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Basics of Structural Equation Modeling.
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. Th...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Thousand Oaks :
SAGE Publications,
1997.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Maruyama, Dr. Geoffrey M. | |
| 245 | 1 | 0 | |a Basics of Structural Equation Modeling. |
| 260 | |a Thousand Oaks : |b SAGE Publications, |c 1997. | ||
| 300 | |a 1 online resource (328 pages) | ||
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| 588 | 0 | |a Print version record. | |
| 520 | |a 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. | ||
| 505 | 0 | |a 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. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Structural equation modeling. | |
| 650 | 0 | |a Social sciences |x Statistical methods. | |
| 650 | 6 | |a Modèles d'équations structurales. | |
| 650 | 6 | |a Sciences sociales |x Méthodes statistiques. | |
| 650 | 7 | |a Social sciences |x Statistical methods |2 fast | |
| 650 | 7 | |a Structural equation modeling |2 fast | |
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| 776 | 0 | 8 | |i Print version: |z 9780803974098 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=997200 |z Texto completo |
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| 938 | |a ProQuest Ebook Central |b EBLB |n EBL997200 | ||
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