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Modern psychometrics with R. (English) Zbl 1414.62006

Use R!. Cham: Springer (ISBN 978-3-319-93175-3/pbk; 978-3-319-93177-7/ebook). xiii, 458 p. (2018).
The book gives an exhaustive overview of statistical methods that may be used when analyzing results of research in psychology. Main accent is on the use of R software during analysis of data. The main goal of the book is to provide the reader with main methods used for data analysis and how those methods may be executed using software package R. Many quite comprehensive examples on how R may be used in practice are provided. Since some methods are quite complex mathematical/statistical methods author firstly gives a short reader-friendly introduction of the method avoiding very technical details. The author begins from classical True Score Model and more complex methods are explained later in the book. Every chapter is finalized with list of references so the interested reader may easily deepen knowledge on particular method.
Classical True Score Model is examined at the beginning as well as some reliability coefficients, including Cronbach’s alpha, are introduced and examples on how to use R functions and packages are given. In Chapters 2–7 methods from factor analysis, path analysis and structural equation models (including but not limited to multivariate regression), item response theory, preference modelling, principal component analysis and correspondence analysis are provided. Gifi methods related to the problem of optimal scaling as well as principal component analysis and correspondence analysis are considered in Chapter 8. Multidimensional scaling which deals with proximities among objects as distances among points are explored in Chapter 9. Biplots, networks and methods of parametric cluster analysis are introduced in Chapters 10–12. In Chapter 13, the author shows how Markov models and time series may be used in psychology experiments. The last chapter provides overview of fMRI (functional magnetic resonance imaging) data. Using fMRI the brain of participants is scanned over time. The data received are usually in 4D format, so quite complex statistical methods are required.
Majority of methods are presented in three parts – firstly short description of model is given avoiding very technical details but focusing on issue when to apply the method; then instructions how to realize the model with R are presented to the reader and finally some examples of visualization and interpretation of results are given. Reader is acquainted with quite complex methods in quite casual language, so even those with basic knowledge of statistics may benefit from this book.
All in all, the book provides a good overview of modern statistical methods that may be used in psychology and the way how these methods may be used in practice using the software package R.

MSC:

62-02 Research exposition (monographs, survey articles) pertaining to statistics
62P15 Applications of statistics to psychology
62H25 Factor analysis and principal components; correspondence analysis
62H20 Measures of association (correlation, canonical correlation, etc.)
68N15 Theory of programming languages
91E45 Measurement and performance in psychology
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62H30 Classification and discrimination; cluster analysis (statistical aspects)
92C55 Biomedical imaging and signal processing
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