Example data set from Gorsuch (1997) for an example factor extension.
Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010)
Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.
Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
create control code for ICLUST graphical output
Intraclass Correlations (ICC1, ICC2, ICC3 from Shrout and Fleiss)
iclust: Item Cluster Analysis -- Hierarchical cluster analysis using psychometric principles
A package for personality, psychometric, and psychological research
Function to form hierarchical cluster analysis of items
Draw an ICLUST graph using the Rgraphviz package
Find the Standard deviation for a vector, matrix, or data.frame - do not return error if there are no cases
Perform Procustes,bifactor, promax or targeted rotations and return the inter factor angles.
12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation
Decision Theory measures of specificity, sensitivity, and d prime
9 Cognitive variables discussed by Tucker and Lewis (1973)
Compute the Moore-Penrose Pseudo Inverse of a matrix
Find the Kaiser, Meyer, Olkin Measure of Sampling Adequacy
Draw pairs of bargraphs based on two groups
Apply the Very Simple Structure, MAP, and other criteria to determine the appropriate number of factors.
Plot the successive eigen values for a scree test
Compare real and random VSS solutions
Plot VSS fits
From a two by two table, find the Yule coefficients of association, convert to phi, or tetrachoric, recreate table the table to create the Yule coefficient.
25 Personality items representing 5 factors
Sort items by absolute size of cluster loadings
A bootstrap aggregation function for choosing most predictive unit weighted items
Create a block randomized structure for n independent variables
Model comparison for regression, mediation, and factor analysis
Draw biplots of factor or component scores by factor or component loadings
The Bass-Ackward factoring algorithm discussed by Goldberg
Bock and Liberman (1970) data set of 1000 observations of the LSAT
12 cognitive variables from Cattell (1963)
Seven data sets showing a bifactor solution.
cluster Fit: fit of the cluster model to a correlation matrix
Find two estimates of reliability: Cronbach's alpha and Guttman's Lambda 6.
Find item by cluster correlations, corrected for overlap and reliability
Convert correlations to distances (necessary to do multidimensional scaling of correlation data)
Bootstrapped and normal confidence intervals for raw and composite correlations
Create an image plot for a correlation or factor matrix
Find a Full Information Maximum Likelihood (FIML) correlation or covariance matrix from a data matrix with missing data
Apply four tests of circumplex versus simple structure
Convert base rates of two diagnoses and their comorbidity into phi, Yule, and tetrachorics
Convert a cluster vector (from e.g., kmeans) to a keys matrix suitable for scoring item clusters.
Find Cohen d and confidence intervals
Find correlations of composite variables (corrected for overlap) from a larger matrix.
Plot factor/cluster loadings and assign items to clusters by their highest loading.
Find the correlations, sample sizes, and probability values between elements of a matrix or data.frame.
Find dis-attenuated correlations given correlations and reliabilities
The sample size weighted correlation may be used in correlating aggregated data
Smooth a non-positive definite correlation matrix to make it positive definite
Simulate the C(ues) T(endency) A(ction) model of motivation
Create a 'violin plot' or density plot of the distribution of a set of variables
Deprecated Exploratory Factor analysis functions. Please use fa
Basic descriptive statistics useful for psychometrics
Helper functions for drawing path model diagrams
Basic summary statistics by group
Bartlett's test that a correlation matrix is an identity matrix
Chi square tests of whether a single matrix is an identity matrix, or a pair of matrices are equal.
Plot data and 1 and 2 sigma correlation ellipses
Plot means and confidence intervals
Functions for analysis of circadian or diurnal data
Count number of pairwise cases for a data set with missing (NA) data and impute values.
Create dummy coded variables
Draw a correlation ellipse and two normal curves to demonstrate tetrachoric correlation
8 cognitive variables used by Dwyer for an example.
Convert eigen vectors and eigen values to the more normal (for psychologists) component loadings
Show a dot.chart with error bars for different groups or variables
Plot x and y error bars
Plot means and confidence intervals for multiple groups
Two way plots of means, error bars, and sample sizes
A first approximation to Random Effects Exploratory Factor Analysis
Correlations between two factor analysis solutions
Multi level (hierarchical) factor analysis
A set of functions for factorial and empirical scale construction
Parse and exten formula input from a model and return the DV, IV, and associated terms.
Scree plots of data or correlation matrix compared to random ``parallel" matrices
Apply Dwyer's factor extension to find factor loadings for extended variables
Graph factor loading matrices
Transformations of r, d, and t including Fisher r to z and z to r and confidence intervals
Alternative estimates of test reliabiity
Sort factor analysis or principal components analysis loadings
Find the harmonic mean of a vector, matrix, or columns of a data.frame
Perform and Exploratory Structural Equation Model (ESEM) by using factor extension techniques
Find various goodness of fit statistics for factor analysis and principal components
Extract cluster definitions from factor loadings
Find the geometric mean of a vector or columns of a data.frame.
Find the interpolated sample median, quartiles, or specific quantiles for a vector, matrix, or data frame
How well does the factor model fit a correlation matrix. Part of the VSS package
Coefficient of factor congruence
Exploratory Factor analysis using MinRes (minimum residual) as well as EFA by Principal Axis, Weighted Least Squares or Maximum Likelihood
Item Response Analysis by Exploratory Factor Analysis of tetrachoric/polychoric correlations
Plot probability of multiple choice responses as a function of a latent trait
"Manhattan" plots of correlations with a set of criteria.
Apply the Kaiser normalization when rotating factors
Sort the elements of a correlation matrix to reflect factor loadings
Logistic transform from x to p and logit transform from p to x
Simple function to estimate item difficulties using IRT concepts
Various ways to estimate factor scores for the factor analysis model
Convert Cartesian factor loadings into polar coordinates
Find R = F F' + U2 is the basic factor model
Find Cohen's kappa and weighted kappa coefficients for correlation of two raters
Find and plot various reliability/gneralizability coefficients for multilevel data
``Hand" rotate a factor loading matrix
Calculate McDonald's omega estimates of general and total factor saturation
Draw an ICLUST hierarchical cluster structure diagram
R* = R- F F'
Plotting functions for the psych package of class ``psych"
Combine calls to head and tail
Find the greatest lower bound to reliability.
Item Response Theory estimate of theta (ability) using a Rasch (like) model
A function to add two vectors or matrices
Reverse the coding of selected items prior to scale analysis
Estimate and display direct and indirect effects of mediators and moderator in path models
Tests of significance for correlations
Combine two square matrices to have a lower off diagonal for one, upper off diagonal for the other
3 Measures of ability: SATV, SATQ, ACT
Score scales and find Cronbach's alpha as well as associated statistics
Miscellaneous helper functions for the psych package
Apply the Schmid Leiman transformation to a correlation matrix
Find Item Response Theory (IRT) based scores for dichotomous or polytomous items
Find correlations for mixtures of continuous, polytomous, and dichotomous variables
Correct correlations for restriction of range. (Thorndike Case 2)
Graph hierarchical factor structures
Find the probability of replication for an F, t, or r and estimate effect size
Create a keys matrix for use by score.items or cluster.cor
Test the difference between (un)paired correlations
Phi or Yule coefficient matrix to polychoric coefficient matrix
Create a population or sample correlation matrix, perhaps with hierarchical structure.
Simulate a 3 way balanced ANOVA or linear model, with or without repeated measures.
Find and graph Mahalanobis squared distances to detect outliers
Prediction function for factor analysis, principal components (pca), bestScales
Find statistics (including correlations) within and between groups for basic multilevel analyses
Score item composite scales and find Cronbach's alpha, Guttman lambda 6 and item whole correlations
Simulate a congeneric data set
Generate simulated data structures for circumplex, spherical, or simple structure
Data set testing causal direction in presumed media influence
Find the partial correlations for a set (x) of variables with set (y) removed.
Make "radar" or "spider" plots.
Find von Neuman's Mean Square of Successive Differences
Find the phi coefficient of correlation between two dichotomous variables
Function to convert scores to ``conventional
" metrics
A simple demonstration (and test) of various IRT scoring algorthims.
Find the trace of a square matrix
Several indices of the unidimensionality of a set of variables.
A utility for basic data cleaning and recoding. Changes values outside of minimum and maximum limits to NA.
Test the adequacy of simple choice, logistic, or Thurstonian scaling.
Find miniscales (parcels) of size 2 or 3 from a set of items
Draw a scatter plot with associated X and Y histograms, densities and correlation
Multiple histograms with density and normal fits on one page
SPLOM, histograms and correlations for a data matrix
create VSS like data
Functions to simulate psychological/psychometric data.
Project Talent data set from Marion Spengler and Rodica Damian
Find the Squared Multiple Correlation (SMC) of each variable with the remaining variables in a matrix
Multiple Regression and Set Correlation from matrix or raw input
Extract residuals from various psych objects
Convert a table with counts to a matrix or data.frame representing those counts.
Testing of functions in the psych package
Tetrachoric, polychoric, biserial and polyserial correlations from various types of input
Simulations of circumplex and simple structure
Form a super matrix from two sub matrices.
Thurstone Case V scaling
Calculate univariate or multivariate (Mardia's test) skew and kurtosis for a vector, matrix, or data.frame
Find various test-retest statistics, including test, person and item reliability
Convert a phi coefficient to a tetrachoric correlation
Principal components analysis (PCA)
A simple demonstration of the Pearson, phi, and polychoric corelation
Score multiple choice items and provide basic test statistics
Print and summary functions for the psych class
Score items using regression or correlation based weights
Simulate multilevel data with specified within group and between group correlations
Find the Winsorized scores, means, sds or variances for a vector, matrix, or data.frame
Draw a structural equation model specified by two measurement models and a structural model
Create correlation matrices or data matrices with a particular measurement and structural model
Create factor model matrices from an input list
An example of the distinction between within group and between group correlations