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E-raamat: Complete Data Analysis Using R: Your Applied Manual

  • Formaat: 408 pages
  • Ilmumisaeg: 10-Nov-2022
  • Kirjastus: Sage Publications Ltd
  • Keel: eng
  • ISBN-13: 9781529736908
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Complete Data Analysis Using R: Your Applied ManualSuurem pilt
  • Formaat: 408 pages
  • Ilmumisaeg: 10-Nov-2022
  • Kirjastus: Sage Publications Ltd
  • Keel: eng
  • ISBN-13: 9781529736908
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This book gets you up and running with using R in your research project, focusing on data analysis.

This step-by-step guide shows you how to use R to get data analysis right.

The book explores the entire process of analysis, covering key steps from preparing your data to putting your analysis together and writing up your findings. It helps you get to grips with doing different statistical techniques in R and:

  • Equips you with practical data visualisation tools to create graphs and tables.
  • Shows you how to prepare and present your research for assessment, publication and dissemination.
  • Covers key issues facing today’s social scientists, such as making research reproducible.

Features include an introduction to each chapter, and end-of-chapter exercises to check your understanding of the material. The online resources for this text include data sets that you can perform your own analysis on, and links to publications that are relevant to programming with R. 

A good starting point for any postgraduate student conducting a research project, this book will help you develop your statistics and programming knowledge and get quickly up to speed.

List of R examples
ix
List of figures
xi
List of tables
xv
List of R scripts
xvii
Preface xxi
Introduction xxiii
Discover the online resources xxv
1 Quick start -- basic training of R functions
1(28)
1.1 Jump-starting R -- Doing statistics at the R console
2(9)
1.2 Programming statistics
11(2)
1.3 Creating data with a data frame or a matrix
13(9)
1.4 Getting help in R
22(2)
1.5 Teamwork and project work -- Our lives as researchers
24(5)
2 Getting your data in and out of R
29(36)
2.1 Examples in this book
30(2)
2.2 Load and import research data
32(10)
2.3 Data scrutiny
42(12)
2.4 Save, export, and archive the data
54(11)
3 Preparing the data for analysis
65(44)
3.1 Data transformations
66(10)
3.2 Types of variables
76(6)
3.3 Attributes -- Adjunct information for variables
82(11)
3.4 Factors -- A special type of variable
93(8)
3.5 Structuring the complete analysis I
101(8)
4 Descriptive and exploratory data analysis
109(46)
4.1 Data exploration and data quality
110(1)
4.2 Descriptive statistics
111(20)
4.3 Powerful helper functions and function teams for data exploration
131(12)
4.4 A graphical user interface to explore data -- The RCommander
143(1)
4.5 Structuring the complete analysis II
144(11)
5 Graphical data analysis
155(44)
5.1 Quick graphical data exploration
156(12)
5.2 Enhancing the graphical display
168(10)
5.3 Checking model assumptions with graphics
178(5)
5.4 Arranging multiple graphics in one display
183(16)
6 Inferential statistics I: a completely randomised factorial design
199(32)
6.1 Review of analysis of variance (ANOVA)
200(4)
6.2 Model specification for the ANOVA in R
204(11)
6.3 Contrasts for the ANOVA
215(2)
6.4 Two-factor ANOVA model
217(4)
6.5 More complex ANOVA models
221(10)
7 Inferential statistics II: a multiple regression analysis
231(36)
7.1 Linear regression analysis with one or two predictors
232(12)
7.2 Multiple linear regression
244(9)
7.3 Further topics in regression
253(4)
7.4 Logistic regression
257(10)
8 Preparing tables for publication
267(46)
8.1 Results tables for publication
268(25)
8.2 Report generation
293(13)
8.3 Structuring the output of results
306(7)
9 Preparing graphics for publication
313(30)
9.1 Planning graphics for publication
314(2)
9.2 Graphics from scratch
316(7)
9.3 Graphic systems
323(20)
10 Data simulation
343(22)
10.1 The idea of data simulation
344(1)
10.2 Data simulation in R
345(8)
10.3 Simulation applications
353(12)
11 Putting it all together: the structure of a statistical analysis
365(12)
11.1 Programming style
366(1)
11.2 Management of a complete statistical analysis
367(6)
11.3 Where to go from here
373(4)
References 377(4)
Index 381
Marco Lehmann is a psychologist and music psychologist. He works as a researcher and consultant for empirical methods at the University Medical Center Hamburg-Eppendorf, Germany. He obtained his diploma in psychology at the University of Kiel, Germany, and his M. Sc. in Music Psychology at Keele University, UK. He completed his PhD at the Hanover University of Music, Drama and Media, Germany. Throughout his work as an empirical researcher, he maintained a strong affiliation with statistics and R programming. With his expertise in applied research he taught young researchers from several scientific domains how R figures in all steps of data analysis. If Marco is not programming R in his leisure time he plays guitar and bass guitar. He lives with his wife and two daughters near Hamburg, Germany.
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