Reading and Understanding More Multivariate Statistics

✍ Scribed by Laurence G. Grimm; Paul R. Yarnold

• Publisher: American Psychological Association (APA)
• Year: 2000
• Tongue: English
• Leaves: 450
• Category: Library

✦ Synopsis

Since 1995, over 13,000 graduate students and researchers have relied on Reading and Understanding Multivariate Statistics for a basic understanding of the most commonly used multivariate analyses in the research literature today. In Reading and Understanding MORE Multivariate Statistics, the editors have responded to reader requests to provide the same accessible approach to a new group of multivariate techniques and related topics in measurement. Chapters demystify the use of cluster analysis, Q-technique factor analysis, structural equation modeling, canonical correlation analysis, repeated measures analyses, and survival analysis.

As with the previous volume, chapter authors describe the research questions for which the statistic is most appropriate, the underlying assumptions and rationale of the analysis, and the logic behind interpreting the results. Designed to clarify each statistic's logic and utility rather than teach hands-on application, the book emphasizes the real-world use of statistical methods with minimal reliance on complex mathematical formulas. Each chapter contains accessible discussions of general principles, instructions for interpreting summary tables, and a glossary of key terms and statistical notations. Whether you are a graduate student, researcher, or consumer of research, this volume is guaranteed to increase your comfort level and confidence in reading and understanding multivariate statistics.

✦ Table of Contents

Chapter 1: Introduction to Multivariate Statistics.
Chapter 2: Reliability and Generalizability Theory.
Chapter 3: Item Response Theory.
Chapter 4: Assessing the Validity of Measurement.
Chapter 5: Cluster Analysis.
Chapter 6: Q-Technique Factor Analysis: One Variation on the Two-Mode Factor Analysis of Variables.
Chapter 7: Structural Equation Modeling.
Chapter 8: Ten Commandments of Structural Equation Modeling.
Chapter 9: Canonical Correlation Analysis.
Chapter 10: Repeated Measures Analyses: ANOVA, MANOVA, and HLM.