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Description

Congeneric Normal-Ogive Model.

The congeneric normal-ogive model is a popular model for psychometric data (McDonald, R. P. (1997) <doi:10.1007/978-1-4757-2691-6_15>). This model estimates the model, calculates theoretical and concrete reliability coefficients, and predicts the latent variable of the model. This is the companion package to Moss (2020) <doi:10.31234/osf.io/nvg5d>.

conogive

BuildStatus AppVeyor BuildStatus

Codecov testcoverage DOI Project Status: Active – The project has reached a stable, usablestate and is being activelydeveloped.

An R package for the congeneric normal-ogive model.

Overview

The congeneric normal-ogive model (McDonald, R. P., 1997) is a psychometric model for Likert data with one latent factor. This package has functions to estimate such models, calculate their ordinal reliabilities, and make predictions. It implements the concrete ordinal reliabilities of (Moss, 2020).

Installation

From inside R, use one of the following commands:

devtools::install_github("JonasMoss/conogive")

Usage

Estimate a congeneric normal-ogive model with congive, predict the value of the latent factor with predict, and calculate the reliability with ordinal_r.

library("conogive")
extraversion = psychTools::bfi[c("E1", "E2", "E3", "E4", "E5")]
extraversion[, "E1"] = 7 - extraversion[, "E1"] # Reverse-coded item.
extraversion[, "E2"] = 7 - extraversion[, "E2"] # Reverse-coded item.

object = conogive(extraversion)

ordinal_r(object) # Observed reliability
#> [1] 0.7046056
theoretical_ordinal_r(object) # Theoretical reliability
#> [1] 0.8122607

hist(predict(object, extraversion)) # Plot distribution of predictions.

References

McDonald, R. P. (1997). Normal-ogive multidimensional model. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of Modern Item Response Theory (pp. 257–269). Springer. https://doi.org/10.1007/978-1-4757-2691-6_15

Moss, J. (2020). Please avoid the standardized alpha and the ordinal alpha. https://psyarxiv.com/nvg5d/

Metadata

Version

1.0.0

License

Unknown

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