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    Applied Multivariate Research: Design and Interpretation (Hardback) By (author) Lawrence S. Meyers, By (author) Glenn C. Gamst, By (author) Anthony J. Guarino

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    DescriptionToday, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioral sciences are exposed to the complex multivariate statistical techniques such as correlation and multiple regression, exploratory factor analysis, MANOVA, path analysis, and structural equation modeling. This book is designed to provide full coverage of the wide range of multivariate topics in a conceptual, non-mathematical, approach. It is geared toward the needs, level of sophistication, and interest in multivariate methodology of students in applied programs in the social and behavioral sciencesathat need to focus on design and interpretation rather than the intricacies of specific computations.


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    Title
    Applied Multivariate Research
    Subtitle
    Design and Interpretation
    Authors and contributors
    By (author) Lawrence S. Meyers, By (author) Glenn C. Gamst, By (author) Anthony J. Guarino
    Physical properties
    Format: Hardback
    Number of pages: 1104
    Width: 198 mm
    Height: 234 mm
    Thickness: 53 mm
    Weight: 1,905 g
    Language
    English
    ISBN
    ISBN 13: 9781412988117
    ISBN 10: 141298811X
    Classifications

    B&T Book Type: NF
    Nielsen BookScan Product Class 3: S3.0
    BIC E4L: SOC
    B&T Modifier: Region of Publication: 01
    B&T General Subject: 750
    BIC subject category V2: JHBC
    Ingram Subject Code: PS
    Libri: I-PS
    Warengruppen-Systematik des deutschen Buchhandels: 15320
    B&T Modifier: Academic Level: 02
    B&T Modifier: Text Format: 06
    B&T Merchandise Category: STX
    B&T Approval Code: A30140000
    LC subject heading: ,
    BISAC V2.8: SOC019000, PSY030000, SOC024000
    LC subject heading:
    B&T Approval Code: A30202000
    BISAC V2.8: SOC027000, PSY032000
    LC subject heading:
    B&T Approval Code: A51184800
    DC22: 300.1519535, 300.1/519535
    DC23: 300.72
    LC classification: HA31.3 .M487 2013
    Sage Categories:
    Thema V1.0: JHBC
    Edition
    2, Revised
    Edition statement
    2nd Revised edition
    Publisher
    SAGE Publications Inc
    Imprint name
    SAGE Publications Inc
    Publication date
    23 October 2012
    Publication City/Country
    Thousand Oaks
    Author Information
    Larry Meyers earned his doctorate in Experimental Psychology, and has been a Professor in the Psychology Department at California State University, Sacramento for a number of years. He supervises research students and teaches research design courses as well as history of psychology at both the undergraduate and graduate level. His areas of expertise include test development and validation. Glenn Gamst is Professor and Chair of the Psychology Department at the University of La Verne, where he teaches the doctoral advanced statistics sequence. He received his Ph.D. from the University of Arkansas in experimental psychology. His research interests include the effects of multicultural variables on clinical outcome. Additional research interests focus conversation memory and discourse processing. A.J. Guarino received his B.A. from the University of California, Berkeley and a Ph.D. from the University of Southern California in statistics and research methodologies from the Department of Educational Psychology. He is professor of biostatistics at Massachusetts General Hospital, Institute of Health Professions. He is the statistician on numerous NIH grants and reviewer on several research journals.
    Review quote
    For me the comprehensive nature of the text is most important - even when I don't cover topics in class students gain value by being able to read about cluster analysis or ROC analysis in enough detail that they can conduct their own analyses. Students appreciate the integration with SPSS. There is an appropriate balance of "practice" and background so that students learn what they need to know about the techniques but also learn how to implement and interpret the analysis. -- E. Kevin Kelloway, Saint Mary's University The key strengths are its clearly written explanations of OLS regression and logistic regression as well as its treatment of path analysis. -- Andrew Jorgenson, University of Utah The comprehensive nature of the topics presented and the numerous figures and charts. -- Marie Kraska, Ph.D., Auburn University 20120601 Organization is excellent. -- Thomas J. Keil, Arizona State University Well written and accessible. I find the additional readings at the end of the chapters to be valuable and have tracked down several of the sources for my own personal use. -- Glenn J. Hansen, University of Oklahoma My students think the book is well written and the language is easy for them to understand -- Xiaofen Deng Keating, The University of Texas at Austin
    Table of contents
    Part I. The Basics of Multivariate Design Chapter 1. An Introduction to Multivariate Design Chapter 2. Some Fundamental Research Design Concepts Chapter 3A. Data Screening Chapter 3B. Data Screening Using IBM SPSS Part II. Comparisons of Means Chapter 4A. Univariate Comparison of Means Chapter 4B. Univariate Comparison of Means Using IBM SPSS Chapter 5A. Multivariate Analysis of Variance (MANOVA) Chapter 5B. Multivariate Analysis of Variance (MANOVA) Using IBM SPSS Part III. Predicting the Value of a Single Variable Chapter 6A. Bivariate Correlation and Simple Linear Regression Chapter 6B. Bivariate Correlation and Simple Linear Regression Using IBM SPSS Chapter 7A. Multiple Regression: Statistical Methods Chapter 7B. Multiple Regression: Statistical Methods Using IBM SPSS Chapter 8A. Multiple Regression: Beyond Statistical Regression Chapter 8B. Multiple Regression: Beyong Statistical Regression Using IBM SPSS Chapter 9A. Multilevel Modeling Chapter 9B. Multilevel Modeling Using IBM SPSS Chapter 10A. Binary and Multinomial Logistic Regression and ROC Analysis Chapter 10B. Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSS Part IV. Analysis of Structure Chapter 11A. Discriminant Function Analysis Chapter 11B. Discriminant Function Analysis Using IBM SPSS Chapter 12A. Principal Components and Exploratory Factor Analysis Chapter 12B. Principal Components and Exploratory Factor Analysis Using IBM SPSS Chapter 13A. Canonical Correlation Analysis Chapter 13B. Canonical Correlation Analysis Using IBM SPSS Chapter 14A. Multidimensional Scaling Chapter 14B. Multidimensional Scaling Using IBM SPSS Chapter 15A. Cluster Analysis Chapter 15B. Cluster Analysis Using IBM SPSS Part V. Fitting Models to Data Chapter 16A. Confirmatory Factor Analysis Chapter 16B. Confirmatory Factor Analysis Using Amos Chapter 17A. Path Analysis: Multiple Regression Chapter 17B. Path Analysis: Multiple Regression Using IBM SPSS Chapter 18A. Path Analysis: Structural Modeling Chapter 18B. Path Analysis: Structural Modeling Using Amos Chapter 19A. Structural Equation Modeling Chapter 19B. Structural Equation Modeling Using Amos Chapter 20A. Model Invariance: Applying a Model to Different Groups Chapter 20B. Assessing Model Invariance Using Amos