9781032962429 - Understanding Structural Equation Models: Models of Relationships Between Variables di Wood, Phillip K. (22 risultati)

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Paperback. Condizione: new. Paperback. The field of structural equation models (SEMs) is rapidly expanding. A researcher who wants to select and apply SEMs to their data faces several challenges: (1) They can often become extremely complex, with many parameters to estimate. Small samples or those with relatively few variables often cannot support this complexity reliably, leading to under-identified models, poor power, or unstable estimates; (2) Researchers must choose an appropriate measurement model, and these choices are not often well understood in advance; (3) No single correct SEM exists, although better ones do, and the existence of competing plausible alternatives is often overlooked; and (4) Critical examination of model assumptions involving the linearity of parameters and the existence of influential or outlying observations is often overlooked. This book provides an overview of SEMs as a flexible, skeptical, and iterative scientific process.Key Features:Emphasis on multiverse analysis, right-sizing statistical models to data, and the generation of plausible skeptical alternativesRobust assumption checking (LOESS regression, regression and SEM diagnostics)Detailed, visual coverage of a variety of path diagrams, their links to matrix-based specifications, and data exploration using heat-map visualization and tests of dimensionalityA variety of SEMs including mediational models, psychometrics (e.g., parallel, tau-equivalent, congeneric measurement), growth curve models, exploratory factor analysis, multigroup, categorical, and exploratory structural equation modelingSupplemented by a website featuring code, data, lecture slides, and additional materialThis text is designed for graduate students, early-career researchers, and advanced undergraduates who wish to move beyond plug-and-play SEMs to a deeper, more philosophical and data-conscious understanding. Its careful balance of theory, worked examples, and emphasis on skepticism will help its audience build confidence in using SEMs flexibly and responsibly for a broad range of social and behavioral science research. For graduate students, early-career researchers, and advanced undergraduates who wish to move beyond plug-and-play SEMs to a deeper, more philosophical and data-conscious understanding. Supplemented by a website. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condizione: new. Paperback. The field of structural equation models (SEMs) is rapidly expanding. A researcher who wants to select and apply SEMs to their data faces several challenges: (1) They can often become extremely complex, with many parameters to estimate. Small samples or those with relatively few variables often cannot support this complexity reliably, leading to under-identified models, poor power, or unstable estimates; (2) Researchers must choose an appropriate measurement model, and these choices are not often well understood in advance; (3) No single correct SEM exists, although better ones do, and the existence of competing plausible alternatives is often overlooked; and (4) Critical examination of model assumptions involving the linearity of parameters and the existence of influential or outlying observations is often overlooked. This book provides an overview of SEMs as a flexible, skeptical, and iterative scientific process.Key Features:Emphasis on multiverse analysis, right-sizing statistical models to data, and the generation of plausible skeptical alternativesRobust assumption checking (LOESS regression, regression and SEM diagnostics)Detailed, visual coverage of a variety of path diagrams, their links to matrix-based specifications, and data exploration using heat-map visualization and tests of dimensionalityA variety of SEMs including mediational models, psychometrics (e.g., parallel, tau-equivalent, congeneric measurement), growth curve models, exploratory factor analysis, multigroup, categorical, and exploratory structural equation modelingThis text is designed for graduate students, early-career researchers, and advanced undergraduates who wish to move beyond plug-and-play SEMs to a deeper, more philosophical and data-conscious understanding. Its careful balance of theory, worked examples, and emphasis on skepticism will help its audience build confidence in using SEMs flexibly and responsibly for a broad range of social and behavioral science research. Designed for graduate students, early-career researchers, and advanced undergraduates who wish to move beyond plug-and-play SEMs to a deeper, more philosophical and data-conscious understanding. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condizione: new. Paperback. The field of structural equation models (SEMs) is rapidly expanding. A researcher who wants to select and apply SEMs to their data faces several challenges: (1) They can often become extremely complex, with many parameters to estimate. Small samples or those with relatively few variables often cannot support this complexity reliably, leading to under-identified models, poor power, or unstable estimates; (2) Researchers must choose an appropriate measurement model, and these choices are not often well understood in advance; (3) No single correct SEM exists, although better ones do, and the existence of competing plausible alternatives is often overlooked; and (4) Critical examination of model assumptions involving the linearity of parameters and the existence of influential or outlying observations is often overlooked. This book provides an overview of SEMs as a flexible, skeptical, and iterative scientific process.Key Features:Emphasis on multiverse analysis, right-sizing statistical models to data, and the generation of plausible skeptical alternativesRobust assumption checking (LOESS regression, regression and SEM diagnostics)Detailed, visual coverage of a variety of path diagrams, their links to matrix-based specifications, and data exploration using heat-map visualization and tests of dimensionalityA variety of SEMs including mediational models, psychometrics (e.g., parallel, tau-equivalent, congeneric measurement), growth curve models, exploratory factor analysis, multigroup, categorical, and exploratory structural equation modelingThis text is designed for graduate students, early-career researchers, and advanced undergraduates who wish to move beyond plug-and-play SEMs to a deeper, more philosophical and data-conscious understanding. Its careful balance of theory, worked examples, and emphasis on skepticism will help its audience build confidence in using SEMs flexibly and responsibly for a broad range of social and behavioral science research. Designed for graduate students, early-career researchers, and advanced undergraduates who wish to move beyond plug-and-play SEMs to a deeper, more philosophical and data-conscious understanding. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condizione: Neu. Understanding Structural Equation Models | Models of Relationships Between Variables | Phillip K. Wood | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | Chapman and Hall/CRC | EAN 9781032962429 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The field of structural equation models (SEMs) is rapidly expanding. A researcher who wants to select and apply SEMs to their data faces several challenges: (1) They can often become extremely complex, with many parameters to estimate. Small samples or those with relatively few variables often cannot support this complexity reliably, leading to under-identified models, poor power, or unstable estimates; (2) Researchers must choose an appropriate measurement model, and these choices are not often well understood in advance; (3) No single 'correct' SEM exists, although 'better' ones do, and the existence of competing plausible alternatives is often overlooked; and (4) Critical examination of model assumptions involving the linearity of parameters and the existence of influential or outlying observations is often overlooked. This book provides an overview of SEMs as a flexible, skeptical, and iterative scientific process.