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Table of Contents
Intro
Preface
Global table of contents
Contents
Part I The Actors
1 Introduction: Multivariate studies in the Humanities
1.1 Preliminaries
1.1.1 Audience
1.1.2 Before you start
1.1.3 Multivariate analysis
1.1.4 Case studies: Quantification and statistical analysis
1.2 The humanities-What are they?
1.3 Qualitative and quantitative research in the humanities
1.4 Multivariate data analysis
1.5 Data: Formats and types
1.5.1 Data formats
1.5.2 Data characteristics: Measurement levels
1.5.3 Characteristics of data types
1.5.4 From one data format to another
1.6 General structure of the case study chapters
1.7 Author references
1.8 Wikipedia
1.9 Web addresses
2 Data inspection: The data are in. Now what?
2.1 Background
2.1.1 A researcher's nightmare
2.1.2 Getting the data right
2.2 Data inspection: Overview
2.2.1 The normal distribution
2.2.2 Distributions: Individual numeric variables
2.2.3 Inspecting several univariate distributions
2.2.4 Bivariate inspection
2.3 Missing data
2.3.1 Unintentionally missing
2.3.2 Systematically missing
2.3.3 Handling missing data
2.4 Outliers
2.4.1 Characteristics of outliers
2.4.2 Types of outliers
2.4.3 Detection of outliers
2.4.4 Handling outliers
2.5 Testing assumptions of statistical techniques
2.5.1 Null hypothesis testing
2.5.2 Model testing
2.6 Content summary
3 Statistical framework
3.1 Overview
3.2 Data formats
3.2.1 Matrices: The basic data format
3.2.2 Contingency tables
3.2.3 Correlations, covariances, similarities
3.2.4 Three-way arrays: Several matrices
3.2.5 Meaning of numbers in a matrix
3.3 Chapter example
3.4 Designs, statistical models, and techniques
3.4.1 Data design
3.4.2 Model
3.5 From questions to statistical techniques
3.5.1 Dependence designs versus internal structure designs
3.5.2 Analysing variables, objects, or both
3.6 Dependence designs: General linear model-glm
3.6.1 The t test
3.6.2 Analysis of variance-anova
3.6.3 Multiple regression analysis-mra
3.6.4 Discriminant analysis
3.6.5 Logistic regression
3.6.6 Advanced analysis of variance models
3.6.7 Nonlinear multivariate analysis
3.7 Internal structure designs: General description
3.8 Internal structure designs: Variables
3.8.1 Principal component analysis-pca
3.8.2 Categorical principal component analysis-CatPCA
3.8.3 Factor analysis-fa
3.8.4 Structural equation modelling-sem
3.8.5 Loglinear models
3.9 Internal structure designs: Objects, individuals, cases, etc.
3.9.1 Similarities and dissimilarities
3.9.2 Multidimensional scaling-mds
3.9.3 Cluster analysis
3.10 Internal structure designs: Objects and variables
3.10.1 Correspondence analysis: Analysis of tables
3.10.2 Multiple correspondence analysis
Preface
Global table of contents
Contents
Part I The Actors
1 Introduction: Multivariate studies in the Humanities
1.1 Preliminaries
1.1.1 Audience
1.1.2 Before you start
1.1.3 Multivariate analysis
1.1.4 Case studies: Quantification and statistical analysis
1.2 The humanities-What are they?
1.3 Qualitative and quantitative research in the humanities
1.4 Multivariate data analysis
1.5 Data: Formats and types
1.5.1 Data formats
1.5.2 Data characteristics: Measurement levels
1.5.3 Characteristics of data types
1.5.4 From one data format to another
1.6 General structure of the case study chapters
1.7 Author references
1.8 Wikipedia
1.9 Web addresses
2 Data inspection: The data are in. Now what?
2.1 Background
2.1.1 A researcher's nightmare
2.1.2 Getting the data right
2.2 Data inspection: Overview
2.2.1 The normal distribution
2.2.2 Distributions: Individual numeric variables
2.2.3 Inspecting several univariate distributions
2.2.4 Bivariate inspection
2.3 Missing data
2.3.1 Unintentionally missing
2.3.2 Systematically missing
2.3.3 Handling missing data
2.4 Outliers
2.4.1 Characteristics of outliers
2.4.2 Types of outliers
2.4.3 Detection of outliers
2.4.4 Handling outliers
2.5 Testing assumptions of statistical techniques
2.5.1 Null hypothesis testing
2.5.2 Model testing
2.6 Content summary
3 Statistical framework
3.1 Overview
3.2 Data formats
3.2.1 Matrices: The basic data format
3.2.2 Contingency tables
3.2.3 Correlations, covariances, similarities
3.2.4 Three-way arrays: Several matrices
3.2.5 Meaning of numbers in a matrix
3.3 Chapter example
3.4 Designs, statistical models, and techniques
3.4.1 Data design
3.4.2 Model
3.5 From questions to statistical techniques
3.5.1 Dependence designs versus internal structure designs
3.5.2 Analysing variables, objects, or both
3.6 Dependence designs: General linear model-glm
3.6.1 The t test
3.6.2 Analysis of variance-anova
3.6.3 Multiple regression analysis-mra
3.6.4 Discriminant analysis
3.6.5 Logistic regression
3.6.6 Advanced analysis of variance models
3.6.7 Nonlinear multivariate analysis
3.7 Internal structure designs: General description
3.8 Internal structure designs: Variables
3.8.1 Principal component analysis-pca
3.8.2 Categorical principal component analysis-CatPCA
3.8.3 Factor analysis-fa
3.8.4 Structural equation modelling-sem
3.8.5 Loglinear models
3.9 Internal structure designs: Objects, individuals, cases, etc.
3.9.1 Similarities and dissimilarities
3.9.2 Multidimensional scaling-mds
3.9.3 Cluster analysis
3.10 Internal structure designs: Objects and variables
3.10.1 Correspondence analysis: Analysis of tables
3.10.2 Multiple correspondence analysis