Basics of structural equation modeling

With the availability of software programs, such as LISREL, EQS, and AMOS, modelling (SEM) techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and test for the plausibility of the hypothesizing for a particular data set. However, the popularity of these techniques has often led to misunderstandings of them and even their misuse, particularly by students exposed to them for the first time. Through the use of careful narrative explanation, Maruyama's text describes the logic underlying SEM approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data. In addition, Maruyama provides carefully constructed exercises both within and at the end of chapters. Through The Use Of Careful Narrative Explanation, Basics Of Structural Equation Modeling Describes The Logic Underlying Structural Equation Modeling (sem) Approaches, Describes How Sem Approaches Relate To Techniques Like Regression And Factor Analysis, Analyzes The Strengths And Shortcomings Of Sem As Compared To Alternative Methodologies, And Explores The Various Methodologies For Analyzing Structural Equation Data. Throughout The Book, Maruyama Uses A Single Data Set (the Relationships Between Peer Acceptance And Achievement) To Demonstrate A Variety Of Techniques Ranging From Path Analysis To Panel Analysis To Confirmatory Analysis To Latent Variable Structural Equation Modeling. In Addition, Maruyama Provides Carefully Constructed Exercises Both Within And At The End Of Chapters So That Readers Can Apply The Material That They Have Just Read. This Book Will Provide Readers With A Good Understanding Of How And Why Structural Equation Approaches Have Come To Be Used. Background. What Does It Mean To Model Hypothesized Causal Processes With Nonexperimental Data? -- History And Logic Of Structural Equation Modeling -- Basic Approaches To Modeling With Single Observed Measures Of Theoretical Variables. The Basics: Path Analysis And Partitioning Of Variance -- Effects Of Collinearity On Regression And Path Analysis -- Effects Of Random And Nonrandom Error On Path Models -- Recursive And Longitudinal Models: Where Causality Goes In More Than One Direction And Where Data Are Collected Over Time -- Factor Analysis And Path Modeling. Introducing The Logic Of Factor Analysis And Multiple Indicators To Path Modeling -- Latent Variable Structural Equation Models. Putting It All Together: Latent Variable Structural Equation Modeling -- Using Latent Variable Structural Equation Modeling To Examine Plausibility Of Models -- Logic Of Alternative Models And Significance Tests -- Variations On The Basic Latent Variable Structural Equation Model -- Wrapping Up -- Appendix A: A Brief Introduction To Matrix Algebra And Structural Equation Modeling. By Geoffrey M. Maruyama. Includes Bibliographical References (p. 299-305) And Indexes.

With the availability of software programs, such as LISREL, EQS, and AMOS, modeling (SEM) techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and test for the plausibility of hypothesizing for a particular data set. Through the use of careful narrative explanation, Maruyama's text describes the logic underlying SEM approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data. In addition, Maruyama provides carefully constructed exercises both within and

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Describes the logic underlying structural equation modeling (SEM) approaches, explains how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores various methodologies for analyzing structural equation data. Uses a single data set (the relationship between peer acceptance and achievement) to demonstrate different techniques, such as path analysis, confirmatory analysis, and latent variable structural equation modeling. Includes exercises, chapter summaries, and discussion questions. For researchers and advanced students. Paper edition (unseen), $26.95. Annotation c. by Book News, Inc., Portland, Or.

"Through the use of careful narrative explanation, Basics of Structural Equation Modeling describes the logic underlying structural equation modeling (SEM) approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data." "Throughout the book, Maruyama uses a single data set (the relationships between peer acceptance and achievement) to demonstrate a variety of techniques ranging from path analysis to panel analysis to confirmatory analysis to latent variable structural equation modeling. In addition, Maruyama provides carefully constructed exercises both within and at the end of chapters so that readers can apply the material that they have just read. This book will provide readers with a good understanding of how and why structural equation approaches have come to be used."--Jacket With the availability of software programs such as LISREL, EQS, and AMOS modeling techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and for testing the plausibility of hypothesizing for a particular data set. The popularity of these techniques, however, has often led to misunderstandings of them, particularly by students being exposed to them for the first time. Through the use of careful narrative explanation, Basics of Structural Equation Modeling describes the logic underlying structural equation modeling (SEM) approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data.