Package: psych 2.4.6.26
psych: Procedures for Psychological, Psychometric, and Personality Research
A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.
Authors:
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psych.pdf |psych.html✨
psych/json (API)
NEWS
# Install 'psych' in R: |
install.packages('psych', repos = c('https://revelle.r-universe.dev', 'https://cloud.r-project.org')) |
- Bechtoldt - Seven data sets showing a bifactor solution.
- Bechtoldt.1 - Seven data sets showing a bifactor solution.
- Bechtoldt.2 - Seven data sets showing a bifactor solution.
- Chen - 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation
- Dwyer - 8 cognitive variables used by Dwyer for an example.
- Garcia - Data from the sexism
- Gleser - Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.
- Gorsuch - Example data set from Gorsuch (1997) for an example factor extension.
- Harman.5 - Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
- Harman.8 - Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
- Harman.Burt - Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
- Harman.Holzinger - Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
- Harman.political - Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
- Holzinger - Seven data sets showing a bifactor solution.
- Holzinger.9 - Seven data sets showing a bifactor solution.
- Reise - Seven data sets showing a bifactor solution.
- Schmid - 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation
- Tal.Or - Data set testing causal direction in presumed media influence
- Tal_Or - Data set testing causal direction in presumed media influence
- Thurstone - Seven data sets showing a bifactor solution.
- Thurstone.33 - Seven data sets showing a bifactor solution.
- Thurstone.33G - Seven data sets showing a bifactor solution.
- Thurstone.9 - Seven data sets showing a bifactor solution.
- Tucker - 9 Cognitive variables discussed by Tucker and Lewis
- West - 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation
- bfi - 25 Personality items representing 5 factors
- bfi.dictionary - 25 Personality items representing 5 factors
- bfi.keys - 25 Personality items representing 5 factors
- bock.table - Bock and Liberman (1970) data set of 1000 observations of the LSAT
- cattell - 12 cognitive variables from Cattell
- lsat6 - Bock and Liberman (1970) data set of 1000 observations of the LSAT
- lsat7 - Bock and Liberman (1970) data set of 1000 observations of the LSAT
- sat.act - 3 Measures of ability: SATV, SATQ, ACT
- schmid.leiman - 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation
- small.msq - A small example data set taken from a larger data set
- withinBetween - An example of the distinction between within group and between group correlations
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 months agofrom:26a808800d. Checks:OK: 5 NOTE: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 26 2024 |
R-4.5-win | OK | Oct 26 2024 |
R-4.5-linux | OK | Oct 26 2024 |
R-4.4-win | OK | Oct 26 2024 |
R-4.4-mac | OK | Oct 26 2024 |
R-4.3-win | NOTE | Oct 26 2024 |
R-4.3-mac | NOTE | Oct 26 2024 |
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Dependencies:GPArotationlatticemnormtnlme