Daniel H. Baker (Senior Lecturer (Associate Professor) Senior Lect
Research Methods Using R
Advanced Data Analysis in the Behavioural and Biological Sciences
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Daniel H. Baker (Senior Lecturer (Associate Professor) Senior Lect
Research Methods Using R
Advanced Data Analysis in the Behavioural and Biological Sciences
- Broschiertes Buch
Providing complete coverage of advanced research methods and their implementation in R to increase students' confidence with programming techniques and their application to new situations and problems.
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Providing complete coverage of advanced research methods and their implementation in R to increase students' confidence with programming techniques and their application to new situations and problems.
Produktdetails
- Produktdetails
- Verlag: Oxford University Press
- Seitenzahl: 352
- Erscheinungstermin: 21. März 2022
- Englisch
- Abmessung: 191mm x 245mm x 22mm
- Gewicht: 688g
- ISBN-13: 9780192896599
- ISBN-10: 0192896598
- Artikelnr.: 63266140
- Verlag: Oxford University Press
- Seitenzahl: 352
- Erscheinungstermin: 21. März 2022
- Englisch
- Abmessung: 191mm x 245mm x 22mm
- Gewicht: 688g
- ISBN-13: 9780192896599
- ISBN-10: 0192896598
- Artikelnr.: 63266140
Daniel H. Baker is a Senior Lecturer at the University of York. He has taught research methods for many years in the Department of Psychology, and also made contributions to the statistical literature on power analysis and multivariate methods. He studies human visual perception, with a particular emphasis on binocular vision, using a range of quantitative techniques including psychophysics, neuroimaging and computational modelling. In 2016 he was awarded the David Marr medal by the Applied Vision Association in recognition of his research contributions. He has a particular interest in making research more open, not only by sharing code and data, but also by making analysis techniques more accessible and easy to use.
1: Introduction
2: Introduction to the R environment
3: Cleaning and preparing data for analysis
4: Statistical tests as linear models
5: Power analysis
6: Meta-analysis
7: Mixed-effects models
8: Stochastic methods
9: Non-linear curve fitting
10: Fourier analysis
11: Multivariate t-tests
12: Structural equation modelling
13: Multidimensional scaling and k-means clustering
14: Multivariate pattern analysis
15: Correcting for multiple comparisons
16: Signal detection theory
17: Bayesian statistics
18: Plotting graphs and data visualisation
19: Reproducible data analysis
2: Introduction to the R environment
3: Cleaning and preparing data for analysis
4: Statistical tests as linear models
5: Power analysis
6: Meta-analysis
7: Mixed-effects models
8: Stochastic methods
9: Non-linear curve fitting
10: Fourier analysis
11: Multivariate t-tests
12: Structural equation modelling
13: Multidimensional scaling and k-means clustering
14: Multivariate pattern analysis
15: Correcting for multiple comparisons
16: Signal detection theory
17: Bayesian statistics
18: Plotting graphs and data visualisation
19: Reproducible data analysis
1: Introduction
2: Introduction to the R environment
3: Cleaning and preparing data for analysis
4: Statistical tests as linear models
5: Power analysis
6: Meta-analysis
7: Mixed-effects models
8: Stochastic methods
9: Non-linear curve fitting
10: Fourier analysis
11: Multivariate t-tests
12: Structural equation modelling
13: Multidimensional scaling and k-means clustering
14: Multivariate pattern analysis
15: Correcting for multiple comparisons
16: Signal detection theory
17: Bayesian statistics
18: Plotting graphs and data visualisation
19: Reproducible data analysis
2: Introduction to the R environment
3: Cleaning and preparing data for analysis
4: Statistical tests as linear models
5: Power analysis
6: Meta-analysis
7: Mixed-effects models
8: Stochastic methods
9: Non-linear curve fitting
10: Fourier analysis
11: Multivariate t-tests
12: Structural equation modelling
13: Multidimensional scaling and k-means clustering
14: Multivariate pattern analysis
15: Correcting for multiple comparisons
16: Signal detection theory
17: Bayesian statistics
18: Plotting graphs and data visualisation
19: Reproducible data analysis