Statistical Techniques for the Study of Language and Language Behaviour
1st Edition
Published on 4. August 2011
XI, 400 pages
978-3-11-087160-9 (ISBN)
Description
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More details
Edition
Reprint 2011
Language
English
Place of publication
Berlin/Boston
Germany
Publishing group
de Gruyter Mouton
Target group
Professional and scholarly
US School Grade: College Graduate Student
Illustrations
Zahlr. Abb.
Zahlr. Abb.
File size
12,48 MB
ISBN-13
978-3-11-087160-9 (9783110871609)
Schweitzer Classification
Other editions
Additional editions
Toni Rietveld | Roeland van Hout
Statistical Techniques for the Study of Language and Language Behaviour
Book
01/1993
1st Edition
De Gruyter Mouton
€169.95
Article exhausted; check different version
Toni Rietveld | Roeland van Hout
Statistical Techniques for the Study of Language and Language Behaviour
Book
01/1993
1st Edition
De Gruyter Mouton
€129.95
Shipment within 7-9 days
Content
- Intro
- 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
