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Statistical Power Analysis with Missing Data : a Structural Equation Modeling Approach.
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research.€ Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Taylor & Francis,
2009.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Davey, Adam. | |
| 245 | 1 | 0 | |a Statistical Power Analysis with Missing Data : |b a Structural Equation Modeling Approach. |
| 260 | |a Hoboken : |b Taylor & Francis, |c 2009. | ||
| 300 | |a 1 online resource | ||
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| 520 | |a Statistical power analysis has revolutionized the ways in which we conduct and evaluate research.€ Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling.€ It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types. | ||
| 505 | 0 | |a Front Cover; Statistical Power Analysis with Missing Data; Copyright Page; Contents; 1. Introduction; Overview and Aims; Statistical Power; Testing Hypotheses; Choosing an Alternative Hypothesis; Central and Noncentral Distributions; Factors Important for Power; Effect Sizes; Determining an Effect Size; Point Estimates and Confidence Intervals; Reasons to Estimate Statistical Power; Conclusions; Further Readings; Section I: Fundamentals; 2. The LISREL Model; Matrices and the LISREL Model; Latent and Manifest Variables; Regression Coefficient Matrices; Variance-Covariance Matrices. | |
| 588 | 0 | |a Print version record. | |
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| 650 | 0 | |a Social sciences |x Mathematical models. | |
| 650 | 0 | |a Social sciences |x Statistical methods. | |
| 650 | 0 | |a Social sciences |v Statistics. | |
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| 650 | 6 | |a Sciences sociales |x Méthodes statistiques. | |
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| 700 | 1 | |a Savla, Jyoti "Tina." | |
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