Psychometric R Package

[Victorino Eagle]

Ageneral 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 web page.

N2 - A general purpose toolbox 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. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, 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.

AB - A general purpose toolbox 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. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, 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.

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Welcome to the psychonetrics website! This website is home to the psychonetrics R package for psychometric network modeling, the psychonetrics lab group at the University of Amsterdam, and a blog on open science, psychometrics, network modeling, and related topics.

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The family plays an essential role in the life of an adolescent. Hence, an acceptable understanding and an evaluation of family functioning is fundamental for effective interventions with adolescents in the psychological, social, and educational fields. The main purpose of this study is to examine the psychometric properties of the Family Adaptability and Cohesion Evaluation Scale (FACES IV), the Family Communication Scale (FCS), and the Family Satisfaction Scale (FSS), for assessing the family functioning of Spanish adolescents. The sample was comprised of 1187 adolescents between 14 -18 years old (49.96% boys and 50.04% girls; M = 16.17; SD = 1.31) from Castile and Leon (Spain), selected from 23 educational centers, 10 university degree courses, and 18 specific juvenile centers for adolescents with either family or behavioral problems. The scales of Balanced Cohesion, Balanced Flexibility and Disengaged showed good convergent validity, while Enmeshed, Rigid, and Chaotic did not. For this reason some items were removed, obtaining a shortened version of FACES IV, that demonstrated acceptable reliability, and good convergent and predictive validity. The FCS and FSS scales yielded excellent psychometric properties. The results confirmed the factorial structure of the FACES IV, its transcultural applicability, and its validity for different ages. The hypotheses of the circumplex model were confirmed, except for the dysfunctionality of two scales, Enmeshed and Rigid, that contrary to what was expected, showed positive correlations with Family Communication, Family Satisfaction, Balanced Cohesion, and Balanced Flexibility. In brief, our results present the FACES IV package as a useful instrument for the assessment of family functioning of Spanish adolescents. Future studies will be necessary to confirm the trend observed for the two aforementioned scales among adolescents.

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 intro vignette tells how to install psych and overview vignette provides examples of using psych in many applications. In addition, there are a growing set of tutorials available on the -project.org/r/ webpages.

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 (e.g., oblimin, quartimin, varimax, geomin, ...). Functions for determining the number of factors in a data matrix include Very Simple Structure (VSS) and Minimum Average Partial correlation (MAP).

There are a number of functions for finding various reliability coefficients (see Revelle and Condon, 2019). These include the traditional alpha (found for multiple scales and with more useful output by scoreItems, score.multiple.choice), beta (ICLUST) and both of McDonald's omega coefficients (omega, omegaSem and omega.diagram) 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. scoreOverlap correct interscale correlations for overlapping items, so that it is possible to examine hierarchical or nested structures.

Scales can be formed that best predict (after cross validation) particular criteria using bestScales using unit weighted or correlation weights. This procedure, also called the BISCUIT algorithm (Best Items Scales that are Cross validated, Unit weighted, Informative, and Transparent) is a simple alternative to more complicated supervised machine learning algorithms.

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.org/) 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 ((lmCor) and partial ((partial.r) correlations from correlation matrices.

lmCor and mediate meet the desire to do regressions and mediation analysis from either raw data or from correlation matrices. If raw data are provided, these functions can also do moderation analyses.

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).

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