JMP for Basic Univariate and Multivariate Statistics

Ann Lehman Phd, Norm O'Rourke Ph.D. R.Psych., Larry Hatcher Ph.D., Edward J. Stepanski Ph.D.

Description

JMP Start Statistics: A Guide to Statistics and Data Analysis Using JMP, Fifth Edition, is the perfect mix of software manual and statistics text. Authors John Sall, Ann Lehman, Mia Stephens, and Lee Creighton provide hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises.
JMP Start Statistics, Fifth Edition, includes many new features of JMP 10, including an enhanced ability to manage a JMP session by easily tracking open and recently opened JMP tables; scripts, analyses, JMP projects, and other files; vastly expanded tools for instructors to demonstrate statistical concepts and interactive scripts to help students grasp difficult topics; Split-Plot designs with examples; examples of Graph Builder and Control Chart Builder; and new features that make the software easier to use.
This book is part of the SAS Press program.

Sujets

• COMPUTERS
• Getting Started
• Repeated measures design
• Enterprise software
• General officer
• Statistics
• Psychometrics
• General

Informations

• Publié par: SAS Institute
• Date de parution: 16 avril 2013
• Nombre de lectures: 0
• EAN13: 9781612906256
• Langue: English
• Poids de l'ouvrage: 5 Mo

Extrait

The correct bibliographic citation for this manual is as follows: Lehman, Ann, Norm O’Rourke, Larry Hatcher, and Edward J. Stepanski. 2013. JMP ® f or Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition. Cary, NC: SAS Institute Inc.
JMP ® for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition
Copyright © 2013, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-61290-625-6 (electronic book)
ISBN 978-1-61290-603-4
All rights reserved. Produced in the United States of America.
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1st printing, April 2013
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704420tf28JUN2013



Contents
Using This Book
Acknowledgments

1 Basic Concepts in Research and Data Analysis
Overview
Introduction: A Common Language for Researchers
Steps to Follow When Conducting Research
Variables, Values, and Observations
Scales of Measurement and JMP Modeling Types
Basic Approaches to Research
Descriptive versus Inferential Statistical Analysis
Hypothesis Testing
Summary
References

2 Getting Started with JMP
Overview
Start the JMP Application
The JMP Approach to Statistics
A Step-by-Step JMP Example
Summary
References

3 Working with JMP Data
Overview
Structure of a JMP Table
JMP Tables, Rows, and Columns
Getting Data into JMP
Data Table Management
Summary
References

4 Exploring Data with the Distribution Platform
Overview
Why Perform Simple Descriptive Analyses?
Example: The Helpfulness Social Survey
Computing Summary Statistics
A Step-by-Step Distribution Analysis Example
Summary
References

5 Measures of Bivariate Association
Overview
Significance Tests versus Measures of Association
Choosing the Correct Statistic
Section Summary
Pearson Correlations
Spearman Correlations
The Chi-Square Test of Independence
Fisher’s Exact Test for 2 X 2 Tables
Summary
Appendix: Assumptions Underlying the Tests
References

6 Assessing Scale Reliability with Coefficient Alpha
Overview
Introduction: The Basics of Scale Reliability
Cronbach’s Alpha
Computing Cronbach’s Alpha
Summarizing the Results
Summary
References

7 t -Tests: Independent Samples and Paired Samples
Overview
Introduction: Two Types of t -Tests
The Independent-Samples t -Test
The Paired-Samples t -Test
Summary
Appendix: Assumptions Underlying the t -Test
References

8 One-Way ANOVA with One Between-Subjects Factor
Overview
Introduction: Basics of One-Way ANOVA Between-Subjects Design
Example with Significant Differences between Experimental Conditions
Example with Nonsignificant Differences between Experimental Conditions
Understanding the Meaning of the F Statistic
Summary
Appendix: Assumptions Underlying One-Way ANOVA with One Between-Subjects Factor
References

9 Factorial ANOVA with Two Between-Subjects Factors
Overview
Introduction to Factorial Designs
Some Possible Results from a Factorial ANOVA
Example with Nonsignificant Interaction
Example with a Significant Interaction
Summary
Appendix: Assumptions for Factorial ANOVA with Two Between-Subjects Factors
References

10 Multivariate Analysis of Variance (MANOVA) with One Between-Subjects Factor
Overview
Introduction: The Basics of Multivariate Analysis of Variance (MANOVA)
A Multivariate Measure of Association
The Commitment Study
Overview: Performing a MANOVA with the Fit Model Platform
Example with Significant Differences between Experimental Conditions
Example with Nonsignificant Differences between Experimental Conditions
Summary
Appendix: Assumptions Underlying MANOVA with One Between-Subjects Factor
References

11 One-Way ANOVA with One Repeated-Measures Factor
Overview
Introduction: What Is a Repeated-Measures Design?
Example with Significant Differences in Investment Size across Time
Repeated-Measures Design versus the Between-Subjects Design
Univariate or Multivariate ANOVA for Repeated-Measures Analysis?
Summary
Appendix: Assumptions of the Multivariate Analysis of Design with One Repeated-Measures Factor
References

12 Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
Overview
Introduction: The Basics of Mixed-Design ANOVA
Possible Results from a Two-Way Mixed-Design ANOVA
Problems with the Mixed-Design ANOVA
Example with a Nonsignificant Interaction
Example with a Significant Interaction
Summary
Appendix A: An Alternative Approach to a Univariate Repeated-Measures Analysis
Appendix B: Assumptions for Factorial ANOVA with Repeated-Measures and Between-Subjects Factors
References

13 Multiple Regression
Overview
Introduction to Multiple Regression
Predicting a Response from Multiple Predictors
The Results of a Multiple Regression Analysis
Example: A Test of the Investment Model
Computing Simple Statistics and Correlations
Estimating the Full Multiple Regression Equation
Uniqueness Indices for the Predictors
Summarizing the Results
Getting the Big Picture
Formal Description of Results for a Paper
Summary
Appendix: Assumptions Underlying Multiple Regression
References

14 Principal Component Analysis
Overview
Introduction to Principal Component Analysis
The Prosocial Orientation Inventory
Conduct the Principal Component Analysis
Summary
Appendix: Assumptions Underlying Principal Component Analysis
References

Appendix Choosing the Correct Statistic
Overview
Introduction: Thinking about the Number and Scale of Your Variables
Guidelines for Choosing the Correct Statistic
Single Response Variable and Multiple Predictor Variables
Summary

Index
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Using This Book
Purpose
This book provides you with what you need to know to manage JMP data and to perform the statistical analyses that are most commonly used in the social sciences and other areas of research. JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists shows you how to understand the basics of using JMP software enter and manage JMP data understand the correct statistic for a variety of study situations perform an analysis interpret the results prepare tables, figures, and text that summarize the results according to the guidelines of the Publication Manual of the American Psychological Association (the most widely used format in social science literature).
Audience
This book is designed for students and researchers who use Version 10 of JMP or JMP Pro and who have limited backgrounds in statistics, but this book can also be useful for more experienced researchers. An introductory chapter reviews basic concepts in statistics and research methods. The chapters on data and statistical analysis assume that the reader has no familiarity with JMP; all statistical concepts are conveyed at an introductory level. The chapters that deal with specific statistics clearly describe the circumstances under which each is used. Each chapter provides at least one detailed example of data, and describes how to analyze the data and interpret the results for a representative research problem. Even users whose only previous exposure to data analysis was