, and model evaluation.
Structural Equation Modeling In Educational Research
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Author :
language : en
Publisher: BRILL
Release Date : 2009-01-01
Structural Equation Modeling In Educational Research written by and has been published by BRILL this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-01 with Education categories.
Over the years, researchers have developed statistical methods to help them investigate and interpret issues of interest in many discipline areas. These methods range from descriptive to inferential to multivariate statistics. As the psychometrics measures in education become more complex, vigorous and robust methods were needed in order to represent research data efficiently. One such method is Structural Equation Modeling (SEM). SEM is a statistical technique that allows the simultaneous analysis of a series of structural equations. It also allows a dependent variable in one equation to become an independent variable in another equation. It is a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables. SEM is commonly known as causal modeling, or path analysis, which hypothesizes causal relationships among variables and tests the causal models with a linear equation system. As educational research questions become more complex, they need to be evaluated with more sophisticated tools. The pervasive use of SEM in the literature has shown that SEM has a potential to be of assistance to modern educational researchers. This book will bring together prominent educators and researchers from around the world to share their contemporary research on structural equation modeling in educational settings. The chapters provide information on recent trends and developments and effective applications of the different models to answer various educational research questions. This book is a critical and specialized source that describes recent advances in SEM in international academia.
Causal Models In The Social Sciences
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Author : H. M. Blalock, Jr.
language : en
Publisher: Transaction Publishers
Release Date : 2011-12-31
Causal Models In The Social Sciences written by H. M. Blalock, Jr. and has been published by Transaction Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12-31 with Social Science categories.
Causal models are formal theories stating the relationships between precisely defined variables, and have become an indispensable tool of the social scientist. This collection of articles is a course book on the causal modeling approach to theory construction and data analysis. H. M. Blalock, Jr. summarizes the then-current developments in causal model utilization in sociology, political science, economics, and other disciplines. This book provides a comprehensive multidisciplinary picture of the work on causal models. It seeks to address the problem of measurement in the social sciences and to link theory and research through the development of causal models. Organized into five sections (Simple Recursive Models, Path Analysis, Simultaneous Equations Techniques, The Causal Approach to Measurement Error, and Other Complications), this volume contains twenty-seven articles (eight of which were specially commissioned). Each section begins with an introduction explaining the concepts to be covered in the section and links them to the larger subject. It provides a general overview of the theory and application of causal modeling. Blalock argues for the development of theoretical models that can be operationalized and provide verifiable predictions. Many of the discussions of this subject that occur in other literature are too technical for most social scientists and other scholars who lack a strong background in mathematics. This book attempts to integrate a few of the less technical papers written by econometricians such as Koopmans, Wold, Strotz, and Fisher with discussions of causal approaches in the social and biological sciences. This classic text by Blalock is a valuable source of material for those interested in the issue of measurement in the social sciences and the construction of mathematical models.
Causal Modeling Structural Equations
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Author :
language : en
Publisher:
Release Date :
Causal Modeling Structural Equations written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Advanced Econometrics
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Author : Cesar Perez
language : en
Publisher: CreateSpace
Release Date : 2015-01-24
Advanced Econometrics written by Cesar Perez and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-24 with Business & Economics categories.
Data analysis has evolved and today not work already only observable variables, but also latent variables or factorials. In this case, the underlying data structures are rather less apparent and new specialized software can detect them through the analysis of an array of data, correlations or covariances. Design and modelling has changed a lot in the last two decades. The researcher used to work exclusively with observable variables when all the underlying structures were clear and obvious, but the need for the measure in the social sciences by unobservable variables drove the evolution of modelling in this sense in all the sciences. In this way appear causal models, structural equation or covariance structures developed by Joreskog (1973), Keesing (1972) and Wiley (1973) and expanded in LISREL (Linear Structural Relationship) model and other models that proposed the analysis of covariance structures different representations. The book essentially develop the following topics: MODELS IN STRUCTURAL EQUATIONS MODELLING USING STRUCTURAL EQUATIONS LISREL AND THE STRUCTURAL EQUATION MODEL SAS AND THE STRUCTURAL EQUATIONS MODEL. PROC CALIS LINEAR REGRESSION MODELS AS STRUCTURAL EQUATION MODELS ADJUSTMENT BASIC REGRESSION MODELS MULTIVARIATE REGRESSION MODELS MODELS WITH MEASUREMENT ERRORS THROUGH STRUCTURAL EQUATIONS MODELS WITH SIMPLE MEASUREMENTS ERRORS COMPLETE MODELS WITH VARIABLES MEASURED WITH ERRORS MODEL OF LINEAR REGRESSION WITH ERRORS OF DIMENSIONS AS A SPECIAL CASE OF STRUCTURAL EQUATION MODEL MODELS MEASUREMENT OF THE ERROR MODELS OF LINEAR EQUATIONS CONFIRMATORY FACTORIAL ANALYSIS CONFIRMATORY FACTOR ANALYSIS MODEL. IDENTIFICATION, ESTIMATION AND DIAGNOSIS STRUCTURAL MODELS WITH SAS. PROC CALIS THE COVARIANCE STRUCTURE MODELS HE COVARIANCE STRUCTURE MODEL SPECIFICATION OF THE MEASUREMENT MODEL SPECIFICATION OF MODEL STRUCTURAL GENERAL MODEL OF THE COVARIANCE STRUCTURE STAGES OF MODELING PECIFICATION OF THE MODEL IDENTIFICATION OF THE MODEL ESTIMATION OF PARAMETERS DIAGNOSIS OR FIT OF THE MODEL INTERPRETATION OF THE MODEL REESPECIFICACION MODEL SAS AND THE GENERAL MODEL OF THE COVARIANCE STRUCTURE. PROC CALIS
Recent Developments On Structural Equation Models
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Author : Kees van Montfort
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-04-30
Recent Developments On Structural Equation Models written by Kees van Montfort and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-04-30 with Psychology categories.
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.
Causal Modeling
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Author : Herbert B. Asher
language : en
Publisher: SAGE
Release Date : 1976
Causal Modeling written by Herbert B. Asher and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1976 with Mathematics categories.
Retains complete coverage of the first edition, while amplifying key areas such as direct/indirect effects, standardized/unstandardized variables, multicollinie-arity, and nonrecursive modeling.
Graphical Methods For Linear Structural Equation Modeling
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Author : Bryant Roi Chen
language : en
Publisher:
Release Date : 2017
Graphical Methods For Linear Structural Equation Modeling written by Bryant Roi Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Estimating causal effects is one of the fundamental problems in the empirical sciences. When a randomized study can be performed, estimating causal effects reduces to standard, well-understood methods. However, randomized experiments can be imperfect, unethical, or infeasible in many real-world scenarios. In such cases, determining whether and how the causal effect can be estimated depends on the underlying causal structure, which is generally modeled using structural equation models (SEMs). These models allow researchers to express causal assumptions formally and transparently, test them against data, and derive their consequences. As a result, researchers can use SEMs to determine whether their assumptions enable a causal effect to be estimated and, if so, derive a consistent estimator for that effect. Likewise, researchers can derive testable implications of their assumptions and test them against data. While linear SEMs have been studied for nearly a century, no complete and tractable algorithm has been developed for determining whether an effect is estimable and for deriving a consistent estimator (called the identification problem). Likewise, little work has been done to develop algorithmic methods for deriving testable implications of linear SEMs. In this work, I devise a new family of graph-based methods to address these two fundamental problems in linear SEMs. Perhaps the most common method of identifying and estimating causal effects in a linear structural model is via regression. However, in order for regression methods to provide unbiased and consistent estimates of the causal effect, the exogeneity assumption must be satisfied. A common way of testing this assumption is to perform a ``robustness test'', where variables are added to the regression and a consequent shift in the coefficient of interest is taken as evidence of misspecification or bias. However, I show that certain regressors, when added to the regression, will induce a shift, even when the model is properly specified. Such robustness tests would produce false alarm, suggesting that the model is misspecified when it is not. I propose a simple, graphical criterion that allows researchers to quickly determine which variables, when added to the regression, constitute informative robustness tests. I also characterize when and how robustness tests are able to detect confounding bias. Another pervasive and well-known method for deriving testable implications is overidentification. Overidentification occurs when the modeling assumptions allow for two distinct and independent estimators for a given parameter. In this case, we can test the identifying assumptions by comparing and imposing equality on the two estimates. In this work, I extend the state-of-the-art half-trek identification algorithm and apply it to systematically derive overidentifying and other constraints that can be used to test the model against the observed covariance matrix. Previous algorithms designed for the identification of linear SEMs are not always able to identify parameters and testable implications that non-parametric methods can, which is surprising given that the assumption of linearity imposes additional constraints over the observed data. I propose a new decomposition strategy where an SEM can be recursively reduced into simpler sub-models, allowing the identification of parameters and testable implications that could not be identified in the original model. I prove that that the resulting procedure enables the identification of any parameter or testable implication that can be identified by non-parametric algorithms, closing the gap between parametric and non-parametric methods. Lastly, I devise a new framework called auxiliary variables (AVs) that allows researchers to incorporate knowledge of causal effects into the model. As a result, researchers can utilize AVs to supplement graphical identification and model testing methods with knowledge derived from previously identified causal effects, related studies, or surrogate experiments. I then apply this framework to develop a procedure that alternates steps of identification using instrumental sets with construction of AVs. I prove that, even without utilizing external knowledge of causal effects, this algorithm is the most powerful polynomial-time identification algorithm currently available, subsuming all methods found in the literature.
Nonrecursive Causal Models
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Author : William Dale Berry
language : en
Publisher: SAGE
Release Date : 1984-07
Nonrecursive Causal Models written by William Dale Berry and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984-07 with Reference categories.
The author defines the concept of identification and explains what 'goes wrong' with some nonrecursive models to make them nonidentified. He provides various tests which can be used to determine whether a nonrecursive model is identified, and reviews common techniques for estimating the parameters of an identified model.
Ecological Statistics
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Author : Gordon A. Fox
language : en
Publisher: Oxford University Press
Release Date : 2015
Ecological Statistics written by Gordon A. Fox and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computers categories.
The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.