R: A package for personality, psychometric, and psychological...

00.psych {psych} | R Documentation

## A package for personality, psychometric, and psychological research

### Description

Overview of the psych package.

The psych package has been developed at Northwestern University to include functions most useful for personality and psychological research. Some of the functions (e.g., `read.file`, `read.clipboard`, `describe`, `pairs.panels`, `error.bars` and `error.dots`) are useful for basic data entry and descriptive analyses. Use help(package="psych") or objects("package:psych") for a list of all functions Two vignettes are included as part of the package. The overview vignette provides examples of using psych in many applications.

Psychometric applications include routines (`fa` for maximum likelihood (fm="mle"), minimum residual (fm="minres"), minimum rank (fm=minrank) principal axes (fm="pa") and weighted least squares (fm="wls") factor analysis as well as functions to do Schmid Leiman transformations (`schmid`) to transform a hierarchical factor structure into a bifactor solution. Principal Components Analysis (`pca` is also available. Rotations may be done using Factor or components transformations to a target matrix include the standard Promax transformation (`Promax`), a transformation to a cluster target, or to any simple target matrix (`target.rot`) as well as the ability to call many of the GPArotation functions. Functions for determining the number of factors in a data matrix include Very Simple Structure (`VSS`) and Minimum Average Partial correlation (`MAP`). An alternative approach to factor analysis is Item Cluster Analysis (`ICLUST`). Reliability coefficients alpha (`scoreItems`, `score.multiple.choice`), beta (`ICLUST`) and McDonald's omega (`omega` and `omega.graph`) as well as Guttman's six estimates of internal consistency reliability (`guttman`) and the six measures of Intraclass correlation coefficients (`ICC`) discussed by Shrout and Fleiss are also available.

The `scoreItems`, and `score.multiple.choice` functions may be used to form single or multiple scales from sets of dichotomous, multilevel, or multiple choice items by specifying scoring keys.

Additional functions make for more convenient descriptions of item characteristics include 1 and 2 parameter Item Response measures. The `tetrachoric`, `polychoric` and `irt.fa` functions are used to find 2 parameter descriptions of item functioning. `scoreIrt`, `scoreIrt.1pl` and `scoreIrt.2pl` do basic IRT based scoring.

A number of procedures have been developed as part of the Synthetic Aperture Personality Assessment (SAPA) project. These routines facilitate forming and analyzing composite scales equivalent to using the raw data but doing so by adding within and between cluster/scale item correlations. These functions include extracting clusters from factor loading matrices (`factor2cluster`), synthetically forming clusters from correlation matrices (`cluster.cor`), and finding multiple ((`setCor`) and partial ((`partial.r`) correlations from correlation matrices.

Functions to generate simulated data with particular structures include `sim.circ` (for circumplex structures), `sim.item` (for general structures) and `sim.congeneric` (for a specific demonstration of congeneric measurement). The functions `sim.congeneric` and `sim.hierarchical` can be used to create data sets with particular structural properties. A more general form for all of these is `sim.structural` for generating general structural models. These are discussed in more detail in the vignette (psych_for_sem).

Functions to apply various standard statistical tests include `p.rep` and its variants for testing the probability of replication, `r.con` for the confidence intervals of a correlation, and `r.test` to test single, paired, or sets of correlations.

In order to study diurnal or circadian variations in mood, it is helpful to use circular statistics. Functions to find the circular mean (`circadian.mean`), circular (phasic) correlations (`circadian.cor`) and the correlation between linear variables and circular variables (`circadian.linear.cor`) supplement a function to find the best fitting phase angle (`cosinor`) for measures taken with a fixed period (e.g., 24 hours).

A dynamic model of personality and motivation (the Cues-Tendency-Actions model) is include as (`cta`.

The most recent development version of the package is always available for download as a source file from the repository at http://personality-project.org/r/src/contrib/.

### Details

Two vignettes (overview.pdf and psych_for_sem.pdf) are useful introductions to the package. They may be found as vignettes in R or may be downloaded from http://personality-project.org/r/book/overview.pdf and http://personality-project.org/r/book/psych_for_sem.pdf.

The more important functions in the package are for the analysis of multivariate data, with an emphasis upon those functions useful in scale construction of item composites. However, there are a number of very useful functions for basic data manipulation including `read.file`, `read.clipboard`, `describe`, `pairs.panels`, `error.bars` and `error.dots`) which are useful for basic data entry and descriptive analyses.

When given a set of items from a personality inventory, one goal is to combine these into higher level item composites. This leads to several questions:

1) What are the basic properties of the data? `describe` reports basic summary statistics (mean, sd, median, mad, range, minimum, maximum, skew, kurtosis, standard error) for vectors, columns of matrices, or data.frames. `describeBy` provides descriptive statistics, organized by one or more grouping variables. `statsBy` provides even more detail for data structured by groups including within and between correlation matrices, ICCs for group differences, as well as basic descriptive statistics organized by group.

`pairs.panels` shows scatter plot matrices (SPLOMs) as well as histograms and the Pearson correlation for scales or items. `error.bars` will plot variable means with associated confidence intervals. `errorCircles` will plot confidence intervals for both the x and y coordinates. `corr.test` will find the significance values for a matrix of correlations. `error.dots` creates a dot chart with confidence intervals.

2) What is the most appropriate number of item composites to form? After finding either standard Pearson correlations, or finding tetrachoric or polychoric correlations, the dimensionality of the correlation matrix may be examined. The number of factors/components problem is a standard question of factor analysis, cluster analysis, or principal components analysis. Unfortunately, there is no agreed upon answer. The Very Simple Structure (`VSS`) set of procedures has been proposed as on answer to the question of the optimal number of factors. Other procedures (`VSS.scree`, `VSS.parallel`, `fa.parallel`, and `MAP`) also address this question. `nfactors` combine several of these approaches into one convenient function.

3) What are the best composites to form? Although this may be answered using principal components (`principal`), principal axis (`factor.pa`) or minimum residual (`factor.minres`) factor analysis (all part of the `fa` function) and to show the results graphically (`fa.diagram)`, it is sometimes more useful to address this question using cluster analytic techniques. Previous versions of `ICLUST` (e.g., Revelle, 1979) have been shown to be particularly successful at forming maximally consistent and independent item composites. Graphical output from `ICLUST.graph` uses the Graphviz dot language and allows one to write files suitable for Graphviz. If Rgraphviz is available, these graphs can be done in R.

Graphical organizations of cluster and factor analysis output can be done using `cluster.plot` which plots items by cluster/factor loadings and assigns items to that dimension with the highest loading.

4) How well does a particular item composite reflect a single construct? This is a question of reliability and general factor saturation. Multiple solutions for this problem result in (Cronbach's) alpha (`alpha`, `scoreItems`), (Revelle's) Beta (`ICLUST`), and (McDonald's) `omega` (both omega hierarchical and omega total). Additional reliability estimates may be found in the `guttman` function.

This can also be examined by applying `irt.fa` Item Response Theory techniques using factor analysis of the `tetrachoric` or `polychoric` correlation matrices and converting the results into the standard two parameter parameterization of item difficulty and item discrimination. Information functions for the items suggest where they are most effective.

5) For some applications, data matrices are synthetically combined from sampling different items for different people. So called Synthetic Aperture Personality Assessement (SAPA) techniques allow the formation of large correlation or covariance matrices even though no one person has taken all of the items. To analyze such data sets, it is easy to form item composites based upon the covariance matrix of the items, rather than original data set. These matrices may then be analyzed using a number of functions (e.g., `cluster.cor`, `fa`, `ICLUST`, `principal`, `mat.regress`, and `factor2cluster`.

6) More typically, one has a raw data set to analyze. `alpha` will report several reliablity estimates as well as item-whole correlations for items forming a single scale, `score.items` will score data sets on multiple scales, reporting the scale scores, item-scale and scale-scale correlations, as well as coefficient alpha, alpha-1 and G6+. Using a ‘keys’ matrix (created by `make.keys` or by hand), scales can have overlapping or independent items. `score.multiple.choice` scores multiple choice items or converts multiple choice items to dichtomous (0/1) format for other functions.

7) In addition to classical test theory (CTT) based scores of either totals or averages, 1 and 2 parameter IRT based scores may be found with `scoreIrt.1pl`, `scoreIrt.2pl` or more generally `scoreIrt`. Although highly correlated with CTT estimates, these scores take advantage of different item difficulties and are particularly appropriate for the problem of missing data.

8) If the data has a multilevel structure (e.g, items nested within time nested within subjects) the `multilevel.reliability` aka `mlr` function will estimate generalizability coefficients for data over subjects, subjects over time, etc. `mlPlot` will provide plots for each subject of items over time. `mlArrange` takes the conventional wide output format and converts it to the long format necessary for some multilevel functions. Other functions useful for multilevel data include `statsBy` and `faBy`.

An additional set of functions generate simulated data to meet certain structural properties. `sim.anova` produces data simulating a 3 way analysis of variance (ANOVA) or linear model with or with out repeated measures. `sim.item` creates simple structure data, `sim.circ` will produce circumplex structured data, `sim.dichot` produces circumplex or simple structured data for dichotomous items. These item structures are useful for understanding the effects of skew, differential item endorsement on factor and cluster analytic soutions. `sim.structural` will produce correlation matrices and data matrices to match general structural models. (See the vignette).

When examining personality items, some people like to discuss them as representing items in a two dimensional space with a circumplex structure. Tests of circumplex fit `circ.tests` have been developed. When representing items in a circumplex, it is convenient to view them in `polar` coordinates.

Additional functions for testing the difference between two independent or dependent correlation `r.test`, to find the `phi` or `Yule` coefficients from a two by table, or to find the confidence interval of a correlation coefficient.

Many data sets are included: `bfi` represents 25 personality items thought to represent five factors of personality, `ability` has 14 multiple choice iq items. `sat.act` has data on self reported test scores by age and gender. `galton` Galton's data set of the heights of parents and their children. `peas` recreates the original Galton data set of the genetics of sweet peas. `heights` and `cubits` provide even more Galton data, `vegetables` provides the Guilford preference matrix of vegetables. `cities` provides airline miles between 11 US cities (demo data for multidimensional scaling).

Package: | psych
Type: | Package
Version: | 1.7.2
Date: | 2017--February--28
License: | GPL version 2 or newer

Index:

psych A package for personality, psychometric, and psychological research.

Useful data entry and descriptive statistics

read.file | search for, find, and read from file
read.clipboard | shortcut for reading from the clipboard
read.clipboard.csv | shortcut for reading comma delimited files from clipboard
read.clipboard.lower | shortcut for reading lower triangular matrices from the clipboard
read.clipboard.upper | shortcut for reading upper triangular matrices from the clipboard
describe | Basic descriptive statistics useful for psychometrics
describe.by | Find summary statistics by groups
statsBy | Find summary statistics by a grouping variable, including within and between correlation matrices.
mlArrange | Change multilevel data from wide to long format
headtail | combines the head and tail functions for showing data sets
pairs.panels | SPLOM and correlations for a data matrix
corr.test | Correlations, sample sizes, and p values for a data matrix
cor.plot | graphically show the size of correlations in a correlation matrix
multi.hist | Histograms and densities of multiple variables arranged in matrix form
skew | Calculate skew for a vector, each column of a matrix, or data.frame
kurtosi | Calculate kurtosis for a vector, each column of a matrix or dataframe
geometric.mean | Find the geometric mean of a vector or columns of a data.frame
harmonic.mean | Find the harmonic mean of a vector or columns of a data.frame
error.bars | Plot means and error bars
error.bars.by | Plot means and error bars for separate groups
error.crosses | Two way error bars
interp.median | Find the interpolated median, quartiles, or general quantiles.
rescale | Rescale data to specified mean and standard deviation
table2df | Convert a two dimensional table of counts to a matrix or data frame

Data reduction through cluster and factor analysis

fa | Combined function for principal axis, minimum residual, weighted least squares,
| and maximum likelihood factor analysis
factor.pa | Do a principal Axis factor analysis (deprecated)
factor.minres | Do a minimum residual factor analysis (deprecated)
factor.wls | Do a weighted least squares factor analysis (deprecated)
fa.graph | Show the results of a factor analysis or principal components analysis graphically
fa.diagram | Show the results of a factor analysis without using Rgraphviz
fa.sort | Sort a factor or principal components output
fa.extension | Apply the Dwyer extension for factor loadingss
principal | Do an eigen value decomposition to find the principal components of a matrix
fa.parallel | Scree test and Parallel analysis
fa.parallel.poly | Scree test and Parallel analysis for polychoric matrices
factor.scores | Estimate factor scores given a data matrix and factor loadings
guttman | 8 different measures of reliability (6 from Guttman (1945)
`irt.fa` | Apply factor analysis to dichotomous items to get IRT parameters
`iclust` | Apply the ICLUST algorithm
ICLUST.graph | Graph the output from ICLUST using the dot language
ICLUST.rgraph | Graph the output from ICLUST using rgraphviz
kaiser | Apply kaiser normalization before rotating
polychoric | Find the polychoric correlations for items and find item thresholds
poly.mat | Find the polychoric correlations for items (uses J. Fox's hetcor)
omega | Calculate the omega estimate of factor saturation (requires the GPArotation package)
omega.graph | Draw a hierarchical or Schmid Leiman orthogonalized solution (uses Rgraphviz)
partial.r | Partial variables from a correlation matrix
predict | Predict factor/component scores for new data
schmid | Apply the Schmid Leiman transformation to a correlation matrix
score.items | Combine items into multiple scales and find alpha
score.multiple.choice | Combine items into multiple scales and find alpha and basic scale statistics
set.cor | Find Cohen's set correlation between two sets of variables
smc | Find the Squared Multiple Correlation (used for initial communality estimates)
tetrachoric | Find tetrachoric correlations and item thresholds
polyserial | Find polyserial and biserial correlations for item validity studies
mixed.cor | Form a correlation matrix from continuous, polytomous, and dichotomous items
VSS | Apply the Very Simple Structure criterion to determine the appropriate number of factors.
VSS.parallel | Do a parallel analysis to determine the number of factors for a random matrix
VSS.plot | Plot VSS output
VSS.scree | Show the scree plot of the factor/principal components
MAP | Apply the Velicer Minimum Absolute Partial criterion for number of factors

Functions for reliability analysis (some are listed above as well).

alpha | Find coefficient alpha and Guttman Lambda 6 for a scale (see also score.items)
guttman | 8 different measures of reliability (6 from Guttman (1945)
omega | Calculate the omega estimates of reliability (requires the GPArotation package)
omegaSem | Calculate the omega estimates of reliability using a Confirmatory model (requires the sem package)
ICC | Intraclass correlation coefficients
score.items | Combine items into multiple scales and find alpha
glb.algebraic | The greates lower bound found by an algebraic solution (requires Rcsdp). Written by Andreas Moeltner

Procedures particularly useful for Synthetic Aperture Personality Assessment

alpha | Find coefficient alpha and Guttman Lambda 6 for a scale (see also score.items)
make.keys | Create the keys file for score.items or cluster.cor
correct.cor | Correct a correlation matrix for unreliability
count.pairwise | Count the number of complete cases when doing pair wise correlations
cluster.cor | find correlations of composite variables from larger matrix
cluster.loadings | find correlations of items with composite variables from a larger matrix
eigen.loadings | Find the loadings when doing an eigen value decomposition
fa | Do a minimal residual or principal axis factor analysis and estimate factor scores
fa.extension | Extend a factor analysis to a set of new variables
factor.pa | Do a Principal Axis factor analysis and estimate factor scores
factor2cluster | extract cluster definitions from factor loadings
factor.congruence | Factor congruence coefficient
factor.fit | How well does a factor model fit a correlation matrix
factor.model | Reproduce a correlation matrix based upon the factor model
factor.residuals | Fit = data - model
factor.rotate | ``hand rotate" factors
guttman | 8 different measures of reliability
mat.regress | standardized multiple regression from raw or correlation matrix input
polyserial | polyserial and biserial correlations with massive missing data
tetrachoric | Find tetrachoric correlations and item thresholds

Functions for generating simulated data sets

sim | The basic simulation functions
sim.anova | Generate 3 independent variables and 1 or more dependent variables for demonstrating ANOVA
| and lm designs
sim.circ | Generate a two dimensional circumplex item structure
sim.item | Generate a two dimensional simple structure with particular item characteristics
sim.congeneric | Generate a one factor congeneric reliability structure
sim.minor | Simulate nfact major and nvar/2 minor factors
sim.structural | Generate a multifactorial structural model
sim.irt | Generate data for a 1, 2, 3 or 4 parameter logistic model
sim.VSS | Generate simulated data for the factor model
phi.demo | Create artificial data matrices for teaching purposes
sim.hierarchical | Generate simulated correlation matrices with hierarchical or any structure
sim.spherical | Generate three dimensional spherical data (generalization of circumplex to 3 space)

Graphical functions (require Rgraphviz) – deprecated

structure.graph | Draw a sem or regression graph
fa.graph | Draw the factor structure from a factor or principal components analysis
omega.graph | Draw the factor structure from an omega analysis(either with or without the Schmid Leiman transformation)
ICLUST.graph | Draw the tree diagram from ICLUST

Graphical functions that do not require Rgraphviz

diagram | A general set of diagram functions.
structure.diagram | Draw a sem or regression graph
fa.diagram | Draw the factor structure from a factor or principal components analysis
omega.diagram | Draw the factor structure from an omega analysis(either with or without the Schmid Leiman transformation)
ICLUST.diagram | Draw the tree diagram from ICLUST
plot.psych | A call to plot various types of output (e.g. from irt.fa, fa, omega, iclust
cor.plot | A heat map display of correlations
spider | Spider and radar plots (circular displays of correlations)

Circular statistics (for circadian data analysis)

circadian.cor | Find the correlation with e.g., mood and time of day
circadian.linear.cor | Correlate a circular value with a linear value
circadian.mean | Find the circular mean of each column of a a data set
cosinor | Find the best fitting phase angle for a circular data set

Miscellaneous functions

comorbidity | Convert base rate and comorbity to phi, Yule and tetrachoric
df2latex | Convert a data.frame or matrix to a LaTeX table
dummy.code | Convert categorical data to dummy codes
fisherz | Apply the Fisher r to z transform
fisherz2r | Apply the Fisher z to r transform
ICC | Intraclass correlation coefficients
cortest.mat | Test for equality of two matrices (see also cortest.normal, cortest.jennrich )
cortest.bartlett | Test whether a matrix is an identity matrix
paired.r | Test for the difference of two paired or two independent correlations
r.con | Confidence intervals for correlation coefficients
r.test | Test of significance of r, differences between rs.
p.rep | The probability of replication given a p, r, t, or F
phi | Find the phi coefficient of correlation from a 2 x 2 table
phi.demo | Demonstrate the problem of phi coefficients with varying cut points
phi2poly | Given a phi coefficient, what is the polychoric correlation
phi2poly.matrix | Given a phi coefficient, what is the polychoric correlation (works on matrices)
polar | Convert 2 dimensional factor loadings to polar coordinates.
scaling.fits | Compares alternative scaling solutions and gives goodness of fits
scrub | Basic data cleaning
tetrachor | Finds tetrachoric correlations
thurstone | Thurstone Case V scaling
tr | Find the trace of a square matrix
wkappa | weighted and unweighted versions of Cohen's kappa
Yule | Find the Yule Q coefficient of correlation
Yule.inv | What is the two by two table that produces a Yule Q with set marginals?
Yule2phi | What is the phi coefficient corresponding to a Yule Q with set marginals?
Yule2tetra | Convert one or a matrix of Yule coefficients to tetrachoric coefficients.

Functions that are under development and not recommended for casual use

irt.item.diff.rasch | IRT estimate of item difficulty with assumption that theta = 0
irt.person.rasch | Item Response Theory estimates of theta (ability) using a Rasch like model

Data sets included in the psych package

bfi | represents 25 personality items thought to represent five factors of personality
Thurstone | 8 different data sets with a bifactor structure
cities | The airline distances between 11 cities (used to demonstrate MDS)
epi.bfi | 13 personality scales
iqitems | 14 multiple choice iq items
msq | 75 mood items
sat.act | Self reported ACT and SAT Verbal and Quantitative scores by age and gender
Tucker | Correlation matrix from Tucker
galton | Galton's data set of the heights of parents and their children
heights | Galton's data set of the relationship between height and forearm (cubit) length
cubits | Galton's data table of height and forearm length
peas | Galton`s data set of the diameters of 700 parent and offspring sweet peas
vegetables | Guilford`s preference matrix of vegetables (used for thurstone)

A debugging function that may also be used as a demonstration of psych.

test.psych | Run a test of the major functions on 5 different data sets. Primarily for development purposes.
| Although the output can be used as a demo of the various functions.

### Note

Development versions (source code) of this package are maintained at the repository http://personality-project.org/r along with further documentation. Specify that you are downloading a source package.
Some functions require other packages. Specifically, omega and schmid require the GPArotation package, ICLUST.rgraph and fa.graph require Rgraphviz but have alternatives using the diagram functions. i.e.:

function | requires
omega | GPArotation
schmid | GPArotation
poly.mat | polychor
phi2poly | polychor
polychor.matrix | polychor
ICLUST.rgraph | Rgraphviz
fa.graph | Rgraphviz
structure.graph | Rgraphviz
glb.algebraic | Rcsdp

### Author(s)

William Revelle
Department of Psychology
Northwestern University
Evanston, Illiniois
http://personality-project.org/revelle.html

Maintainer: William Revelle <revelle@northwestern.edu>

### References

A general guide to personality theory and research may be found at the personality-project http://personality-project.org. See also the short guide to R at http://personality-project.org/r. In addition, see

Revelle, W. (in preparation) An Introduction to Psychometric Theory with applications in R. Springer. at http://personality-project.org/r/book/

### Examples

    #See the separate man pages
    #to test most of the psych package run the following
    #test.psych()

[Package psych version 1.7.8 Index]