Research Catalog

Title

A step-by-step approach to using the SAS system for univariate and multivariate statistics / Larry Hatcher, Edward J. Stepanski.

Author

Hatcher, Larry

Publication

Cary, N.C. : SAS Institute, c1994.

Items in the Library & Off-site

Holdings

Details

Additional Authors

Stepanski, Edward J.

Description

xiv, 532 p. : ill.; 28 cm.

Summary

"Researchers and students of the Social Sciences will find this book a user-friendly introduction to both the SAS System and elementary statistical procedures. Its clear presentation of more advanced statistical procedures makes it an invaluable reference guide for more experienced researchers as well. Step by step, this book guides you through the basic concepts of research and data analysis, to data input, and on to ANOVA and MANOVA. Book jacket."--BOOK JACKET.

Subject

• Mathematical statistics > Data processing
• Multivariate Analysis
• SAS (Computer file)

Bibliography (note)

• Includes bibliographical references and index.

Processing Action (note)

• committed to retain

Contents

• Basic Concepts in Research and Data Analysis -- Introduction: A Common Language for Researchers -- Steps to Follow when Conducting Research -- Variables, Values, and Observations -- Scales of Measurement -- Basic Approaches to Research -- Descriptive Versus Inferential Statistical Analysis -- Hypothesis Testing -- Introduction to SAS Programs, SAS Logs, and SAS Output -- Introduction: What Is the SAS System? -- Three Types of SAS System Files -- Data Input -- Introduction: Inputting Questionnaire Data versus Other Types of Data -- Keying Data: An Illustrative Example -- Inputting Data Using the CARDS Statement -- Additional Guidelines -- Inputting a Correlation or Covariance Matrix -- Inputting Data Using the INFILE Statement Rather than the CARDS Statement -- Controlling the Size of the Ouput and Log Pages with the OPTIONS Statement -- Working with Variables and Observations in SAS Data Sets -- Introduction: Manipulating, Subsetting, Concatenating, and Merging Data --^
• Placement of Data Manipulation and Data Subsetting Statements -- Data Manipulation -- Data Subsetting -- A More Comprehensive Example -- Concatenating and Merging Data Sets -- Exploring Data with PROC MEANS, PROC FREQ, PROC PRINT, and PROC UNIVARIATE -- Introduction: Why Perform Simple Descriptive Analyses? -- Example: A Revised Volunteerism Survey -- Computing Descriptive Statistics with PROC MEANS -- Creating Frequency Tables with PROC FREQ -- Printing Raw Data with PROC PRINT -- Testing for Normality with PROC UNIVARIATE -- Measures Of Bivariate Association -- Introduction: Significance Tests versus Measures of Association -- Choosing the Correct Statistic -- Pearson Correlations -- Spearman Correlations -- The Chi-Square Test of Independence -- Assumptions Underlying the Tests -- t Tests: Independent Samples and Paired Samples -- Introduction: Two Types of t Tests -- The Independent-Samples t Test -- The Paired-Samples t Test -- Assumptions Underlying the t Test --^
• One-Way ANOVA with One Between-Groups Factor -- Introduction: The Basics of One-Way ANOVA, Between-Groups Design -- Example with Significant Differences between Experimental Conditions -- Example with Nonsignificant Differences between Experimental Conditions -- Understanding the Meaning of the F Statistic -- Assumptions Underlying One-Way ANOVA with One Between-Groups Factor -- Factorial ANOVA with Two Between-Groups Factors -- Introduction to Factorial Designs -- Some Possible Results from a Factorial ANOVA -- Example with a Nonsignificant Interaction -- Example with a Significant Interaction -- Assumptions Underlying Factorial ANOVA with Two Between-Groups Factors -- Multivariate Analysis of Variance (MANOVA), with One Between-Groups Factor -- Introduction: The Basics of Multivariate Analysis of Variance -- Example with Significant Differences between Experimental Conditions -- Example with Nonsignificant Differences between Experimental Conditions --^
• Assumptions Underlying Multivariate ANOVA with One Between-Groups Factor -- One-Way ANOVA with One Repeated-Measures Factor -- Introduction: What is a Repeated-Measures Design? -- Example: Significant Differences in Investment Size across Time -- Further Notes on Repeated-Measures Analyses -- Assumptions Underlying the One-Way ANOVA with One Repeated-Measures Factor -- Factorial ANOVA with Repeated-Measures Factors and Between-Groups Factors -- Introduction: The Basics of Mixed-Design ANOVA -- Some 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 -- Use of Other Post-Hoc Tests with the Repeated-Measures Variable -- Assumptions Underlying Factorial ANOVA with Repeated-Measures Factors and Between-Groups Factors -- Multiple Regression -- Introduction: Answering Questions with Multiple Regression --^
• Background: Predicting a Criterion Variable from Multiple Predictors -- The Results of a Multiple Regression Analysis -- Example: A Test of the Investment Model -- Overview of the Analysis -- Gathering and Inputting Data -- Computing Bivariate Correlations with PROC CORR -- Estimating the Full Multiple Regression Equation with PROC REG -- Computing Uniqueness Indices with PROC REG -- Summarizing the Results in Tables -- Getting the Big Picture -- Formal Description of Results for a Paper -- Conclusion: Learning More about Multiple Regression -- Assumptions Underlying Multiple Regression -- Principal Component Analysis -- Introduction: The Basics of Principal Component Analysis -- Example: Analysis of the Prosocial Orientation Inventory -- SAS Program and Output -- Steps in Conducting Principal Component Analysis -- An Example with Three Retained Components -- Assumptions Underlying Principal Component Analysis -- Assessing Scale Reliability with Coefficient Alpha --^
• Introduction: The Basics of Scale Reliability -- Coefficient Alpha -- Assessing Coefficient Alpha with PROC CORR -- Choosing the Correct Statistic -- Introduction: Thinking about the Number and Scale of Your Variables -- Data Sets -- Critical Values of the F Distribution.

ISBN

1555446345

Owning Institutions

Harvard Library