JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second edition

  • 7h 16m
  • Ann Lehman, Edward J. Stepanski, Larry Hatcher, Norm O’Rourke
  • SAS Institute
  • 2013

Learn how to manage JMP data and perform the statistical analyses most commonly used in research in the social sciences and other fields with JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition.

Updated for JMP 10 and including new features on the statistical platforms, this book offers clearly written instructions to guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research. Step by step, you'll discover how to obtain descriptive and inferential statistics, summarize results clearly in a way suitable for publication, perform a wide range of JMP analyses, interpret the results, and more.

Topics include

  • screening data for errors
  • selecting subsets
  • computing the coefficient alpha reliability index (Cronbach's alpha) for a multiple-item scale
  • performing bivariate analyses for all types of variables
  • performing a one-way analysis of variance (ANOVA), multiple regression, and a one-way multivariate analysis of variance (MANOVA)

Advanced topics include analyzing models with interactions and repeated measures. There is also comprehensive coverage of principle components with emphasis on graphical interpretation.

This user-friendly book introduces researchers and students of the social sciences to JMP and to elementary statistical procedures, while the more advanced statistical procedures that are presented make it an invaluable reference guide for experienced researchers as well.

About the Authors

Ann Lehman, PhD, joined SAS Institute in 1979 and is retired and working as a JMP consultant doing applications programming and statistical documentation. She has been working with JMP since its inception in 1988. A co-author of JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition, and JMP Start Statistics, Ann has a diverse background that includes editing and writing SAS user's guides, writing and teaching SAS courses, and serving as technical editor of the JMPer Cable, JMP's technical newsletter.

Norm O'Rourke, Ph.D., R.Psych., is a clinical psychologist and associate professor with the Faculty of Arts & Social Sciences at Simon Fraser University in Vancouver, BC, Canada. His areas of research interest include mental illness and well-being, marriage in later life, and test construction and validation.

Larry Hatcher, Ph.D., is a professor of psychology at Saginaw Valley State University in Saginaw, Michigan, where he teaches classes in general psychology, industrial psychology, statistics, and computer applications in data analysis. The author of several books dealing with statistics and data analysis, Larry has taught at the college level since 1984 after earning his doctorate in industrial and organizational psychology from Bowling Green State University in 1983.

Dr. Stepanski is currently the Chief Operating Officer of ACORN Research LLC, a company that conducts clinical research in oncology. In this role, he oversees operations for several service areas including a US-based oncology research network, a contract research organization, and a health outcomes unit. He has written over 90 publications on a variety of topics related to clinical research.

In this Book

  • Using This Book
  • Basic Concepts in Research and Data Analysis
  • Getting Started with JMP
  • Working with JMP Data
  • Exploring Data with the Distribution Platform
  • Measures of Bivariate Association
  • Assessing Scale Reliability with Coefficient Alpha
  • t-Tests—In dependent Samples and Paired Samples
  • One-Way ANOVA with One Between-Subjects Factor
  • Factorial ANOVA with Two Between-Subjects Factors
  • Multivariate Analysis of Variance (MANOVA) with One Between-Subjects Factor
  • One-Way ANOVA with One Repeated-Measures Factor
  • Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
  • Multiple Regression
  • Principal Component Analysis
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