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Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node's latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it is well-known that Metropolis within Gibbs is inefficient for large networks; the acceptance ratios are expensive to compute, and the resultant posterior draws are highly correlated. In this article, we propose an alternative Markov chain Monte Carlo strategy--defined using a combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo--that leverages the posterior distribution's functional form for more efficient posterior computation. We demonstrate that these strategies outperform Metropolis within Gibbs and other algorithms on synthetic networks, as well as on real information-sharing networks of teachers and staff in a school district. [ABSTRACT FROM AUTHOR]

We study regularized estimation in high‐dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. We apply our proposed technique to a longitudinal data set on Alzheimer's disease from the Cardiovascular Health Study Cognition Study. Using data analysis and a simulation study, we motivate and demonstrate several practical considerations such as the selection of tuning parameters and the assessment of model stability. While race, gender, vascular and heart disease, lack of caregivers, and deterioration of learning and memory are all important predictors of dementia, we also find that these risk factors become more relevant in the later stages of life. [ABSTRACT FROM AUTHOR]

Through the development and field testing of an instrument designed to measure youth social capital in the context of postsecondary transitions, this research addresses the need for theory-driven measures of social capital among youth. The results offer preliminary evidence that dimensions of youth social capital, including network structure and network content, can be reliably measured and that these dimensions of social capital are interrelated in a manner consistent with theory. The results also offer initial support for the validity of the social capital construct within the domain of youth postsecondary transitions. Taken together, the findings provide a foundation for continued research that might surmount inadequate measures and theoretical disputes to encompass more careful and rigorous empirical scrutiny when it comes to the measurement of social capital among children and adolescents. [ABSTRACT FROM AUTHOR]

Research studies in psychology and education often seek to detect changes or growth in an outcome over a duration of time. This research provides a solution to those interested in estimating latent traits from psychological measures that rely on human raters. Rater effects potentially degrade the quality of scores in constructed response and performance assessments. We develop an extension of the hierarchical rater model (HRM), which yields estimates of latent traits that have been corrected for individual rater bias and variability, for ratings that come from longitudinal designs. The parameterization, called the longitudinal HRM (L-HRM), includes an autoregressive time series process to permit serial dependence between latent traits at adjacent timepoints, as well as a parameter for overall growth. We evaluate and demonstrate the feasibility and performance of the L-HRM using simulation studies. Parameter recovery results reveal predictable amounts and patterns of bias and error for most parameters across conditions. An application to ratings from a study of character strength demonstrates the model. We discuss limitations and future research directions to improve the L-HRM. [ABSTRACT FROM AUTHOR]

The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention effect using the hierarchical latent space model, an important subfamily of HNMs. We derive basic convergence results and asymptotic bounds on power, showing that standard error for the treatment effect is inversely proportional to the product of the number of ties and the number of networks; a result rather different from the usual effect of cluster size in hierarchical linear models, for example. We explore these results with a simulation study and suggest a tentative approach to power for practical applications. [ABSTRACT FROM AUTHOR]

Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another—in short, changing the professional social networks of educators. Current methods of social network analysis are ill-suited to modeling the multiple partially exchangeable networks that arise in randomized field trials and observational studies in which multiple classrooms, schools, or districts are involved, and to detecting the effect of an intervention on the social network itself. To address these needs, we introduce a new modeling framework, the Hierarchical Network Models (HNM) framework. The HNM framework can be used to extend single-network statistical network models to multiple networks, using a hierarchical modeling approach. We show how to generalize the latent space model for a single network to the HNM/multiple-network setting, and illustrate our approach with real and simulated social network data among education professionals. [ABSTRACT FROM AUTHOR]

Introduces two Markov chain Monte Carlo (MCMC) estimation procedures for the Bayesian estimation of unfolding model parameters. Direct implementation of MCMC; Use of the estimation procedure to analyze three data sets and two from real attitudinal surveys; Analysis of the unfolding model as a latent response model.

Open-ended or “constructed” student responses to test items have become a stock component of standardized educational assessments. Digital imaging of examinee work now enables a distributed rating process to be flexibly managed, and allocation designs that involve as many as six or more ratings for a subset of responses are now feasible. In this article we develop Patz’s (1996) hierarchical rater model (HRM) for polytomous item response data scored by multiple raters, and show how it can be used to scale examinees and items, to model aspects of consensus among raters, and to model individual rater severity and consistency effects. The HRM treats examinee responses to open-ended items as unobsered discrete varibles, and it explicitly models the “proficiency” of raters in assigning accurate scores as well as the proficiency of examinees in providing correct responses. We show how the HRM “fits in” to the generalizability theory framework that has been the traditional tool of analysis for rated item response data, and give some relationships between the HRM, the design effects correction of Bock, Brennan and Muraki (1999), and the rater bundle model of Wilson and Hoskens (2002). Using simulated and real data, we compare the HRM to the conventional IRT Facets model for rating data (e.g., Linacre, 1989; Engelhard, 1994, 1996), and we explore ways that information from HRM analyses may improved the quality of the rating process. [ABSTRACT FROM PUBLISHER]

Presents information on nonparametric item response theory (NIRT). Basic assumptions of NIRT; Exploratory data analysis and item and test features; Model-data fit and the explanation of data structure.

Presents a study which discussed the usability and interpretability issues for single-strategy cognitive assessment models. Discussion on the extensions of item response theory (IRT) models for cognitive assessment; Assessments of transitive reasoning in children; Description of two IRT-like cognitive assessment models.

Presents information on a study which focused on the monotonocity theory that is essential to applications of nonparametric item response models. Background on the study; Discussion on elementary symmetric functions; Methods of evaluating monotonicity; Implications for exploratory analysis of dichotomous and polytomous item response data.

Patz and Junker (1999) describe a general Markov chain Monte Carlo (MCMC) strategy, based on Metropolis-Hastings sampling, for Bayesian inference in complex item response theory (IRT) settings. They demonstrate the basic methodology using the two-parameter logistic (2PL) model. In this paper we extend their basic MCMC methodology to address issues such as non-response, designed missingness, multiple raters, guessing behavior and partial credit (polytomous) test items. We apply the basic MCMC methodology to two examples from the National Assessment of Educational Progress 1992 Trial State Assessment in Reading: (a) a multiple item format (2PL, 3PL, and generalized partial credit) subtest with missing response data; and (b) a sequence of rated, dichotomous short-response items, using a new IRT model called the generalized linear logistic test model (GLLTM). [ABSTRACT FROM PUBLISHER]

This paper demonstrates Markov chain Monte Carlo (MCMC) techniques that are particularly well-suited to complex models with item response theory (IRT) assumptions. MCMC may be thought of as a successor to the standard practice of first calibrating the items using E-M methods and then taking the item parameters to be known and fixed at their calibrated values when proceeding with inference regarding the latent trait, in contrast to this two-stage E-M approach, MCMC methods treat item and subject parameters at the same time; this allows us to incorporate standard errors of item estimates into trait inferences, and vice versa. We develop a MCMC methodology, based on Metropolis-Hastings sampling, that can be routinely implemented to fit novel IRT models, and we compare the algorithmic features of the Metropolis- Hastings approach to other approaches based on Gibbs sampling. For concreteness we illustrate the methodology using the familiar two-parameter logistic (2PL) IRT model; more complex models are treated in a subsequent paper (Patz & Junker, in press). [ABSTRACT FROM PUBLISHER]

A central assumption in the standard capture-recapture approach to the estimation of the size of a dosed population is the homogeneity of the "capture" probabilities. In this article we develop an approach that allows for varying susceptibility to capture through individual parameters using a variant of the Rasch model from psychological measurement situations. Our approach requires an additional recapture. In the context of census undercount estimation, this requirement amounts to the use of a second independent sample or alternative data source to be matched with census and Post-Enumeration Survey (PFS) data. The models we develop provide a mechanism for separating out the dependence between census and PFS reduced by individual heterogeneity. The resulting data take the form of an incomplete 23 contingency table and we describe holy to estimate the expected values of the observable cells of this table using log-linear quasi-symmetry models. The protection of these estimates onto the unobserved cell corresponding to those individuals missed by all three sources revolves the log-linear model of no second-order interaction, which is quite plausible under the Rasch model. We illustrate the models and their estimation using data from a 198g dress-rehearsal study for the 1990 census conducted by the U S Bureau of the Census, which explored the use of administrative data as a supplement to the PES. The article includes a discussion of extensions and related models. [ABSTRACT FROM AUTHOR]

An exploratory analysis of the Suicide Intent Scale was performed on a sample of 98 psychiatric inpatients who had made suicide attempts. The factor analysis was performed using a method for polychotomous data, and resulted in a two-factor solution. The Lethal Intent factor contained items pertaining to the subjective level of lethal intent, while the Planning factor contained items largely related to objective planning for the attempt. Preliminary analysis of these factors suggest that the Suicide Intent Scale can be used to evaluate two separate aspects of suicidal behavior. [ABSTRACT FROM AUTHOR]

We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. We apply our proposed technique to a longitudinal data set on Alzheimer's disease from the Cardiovascular Health Study Cognition Study. Using data analysis and a simulation study, we motivate and demonstrate several practical considerations such as the selection of tuning parameters and the assessment of model stability. While race, gender, vascular and heart disease, lack of caregivers, and deterioration of learning and memory are all important predictors of dementia, we also find that these risk factors become more relevant in the later stages of life. [ABSTRACT FROM AUTHOR]