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