Reading and Understanding Multivariate Statistics. Este artículo no está disponible.
Laurence G. Grimm; Paul R. Yarnold [Editor]
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Idioma: inglés
Editorial: American Psychological Association, 1994
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- Usado

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N° de ref. del artículo 1557982732-4-30981648
- Título
- Reading and Understanding Multivariate Statistics
- Autor
- Laurence G. Grimm; Paul R. Yarnold [Editor]
- Editorial
- American Psychological Association
- Año de publicación
- 1994
- Estado
- Fair
- Encuadernación
- paperback
- Idioma
- inglés
- ISBN 10
- 1557982732
- ISBN 13
- 9781557982735
- Peso del artículo
- 23 onzas
- Dimensiones
- 7x1x10
Reading and Understanding Multivariate Statistics helps researchers, students, and other readers of research to understand the purpose and presentation of multivariate techniques. The editors focus on providing a conceptual understanding of the meaning of the statistics in the context of the research questions and results they leave the subject of how to perform multivariate analysis to other texts.
The book presents an overview of multivariate statistics and their place in research. It describes the appropriate context for-and the types of empirical questions that can best be addressed by-each technique or family of techniques, as well as the distribution assumptions that must be met for the analysis to be meaningful.
The most commonly used multivariate techniques are examined in detail: multiple regression and correlation path analysis principal-components analysis exploratory and confirmatory factor analysis multidimensional scaling analysis of cross-classified data logistic regression multivariate analysis of variance (MANOVA) discriminant analysis meta-analysis.
The book presents an overview of multivariate statistics and their place in research. It describes the appropriate context for-and the types of empirical questions that can best be addressed by-each technique or family of techniques, as well as the distribution assumptions that must be met for the analysis to be meaningful.
The most commonly used multivariate techniques are examined in detail: multiple regression and correlation path analysis principal-components analysis exploratory and confirmatory factor analysis multidimensional scaling analysis of cross-classified data logistic regression multivariate analysis of variance (MANOVA) discriminant analysis meta-analysis.
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Reseña del editor
This text aims to help researchers and students to understand the purpose and presentation of multivariate statistical techniques. The most commonly used techniques are described in detail, such as multiple regression and correlation and path analysis
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