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ltm (version 1.2-0)

Latent Trait Models under IRT

Description

Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.

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Install

install.packages('ltm')

Monthly Downloads

10,183

Version

1.2-0

License

GPL (>= 2)

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Repository

https://github.com/drizopoulos/ltm

Maintainer

Dimitris Rizopoulos

Last Published

February 18th, 2022

Functions in ltm (1.2-0)

Goodness of Fit for Rasch Models

Factor Scores - Ability Estimates

Descriptive Statistics

Graded Response Model - Polytomous IRT

Fitted Values for IRT model

Latent Trait Model - Latent Variable Model for Binary Data

Fit of the model on the margins

The Law School Admission Test (LSAT), Section VI

Residuals for IRT models

Generalized Partial Credit Model - Polytomous IRT

Pairwise Associations between Items using a Correlation Coefficient

Attitude to the Environment

Attitude Towards Abortion

Area under the Test or Item Information Curves

Gauss-Hermite Quadrature Points

vcov method for fitted IRT models

Unidimensionality Check using Modified Parallel Analysis

Factor Scores - Ability Estimates Plot method

Workplace Industrial Relation Survey Data

Generate Random Responses Patterns under Dichotomous and Polytomous IRT models

Person-Fit Statistics and P-values

Multiple Choice Items to Binary Responses

Anova method for fitted IRT models

Item-Fit Statistics and P-values

Summary method for fitted IRT models

Plot method for fitted IRT models

Latent Trait Models for Item Response Theory Analyses

Descriptive Statistics Plot method

testEquatingData

Prepares Data for Test Equating

Birnbaum's Three Parameter Model

Extract Estimated Loadings

Point-Biserial Correlation

Women's Mobility

Attitude to Science and Technology

Cronbach's alpha