Author : Norm O'Rourke
Publisher : SAS Institute
Page : 552 pages
File Size : 18,92 MB
Release : 2005
Category : Computers
ISBN : 1590474171
Providing practice data inspired by actual studies, this book explains how to choose the right statistic, understand the assumptions underlying the procedure, prepare an SAS program for an analysis, interpret the output, and summarize the analysis and results according to the format prescribed in the Publication Manual of the American Psychological Association.
Author : Norm O'Rourke
Publisher : Wiley-Interscience
Page : 0 pages
File Size : 36,21 MB
Release : 2008-03-14
Category : Mathematics
ISBN : 9780470388006
This set contains: 9780471469445 A Step-by-Step Approach to Using SAS for Univariate & Multivariate Statistics, Second Edition by Norm O'Rourke, Larry Hatcher, Edward J. Stepanski, SAS Institute Inc and 9780471226185 An Introduction to Categorical Data Analysis, Second Edition by Alan Agresti.
Author : Ann Lehman
Publisher : SAS Institute
Page : 559 pages
File Size : 18,36 MB
Release : 2013
Category : Computers
ISBN : 1612906036
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 that is 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.
Author : Alan Agresti
Publisher : John Wiley & Sons
Page : 393 pages
File Size : 44,62 MB
Release : 2018-10-11
Category : Mathematics
ISBN : 1119405270
A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
Author : Ruth M. Tappen
Publisher : Jones & Bartlett Publishers
Page : 522 pages
File Size : 33,59 MB
Release : 2010-08-27
Category : Medical
ISBN : 1449656080
Written by an active nurse researcher, this graduate-level text takes a practical approach to preparing research proposals and carrying out research studies. It moves the reader through the entire process of conducting a research study from selecting a topic to publishing the results and discusses both theoretical and practical considerations in conducting a study. Advanced Nursing Research: From Theory to Practice uses real life examples to address topics such as sampling, participant recruitment, data collection, presenting research, and a career in research.
Author : Ruth M. Tappen
Publisher : Jones & Bartlett Learning
Page : 630 pages
File Size : 48,1 MB
Release : 2022-01-19
Category : Medical
ISBN : 1284231755
Advanced Nursing Research: From Theory to Practice, Third Edition is the ideal graduate-level text for learning how to conduct nursing research, from development of an idea to the completion of the study. It focuses on the conduct of research with an emphasis on the connection to evidence-based practice, quality improvement, and the use of aggregate data. Despite its wide scope, this text is concise with little repetition. The outstanding feature is its reality-based approach to the actual conduct of research. Difficult, complex topics are addressed in a readable manner while the author uses her own experience and stories about conducting a wide range of research studies to engage students. Advanced Nursing Research: From Theory to Practice, Third Edition reflects modern practice and current thinking about research and integrates qualitative and quantitative methods, including emerging mixed methods.
Author : Larry Hatcher
Publisher : SAS Institute
Page : 444 pages
File Size : 23,78 MB
Release : 2013-03-01
Category : Computers
ISBN : 1612903878
Annotation Structural equation modeling (SEM) has become one of the most important statistical procedures in the social and behavioral sciences. This easy-to-understand guide makes SEM accessible to all userseven those whose training in statistics is limited or who have never used SAS. It gently guides users through the basics of using SAS and shows how to perform some of the most sophisticated data-analysis procedures used by researchers: exploratory factor analysis, path analysis, confirmatory factor analysis, and structural equation modeling. It shows how to perform analyses with user-friendly PROC CALIS, and offers solutions for problems often encountered in real-world research. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.
Author : Alan Agresti
Publisher : John Wiley & Sons
Page : 756 pages
File Size : 28,76 MB
Release : 2013-04-08
Category : Mathematics
ISBN : 1118710940
Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
Author : Rudolf Freund
Publisher : John Wiley & Sons
Page : 258 pages
File Size : 27,14 MB
Release : 2000-12-29
Category : Mathematics
ISBN : 0471416649
SAS® System for Regression Learn to perform a wide variety of regression analyses using SAS® software with this example-driven revised favorite from SAS Publishing. With this Third Edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics covered include performing linear regression analyses using PROC REG diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and the SAS System are assumed. New for this edition The Third Edition includes revisions, updated material, and new material. You’ll find new information on using SAS/INSIGHT® software regression with a binary response with emphasis on PROC LOGISTIC nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, using the OUTEST option to produce a data set, and using PROC SCORE to predict another data set.
Author : Brian Everitt
Publisher : Springer Science & Business Media
Page : 284 pages
File Size : 11,37 MB
Release : 2011-04-23
Category : Mathematics
ISBN : 1441996508
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
