A Comparison of Different Folding Models in Variations of the Map Folding Problem

In this paper, we compare the performance of three different folding models when they are applied to three different map folding settings. Precisely, the three folding models include the simple folding model, the simple folding–unfolding model, and the general folding model. The different map folding settings are discussed by comparing different folded states, i.e., different overlapping orders on the set of the squares of 1 × n maps, the squares of m × n maps, and the squares lying on the boundary of m × n maps. These folding models are abstracts of manual works and robotics. We clarify the relationship between their reachable final folded states under different settings and give proof of all the inclusion relationships between every two of these sets. In addition, there are nine distinct problems with the three folding models applied to three folding settings. We give the optimal linear time solutions to all the unsolved ones: the valid total overlapping order problems of 1 × n maps, m × n maps, as well as the valid boundary overlapping order problems of m × n maps with the three different folding models. Our work gives the conclusion of the research field where the folding models and the overlapping orders of map folding are concerned.

The Explanatory Generalized Graded Unfolding Model: Incorporating Collateral Information to Improve the Latent Trait Estimation Accuracy

Collateral information has been used to address subpopulation heterogeneity and increase estimation accuracy in some large-scale cognitive assessments. The methodology that takes collateral information into account has not been developed and explored in published research with models designed specifically for noncognitive measurement. Because the accurate noncognitive measurement is becoming increasingly important, we sought to examine the benefits of using collateral information in latent trait estimation with an item response theory model that has proven valuable for noncognitive testing, namely, the generalized graded unfolding model (GGUM). Our presentation introduces an extension of the GGUM that incorporates collateral information, henceforth called Explanatory GGUM. We then present a simulation study that examined Explanatory GGUM latent trait estimation as a function of sample size, test length, number of background covariates, and correlation between the covariates and the latent trait. Results indicated the Explanatory GGUM approach provides scoring accuracy and precision superior to traditional expected a posteriori (EAP) and full Bayesian (FB) methods. Implications and recommendations are discussed.

bmggum: An R Package for Bayesian Estimation of the Multidimensional Generalized Graded Unfolding Model With Covariates

Over the past couple of decades, there has been an increasing interest in adopting ideal point models to represent noncognitive constructs, as they have been demonstrated to better measure typical behaviors than traditional dominance models do. The generalized graded unfolding model ( GGUM) has consistently been the most popular ideal point model among researchers and practitioners. However, the GGUM2004 software and the later developed GGUM package in R can only handle unidimensional models despite the fact that many noncognitive constructs are multidimensional in nature. In addition, GGUM2004 and the GGUM package often yield unreasonable estimates of item parameters and standard errors. To address these issues, we developed the new open-source bmggum R package that is capable of estimating both unidimensional and multidimensional GGUM using a fully Bayesian approach, with supporting capabilities of stabilizing parameterization, incorporating person covariates, estimating constrained models, providing fit diagnostics, producing convergence metrics, and effectively handling missing data.

Thalassa, "Confusion of the tongues", "The unwelcome child and his death-instinct": Sándor Ferenczi's unfolding model of trauma

In this article, I trace the evolution and unfolding meanings and use of Ferenczi's model of trauma as it appears in his work on sexual abuse, on war trauma, and on early neglect. Some are works of quite long gestation and some written and published in the context of immediate circumstances, for instance, his paper on war neurosis. Ferenczi's work is seen as an influence on the psychoanalytic study of somatic states, on early gaps in psychic structure and in early and adult trauma.

GGUM: An R Package for Fitting the Generalized Graded Unfolding Model

In this article, the newly created GGUM R package is presented. This package finally brings the generalized graded unfolding model (GGUM) to the front stage for practitioners and researchers. It expands the possibilities of fitting this type of item response theory (IRT) model to settings that, up to now, were not possible (thus, beyond the limitations imposed by the widespread GGUM2004 software). The outcome is therefore a unique software, not limited by the dimensions of the data matrix or the operating system used. It includes various routines that allow fitting the model, checking model fit, plotting the results, and also interacting with GGUM2004 for those interested. The software should be of interest to all those who are interested in IRT in general or to ideal point models in particular.

The nonideological component of coalition preferences

Recent research on political attitudes has emphasized that coalition preferences determine electoral choices, prompting scholars to investigate the sources of coalition preferences. While it is not surprising that coalition preferences are strongly informed by spatial considerations, several studies have drawn attention to additional nonideological factors. Relying on this insight, the present study aims to systematically investigate the nonideological or valence component of coalition preferences. In order to decompose attitudes into their principal ideological and nonideological components, we apply a Bayesian unfolding model to coalition sympathy ratings. We find that coalitions differ strongly with regard to their valence component. This surplus cannot be reconstructed as a linear combination of the coalitions’ constituent party valences and is predominantly structured by campaign valence.

The lz(p)* Person-Fit Statistic in an Unfolding Model Context

Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded unfolding model is used. Results from a simulation study indicate that the person-fit statistic performed relatively well in detecting midpoint response style patterns and not so well in detecting extreme response style patterns.

Evaluating Rater Accuracy in Rater-Mediated Assessments Using an Unfolding Model

The number of performance assessments continues to increase around the world, and it is important to explore new methods for evaluating the quality of ratings obtained from raters. This study describes an unfolding model for examining rater accuracy. Accuracy is defined as the difference between observed and expert ratings. Dichotomous accuracy ratings (0 = inaccurate, 1 = accurate) are unfolded into three latent categories: inaccurate below expert ratings, accurate ratings, and inaccurate above expert ratings. The hyperbolic cosine model (HCM) is used to examine dichotomous accuracy ratings from a statewide writing assessment. This study suggests that HCM is a promising approach for examining rater accuracy, and that the HCM can provide a useful interpretive framework for evaluating the quality of ratings obtained within the context of rater-mediated assessments.