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Verlagstext: Das Buch führt in Grundprinzipien der Stichprobenziehung ein. Dabei stehen Motivation und anschauliche Beschreibung der Verfahren im Vordergrund. Nach einer generellen Einführung werden sowohl Modell-basierte als auch Design-basierte Stichprobenverfahren wie Clusterstichprobe und geschichtete Stichprobe entwickelt. Jedes Kapitel wird abgeschlossen mit einer Beschreibung, wie die Verfahren in R umgesetzt werden können. Hierdurch wird die Leserin und der Leser in die Lage versetzt selbst komplexe Stichprobenverfahren direkt in R umsetzen zu können. Ein Ausblick auf weitere Verfahren und praktische Probleme schliesst jedes Kapitel des Buches ab

"An R Companion for Applied Statistics II: Multivariable and Multivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use R to analyze multivariate data. The book focuses on the statistics generally covered in an intermediate or multivariate statistics course and provides one or two ways to run each analysis in R. The book has been designed to be an R companion to Rebecca M. Warner's Applied Statistics II: Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. However, this text can also be used as a stand-alone R guide for a multivariate statistics course, without reference to the Warner text. Datasets and scripts to run the examples are provided on an accompanying website"--

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Readings -- Acknowledgments -- Author Biographies -- List of Abbreviations -- Chapter 1 Survey Sampling and Data Collection -- 1.1 Research Interest/Question -- 1.2 Some Basic Terminology -- 1.3 Sample Properties -- 1.4 Probability Sampling Design -- 1.4.1 Simple Random Sampling Design -- 1.4.2 Stratified Random Sampling Design -- 1.4.3 Cluster Random Sampling Design -- 1.4.4 Systematic Random Sampling Design -- 1.4.5 Complex Sampling Design or Multi-Stage Sampling -- 1.5 Nonprobability Sampling Schemes -- 1.6 Surveys -- 1.6.1 Studies -- 1.6.2 Cohort Study -- 1.6.3 Cross-Sectional Studies -- 1.6.4 Case-Control Studies -- 1.7 Key Terms in Surveys -- 1.8 Examples of Public Health Surveys -- 1.8.1 Los Angeles County Health Survey (2015) -- 1.8.2 National Crime Victimization Survey -- References -- Chapter 2 Measures of Tendency, Spread, Relative Standing, Association, and Belief -- 2.1 Research Interest/Question -- 2.2 Body Size, Glycohemoglobin, and Demographics from NHANES 2009-2010 Data -- 2.3 Statistical Measures of Central Tendency and Measures of Spread -- 2.3.1 Variables -- 2.3.2 Measures of Central Tendency -- 2.3.3 Measures of Spread or Variation or Dispersion -- 2.3.4 Measures of Relative Standing -- 2.3.4.1 Single Values -- 2.3.4.2 Interval Values -- 2.3.5 Measure of Skewness -- 2.3.6 Measure of Kurtosis -- 2.4 Results from NHANES Data on BMI -- 2.4.1 Analysis Results with SAS Program -- 2.4.2 Analysis Results with R Program -- 2.5 Measures of Association of Two Continuous Variables -- 2.6 NHANES Data on Measures of Association for Continuous Variables -- 2.6.1 Analysis of Data with SAS Program -- 2.6.2 Analysis of Data with R Program -- 2.7 Association Between Two Categorical Variables -- 2.7.1 Contingency Table -- 2.7.2 Odds Ratio.

"The purpose of this chapter is to introduce the fundamentals of the R programming language, and the basic tools you will need to use this book; it is therefore an important chapter for readers new to R. The reader is advised, following reading this chapter, to proceed to Chapter 2 which, together with this chapter, forms our introduction to programming in R. R is an exceptional statistical computing tool which is increasingly used by researchers and students in many disciplines. R is an open-source language which one can use without purchasing a licence, and contribute to solving problems within the vast R community."--

"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."--Publisher's description

"A Guide to R for Social and Behavioral Science Statistics is a short, accessible book for learning R. This handy guide contains basic information on statistics for undergraduates and graduate students, shown in the R statistical language using RStudio. The book is geared toward social and behavioral science statistics students, especially those with no background in computer science. Written as a companion book to be used alongside a larger introductory statistics text, the text follows the most common progression of statistics for social scientists. The guide also serves as a companion for conducting data analysis in a research methods course or as a stand-alone R and statistics text. This guide can teach anyone how to use R to analyze data, and uses frequent reminders of basic statistical concepts to accompany instructions in R to help walk students through the basics of learning how to use R for statistics"--