ltm: An R Package for Latent Variable Modeling and Item Response Analysis

By Dimitris Rizopoulos. 2006

1,064 citations recorded by the source corpus.

The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum's Three-Parameter models have been implemented, whereas for polytomous data Semejima's Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f00cdbd27e0898a4c78077ad6c7be52c208c3b54

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In Computer Networks and Communications

In Management Science and Operations Research

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Citations in the corpus

Measuring the formal independence of regulatory agencies (2012, methodology): “Similar discrimination parameters were obtained using marginal maximum likelihood as implemented in the ltm package (Rizopoulos 2006), and through normal factor analysis imputing missing values with the item mean, and taking the first unrotated factor.”
Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules (2017, positive): “Let β∗ denote the true parameter value; we use the EM algorithm to construct an estimator, β̂n, of β ∗ (Rizopoulos, 2006).”
Why does the Cognitive Reflection Test (sometimes) predict utilitarian moral judgment (and other things) (2015, methodology): “We also used the ltm package (Rizopoulos, 2006) to fit a Rasch model to the data, as we explain later.”
Developing an Instrument for Teacher Feedback : Using the Rasch Model to Explore Teachers' Development of Effective Teaching Strategies and Behaviors (2018, methodology): “The analysis was performed in R using the package ltm (Rizopoulos, 2006).”
A Didactic Presentation of Snijders’s lz* Index of Person Fit With Emphasis on Response Model Selection and Ability Estimation (2012, methodology): “The R package ltm (Rizopoulos, 2006) was considered for such an item calibration.”
An IRT analysis of motive questionnaires: The Unified Motive Scales (2012, methodology): “The ltm package was used for model estimation (Rizopoulos, 2006).”
Variational Approximations for Generalized Linear Latent Variable Models (2017, positive): “…(Huber et al., 2004), where we wrote our own code to compute the estimates (see supplementary material), 4) the ltm function in the R package ltm (Rizopoulos, 2006), which uses a hybrid algorithm combining EM and quasi-Newton optimization, with the integration performed using Gauss-Hermite…”
Working Memory and Dynamic Measures of Analogical Reasoning as Predictors of Children's Math and Reading Achievement (2014, methodology): “The gPCM estimates were computed for the aggregated dataset (N= 379) using the ltm package for R (Rizopoulos, 2006).”
A Principled Way of Assessing Visualization Literacy (2014, methodology): “Finding the right model variant Different IRT models, implemented in the ltm R package [40], were then fitted to LG1s.”
The Basics of Item Response Theory Using R (2018, methodology): “Rather than using any of the existing R packages developed specifically for IRT—e.g., mirt (Chalmers, 2012), ltm (Rizopoulos, 2006)—the tutorials in this book require only those functions that are available in base R.”
What makes mathematical word problem solving challenging? Exploring the roles of word problem characteristics, text comprehension, and arithmetic skills (2019, methodology): “In this study, IRT analyses were conducted using the R 3.2.3 with ltm (latent trait models) package, 1 3 which was developed to analyze multivariate dichotomous data using latent variable models (Rizopoulos 2006).”