The Essence of multivariate thinking: Basic themes and methods / Lisa L. Harlow
Material type:
TextPublication details: New York : Routledge/ Taylor & Francis Group, c2014Edition: SECOND EDITIONDescription: xxxvi, 396 pages : illustrations ; 26 cmISBN: 9780415873727Subject(s): MULTIVARIATE ANALYSIS | PSYCHOLOGY -- MATHEMATICAL MODELSLOC classification: QA 278 .H349 2014
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LRC - Graduate Studies | National University - Manila | Gen. Ed - CEAS | General Circulation | GC QA 278 .H349 2014 (Browse shelf (Opens below)) | c.1 | Available | NULIB000013072 |
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| GC QA 276 .I58 2013 c.1 An introduction to statistical learning with applications in R / | GC QA 276 .I58 2013 c.2 An introduction to statistical learning with applications in R / | GC QA 276 .W35 2006 Introduction to statistics / | GC QA 278 .H349 2014 The Essence of multivariate thinking: Basic themes and methods / | GC QA 278 .I94 2013 Modern multivariate statistical techniques : regression, classification, and manifold learning / | GC QA 278 .R46 2012 Methods of multivariate analysis / | GC QA 303 .T45 1992 Calculus and analytic geometry / |
Includes bibliographical references and indexes.
PART I. OVERVIEW -- Chapter 1. Introduction and Multivariate Themes -- Chapter 2. Background Considerations -- PART II. INTERMEDIATE MULTIVARIATE METHODS WITH ONE CONTINUOUS OUTCOME -- Chapter 3. Multiple Regression -- Chapter 4. Analysis of Covariance -- PART III. MULTIVARIATE GROUP METHODS WITH CATEGORICAL VARIABLE(S) -- Chapter 5. Multivariate Analysis OF Variance -- Chapter 6. Discriminant Function Analysis -- Chapter 7. Logistic Regression -- PART IV. MULTIVARIATE MODELING METHODS -- Chapter 8. Multilevel Modeling -- Chapter 9: Principal Components and Factor Analysis -- PART V. STRUCTURAL EQUATION MODELING -- Chapter 10. Structural Equation Modeling Overview -- Chapter 11. Path Analysis -- Chapter 12. Confirmatory Factor Analysis -- Chapter 13. Latent Variable Modeling -- PART VI. SUMMARY -- Chapter 14. Integration of Multivariate Methods.
By focusing on underlying themes, this book helps readers better understand the connections between multivariate methods. For each method the author highlights: the similarities and differences between the methods, when they are used and the questions they address, the key assumptions and equations, and how to interpret the results. The concepts take center stage while formulas are kept to a minimum. Examples using the same data set give readers continuity so they can more easily apply the concepts. Each method is also accompanied by a worked out example, SPSS and SAS input, and an example of how to write up the results. EQS code is used for the book’s SEM applications.
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