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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]

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 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 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.

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]

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]

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]