Statistical Techniques for the Study of Language and Language Behaviour

Toni Rietveld, Roeland van Hout

• Publisher: De Gruyter Mouton
• Year: 2011
• Print ISBN:
  9783110136630
• eBook ISBN:
  9783110871609
• Topic: Languages & Linguistics
• Subtopic: Linguistics
• Index: Languages & Linguistics

No detailed description available for "Statistical Techniques for the Study of Language and Language Behaviour".

Table of contents

• Acknowledgements
• Permission of reproduction
• 1 Statistics and the study of language behaviour
• 1.1 The structure of the book
• 1.2 Basic statistical concepts
• 1.3 A few preliminary remarks
• 1.4 Other techniques
• 1.5 Language statistics
• 2 Analysis of variance
• 2.1 Introduction
• 2.2 A simple example
• 2.3 One-way analysis of variance
• 2.4 Testing effects: the F distribution
• 2.5 Multi-factorial designs and interaction
• 2.6 Random and fixed factors
• 2.7 Testing effects in a two-factor design
• 2.8 The interpretation of interactions
• 2.9 Summary of the procedure
• 2.10 Other design types
• 2.11 A hierarchical three-factor design
• 2.12 Simple main effects
• 2.13 Post hoc comparisons and contrasts
• 2.14 Strength of association
• 2.15 Strange F ratios: testing hypotheses made difficult
• 2.16 Pooling and unequal cell frequencies
• 2.17 Overview of the steps in an analysis of variance
• 3 Multiple regression
• 3.1 Introduction
• 3.2 Simple regression
• 3.3 Tests of significance
• 3.4 Two independent variables
• 3.5 Selecting a model
• 3.6 Multicollinearity
• 3.7 Coding nominal independent variables
• 3.8 Comparing regression lines
• 3.9 Measurement and specification errors
• 4 More ANOVA and MR: assumptions and alternatives
• 4.1 Introduction
• 4.2 Assumptions in analysis of variance
• 4.3 Homoscedasticity
• 4.4 The impact of transformations
• 4.5 Unbalanced designs: unequal cell frequencies
• 4.6 Repeated measures and MANOVA
• 4.7 A nonparametric alternative: randomization tests
• 4.8 Bootstrapping
• 4.9 Assumptions in linear multiple regression
• 4.10 Plots and diagnostics
• 4.11 Outliers and influential observations
• 4.12 Linearizing transformations
• 4.13 Time series analysis
• 4.14 Alternative regression techniques
• 5 Reliability and agreement among raters
• 5.1 Introduction
• 5.2 Reliability: true scores and the error component
• 5.3 Interrater reliability
• 5.4 Interrater agreement
• 5.5 Intrarater reliability and agreement
• 6 Introductory matrix algebra
• 6.1 Introduction
• 6.2 Matrices and vectors
• 6.3 Matrix operations
• 6.4 Three applications
• 6.5 Some special matrices
• 6.6 Some key concepts in matrix algebra
• 6.7 Some matrix operations for statistical data
• 7 Factor analysis
• 7.1 Introduction
• 7.2 Dimensionality reduction
• 7.3 Axis rotation and linear tranformation
• 7.4 Criteria for dimensionality reduction
• 7.5 The role of eigenvalues in principal component analysis
• 7.6 Loadings
• 7.7 A PC analysis with SPSS: the two-variable example
• 7.8 Principal component analysis or factor analysis?
• 7.9 How many factors/components are to be retained?
• 7.10 Which factor loadings are significant?
• 7.11 Data problems
• 7.12 Rotations revisited: rotating factors
• 7.13 Oblique factors
• 7.14 Options
• 7.15 An example of factor analysis
• 7.16 Factor scores
• 7.17 Overview of the steps in a factor analysis
• 8 Analysis of frequencies
• 8.1 Introduction
• 8.2 Log-linear analysis of a 2x2 table
• 8.3 Multi-dimensional contingency tables
• 8.4 Logit analysis
• 8.5 Overview of the steps in a log-linear analysis
• 9 Logistic regression
• 9.1 Introduction
• 9.2 Regression and the logistic function
• 9.3 Evaluating a model
• 9.4 Polytomous independent variables
• 9.5 Stepwise selection
• 9.6 The model matrix approach and GSK
• 9.7 Comparing different techniques
• Appendices
• A: Logarithms
• B: Characteristic root test
• C: Eigenvalues of random data correlation matrices
• D: Command lines statistical programs
• E: Key to the exercises
• F: Statistical tables
• A. Normal Distribution
• B. Critical values of F
• C. Critical values of x2
• Index