Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010)
Example data set from Gorsuch (1997) for an example factor extension.
Intraclass Correlations (ICC1, ICC2, ICC3 from Shrout and Fleiss)
iclust: Item Cluster Analysis -- Hierarchical cluster analysis using psychometric principles
Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.
Decision Theory measures of specificity, sensitivity, and d prime
Function to form hierarchical cluster analysis of items
A package for personality, psychometric, and psychological research
create control code for ICLUST graphical output
12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation
Perform Procustes,bifactor, promax or targeted rotations and return the inter factor angles.
Sort items by absolute size of cluster loadings
Draw an ICLUST graph using the Rgraphviz package
9 Cognitive variables discussed by Tucker and Lewis (1973)
Find the Standard deviation for a vector, matrix, or data.frame - do not return error if there are no cases
Find two estimates of reliability: Cronbach's alpha and Guttman's Lambda 6.
Compute the Moore-Penrose Pseudo Inverse of a matrix
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.
Find the Kaiser, Meyer, Olkin Measure of Sampling Adequacy
25 Personality items representing 5 factors
A bootstrap aggregation function for choosing most predictive unit weighted items
Draw pairs of bargraphs based on two groups
Seven data sets showing a bifactor solution.
Bock and Liberman (1970) data set of 1000 observations of the LSAT
12 cognitive variables from Cattell (1963)
Plot VSS fits
Plot the successive eigen values for a scree test
cluster Fit: fit of the cluster model to a correlation matrix
Find item by cluster correlations, corrected for overlap and reliability
Find correlations of composite variables (corrected for overlap) from a larger matrix.
Apply the Very Simple Structure, MAP, and other criteria to determine the appropriate number of factors.
Apply four tests of circumplex versus simple structure
Convert correlations to distances (necessary to do multidimensional scaling of correlation data)
Compare real and random VSS solutions
Find a Full Information Maximum Likelihood (FIML) correlation or covariance matrix from a data matrix with missing data
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.
Create a block randomized structure for n independent variables
Plot factor/cluster loadings and assign items to clusters by their highest loading.
Model comparison for regression, mediation, and factor analysis
Find Cohen d and confidence intervals
Draw biplots of factor or component scores by factor or component loadings
Create an image plot for a correlation or factor matrix
Bootstrapped and normal confidence intervals for raw and composite correlations
The Bass-Ackward factoring algorithm discussed by Goldberg
Find dis-attenuated correlations given correlations and reliabilities
Find the correlations, sample sizes, and probability values between elements of a matrix or data.frame.
Bartlett's test that a correlation matrix is an identity matrix
The sample size weighted correlation may be used in correlating aggregated data
Smooth a non-positive definite correlation matrix to make it positive definite
Basic summary statistics by group
Helper functions for drawing path model diagrams
Functions for analysis of circadian or diurnal data
Deprecated Exploratory Factor analysis functions. Please use fa
Count number of pairwise cases for a data set with missing (NA) data and impute values.
Basic descriptive statistics useful for psychometrics
Create a 'violin plot' or density plot of the distribution of a set of variables
8 cognitive variables used by Dwyer for an example.
Simulate the C(ues) T(endency) A(ction) model of motivation
Convert eigen vectors and eigen values to the more normal (for psychologists) component loadings
Plot data and 1 and 2 sigma correlation ellipses
Chi square tests of whether a single matrix is an identity matrix, or a pair of matrices are equal.
Plot means and confidence intervals for multiple groups
Two way plots of means, error bars, and sample sizes
Plot x and y error bars
Show a dot.chart with error bars for different groups or variables
Create dummy coded variables
Draw a correlation ellipse and two normal curves to demonstrate tetrachoric correlation
Plot means and confidence intervals
Apply Dwyer's factor extension to find factor loadings for extended variables
Scree plots of data or correlation matrix compared to random ``parallel" matrices
Sort factor analysis or principal components analysis loadings
A set of functions for factorial and empirical scale construction
Correlations between two factor analysis solutions
Multi level (hierarchical) factor analysis
Perform and Exploratory Structural Equation Model (ESEM) by using factor extension techniques
Graph factor loading matrices
``Hand" rotate a factor loading matrix
A first approximation to Random Effects Exploratory Factor Analysis
Find various goodness of fit statistics for factor analysis and principal components
Extract cluster definitions from factor loadings
Find R = F F' + U2 is the basic factor model
Various ways to estimate factor scores for the factor analysis model
R* = R- F F'
How well does the factor model fit a correlation matrix. Part of the VSS package
Find the geometric mean of a vector or columns of a data.frame.
Find the greatest lower bound to reliability.
Coefficient of factor congruence
Item Response Theory estimate of theta (ability) using a Rasch (like) model
Simple function to estimate item difficulties using IRT concepts
Draw an ICLUST hierarchical cluster structure diagram
Combine calls to head and tail
Exploratory Factor analysis using MinRes (minimum residual) as well as EFA by Principal Axis, Weighted Least Squares or Maximum Likelihood
Parse and exten formula input from a model and return the DV, IV, and associated terms.
Combine two square matrices to have a lower off diagonal for one, upper off diagonal for the other
Find the harmonic mean of a vector, matrix, or columns of a data.frame
Create a keys matrix for use by score.items or cluster.cor
Transformations of r, d, and t including Fisher r to z and z to r and confidence intervals
Find the interpolated sample median, quartiles, or specific quantiles for a vector, matrix, or data frame
Alternative estimates of test reliabiity
Item Response Analysis by Exploratory Factor Analysis of tetrachoric/polychoric correlations
Plot probability of multiple choice responses as a function of a latent trait
A function to add two vectors or matrices
"Manhattan" plots of correlations with a set of criteria.
Estimate and display direct and indirect effects of mediators and moderator in path models
Apply the Kaiser normalization when rotating factors
Sort the elements of a correlation matrix to reflect factor loadings
Calculate McDonald's omega estimates of general and total factor saturation
Find and plot various reliability/gneralizability coefficients for multilevel data
Find the probability of replication for an F, t, or r and estimate effect size
Test the difference between (un)paired correlations
Find and graph Mahalanobis squared distances to detect outliers
Graph hierarchical factor structures
A simple demonstration of the Pearson, phi, and polychoric corelation
Miscellaneous helper functions for the psych package
SPLOM, histograms and correlations for a data matrix
Convert Cartesian factor loadings into polar coordinates
Find correlations for mixtures of continuous, polytomous, and dichotomous variables
Plotting functions for the psych package of class ``psych"
Phi or Yule coefficient matrix to polychoric coefficient matrix
Prediction function for factor analysis, principal components (pca), bestScales
Logistic transform from x to p and logit transform from p to x
Find von Neuman's Mean Square of Successive Differences
Convert a phi coefficient to a tetrachoric correlation
Find the partial correlations for a set (x) of variables with set (y) removed.
Find Cohen's kappa and weighted kappa coefficients for correlation of two raters
Multiple histograms with density and normal fits on one page
Principal components analysis (PCA)
Print and summary functions for the psych class
Find miniscales (parcels) of size 2 or 3 from a set of items
Find the phi coefficient of correlation between two dichotomous variables
Extract residuals from various psych objects
Correct correlations for restriction of range. (Thorndike Case 2)
Tests of significance for correlations
Reports 7 different estimates of scale reliabity including alpha, omega, split half
Test the adequacy of simple choice, logistic, or Thurstonian scaling.
3 Measures of ability: SATV, SATQ, ACT
Reverse the coding of selected items prior to scale analysis
Function to convert scores to ``conventional
" metrics
Functions to simulate psychological/psychometric data.
Multiple Regression and Set Correlation from matrix or raw input
Score scales and find Cronbach's alpha as well as associated statistics
Find Item Response Theory (IRT) based scores for dichotomous or polytomous items
A utility for basic data cleaning and recoding. Changes values outside of minimum and maximum limits to NA.
Score items using regression or correlation based weights
Apply the Schmid Leiman transformation to a correlation matrix
Draw a scatter plot with associated X and Y histograms, densities and correlation
Simulate a congeneric data set
Functions to simulate psychological/psychometric data.
Score multiple choice items and provide basic test statistics
Simulations of circumplex and simple structure
Create correlation matrices or data matrices with a particular measurement and structural model
Score item composite scales and find Cronbach's alpha, Guttman lambda 6 and item whole correlations
Simulate multilevel data with specified within group and between group correlations
Further functions to simulate psychological/psychometric data.
Create a population or sample correlation matrix, perhaps with hierarchical structure.
Generate simulated data structures for circumplex, spherical, or simple structure
Convert a table with counts to a matrix or data.frame representing those counts.
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
Create factor model matrices from an input list
Simulate a 3 way balanced ANOVA or linear model, with or without repeated measures.
Calculate univariate or multivariate (Mardia's test) skew and kurtosis for a vector, matrix, or data.frame
create VSS like data
Data set testing causal direction in presumed media influence
Make "radar" or "spider" plots.
Draw a structural equation model specified by two measurement models and a structural model
Find statistics (including correlations) within and between groups for basic multilevel analyses
Find various test-retest statistics, including test, person and item reliability
Thurstone Case V scaling
Find the trace of a square matrix
Tetrachoric, polychoric, biserial and polyserial correlations from various types of input
Testing of functions in the psych package
A simple demonstration (and test) of various IRT scoring algorthims.
Form a super matrix from two sub matrices.
Find the Winsorized scores, means, sds or variances for a vector, matrix, or data.frame
Several indices of the unidimensionality of a set of variables.
An example of the distinction between within group and between group correlations