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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 |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Thousand Oaks :
SAGE Publications,
1997.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.