## A Step-by-Step Approach to Using SAS for Univariate & Multivariate StatisticsUpdated for SAS 9, A Step-by-Step Approach to Using SAS for Univariate and Multivariate Statistics, Second Edition, is an easy-to-understand introduction to SAS as well as to univariate and multivariate statistics. Clear explanations and simple language guide you through the research terminology, data input, data manipulation, and types of statistical analysis that are most commonly used in the social and behavioral sciences. Providing practice data inspired by actual studies, this book teaches you how to choose the right statistic, understand the assumptions underlying the procedure, prepare the SAS program for the analysis, interpret the output, and summarize the analysis and results according to the format prescribed in the Publication Manual of the American Psychological Association. Step by step, authors Norm O'Rourke, Larry Hatcher, and Edward Stepanski demonstrate how to perform the following types of analysis: simple descriptive statistics, measures of bivariate association, t tests for independent samples and paired samples, ANOVA and MANOVA, multiple regression, principal component analysis, and assessing scale reliability with coefficient alpha. This text is ideally suited to students who are beginning their study of data analysis, and to professors and researchers who want a handy reference on their bookshelf. |

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### Contents

Basic Concepts in Research and DATA Analysis | 1 |

Introduction to SAS Programs SAS Logs and SAS Output | 21 |

Data Input | 29 |

Working with Variables and Observations in SAS Datasets | 57 |

Exploring Data with PROC MEANS PROC FREQ PROC PRINT and PROC UNIVARIATE | 89 |

Measures of Bivariate Association | 119 |

Assessing Scale Reliability with Coefficient Alpha | 155 |

Independent Samples and Paired Samples | 167 |

Multivariate Analysis of Variance MANOVA with One Between Subjects Factor | 279 |

OneWay ANOVA with One Repeated Measures Factor | 299 |

Factorial ANOVA with Repeated Measures Factors and Between Subjects Factors | 325 |

Multiple Regression | 367 |

Principal Component Analysis | 429 |

Choosing the Correct Statistic | 483 |

Datasets | 491 |

Critical Values of the F Distribution | 495 |

### Other editions - View all

A Step-by-step Approach to Using SAS for Univariate & Multivariate Statistics Edward J. Stepanski No preview available - 2005 |

### Common terms and phrases

alternative Analysis of Variance analyzed appear assessed chapter column commitment scores compute correlation coefficient COSTGRP criterion variable DATA=D1 DATALINES dataset degrees of freedom descriptive statistics displayed eigenvalue error example experimental group F statistic F Value Pr Factorial ANOVA Figure GLM Procedure GREMATH GREVERBAL high-reward condition includes independent variable indicates INPUT statement interaction interpret investment model investment scores level of measurement low-reward condition main effect matrix multiple comparison multiple regression multivariate nonsignificant null hypothesis number of observations observed variables one-way ANOVA option paired-samples partner Pearson correlation perform plot population preceding program predicted predictor variable principal component analysis PROC CORR PROC GLM PROC MEANS prosocial behavior provides questionnaire regression coefficients reject the null relationship Repeated Measures repeated-measures factor responses REWGRP sample SAS program SAS System scale scree simple effect Square F Value standard deviations type of rewards uniqueness index univariate