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Abstract Nowadays, multidimensional data are often available from educational testing. One natural issue is to identify whether more dimensional data are useful in fitting the item response data. To address this important issue, we develop a new decomposition of Widely Applicable Information Criterion (WAIC) via the posterior predictive ordinate (PPO) under the joint model for the response, response time and two additional educational testing scores. Based on this decomposition, a new model assessment criterion is then proposed, which allows us to determine which of the response time and two additional scores are most useful in fitting the response data and whether other dimensional data are further needed given that one of these dimensional data is already included in the joint model with the response data. In addition, an efficient Monte Carlo method is developed to compute PPO. An extensive simulation study is conducted to examine the empirical performance of the proposed joint model and the model assessment criterion in the psychological setting. The proposed methodology is further applied to an analysis of a real dataset from a computerized educational assessment program. 

Wearable devices for continuous health monitoring in humans are constantly evolving, yet the signal quality may be improved by optimizing electrode placement. While the commonly used locations to measure electrodermal activity (EDA) are at the fingers or the wrist, alternative locations, such as the torso, need to be considered when applying an integrated multimodal approach of concurrently recording multiple bio-signals, such as the monitoring of visceral pain symptoms like those related to irritable bowel syndrome (IBS). This study aims to quantitatively determine the EDA signal quality at four torso locations (mid-chest, upper abdomen, lower back, and mid-back) in comparison to EDA signals recorded from the fingers. Concurrent EDA signals from five body locations were collected from twenty healthy participants as they completed a Stroop Task and a Cold Pressor task that elicited salient autonomic responses. Mean skin conductance (meanSCL), non-specific skin conductance responses (NS.SCRs), and sympathetic response (TVSymp) were derived from the torso EDA signals and compared with signals from the fingers. Notably, TVSymp recorded from the mid-chest location showed significant changes between baseline and Stroop phase, consistent with the TVSymp recorded from the fingers. A high correlation (0.77–0.83) was also identified between TVSymp recorded from the fingers and three torso locations: mid-chest, upper abdomen, and lower back locations. While the fingertips remain the optimal site for EDA measurement, the mid-chest exhibited the strongest potential as an alternative recording site, with the upper abdomen and lower back also demonstrating promising results. These findings suggest that torso-based EDA measurements have the potential to provide reliable measurement of sympathetic neural activities and may be incorporated into a wearable belt system for multimodal monitoring. 

The recent surge in computerized testing brings challenges in the analysis of testing data with classic item response theory (IRT) models. To handle individually varying and irregularly spaced longitudinal dichotomous responses, we adopt a dynamic IRT model framework and then extend the model to link with individual characteristics at a hierarchical level. Further, we have developed an algorithm to select important characteristics of individuals that can capture the growth changes of one’s ability under this multi-level dynamic IRT model, where we can compute the Bayes factor of the proposed model including different covariates using a single Markov chain Monte Carlo output from the full model. In addition, we have shown the model selection consistency under the modified Zellner–Siow prior, and we have conducted simulations to illustrate the properties of the model selection consistency in finite samples. Finally, we have applied our proposed model and computational algorithms to a real data application, called EdSphere dataset, in educational testing. 

As binary switches, RAS proteins switch to an ON/OFF state during signaling and are on a leash under normal conditions. However, in RAS-related diseases such as cancer and RASopathies, mutations in the genes that regulate RAS signaling or the RAS itself permanently activate the RAS protein. The structural basis of this switch is well understood; however, the exact mechanisms by which RAS proteins are regulated are less clear. RAS/MAPK syndromes are multisystem developmental disorders caused by germline mutations in genes associated with the RAS/mitogen-activated protein kinase pathway, impacting 1 in 1,000–2,500 children. These include a variety of disorders such as Noonan syndrome (NS) and NS-related disorders (NSRD), such as cardio facio cutaneous (CFC) syndrome, Costello syndrome (CS), and NS with multiple lentigines (NSML, also known as LEOPARD syndrome). A frequent manifestation of cardiomyopathy (CM) and hypertrophic cardiomyopathy associated with RASopathies suggest that RASopathies could be a potential causative factor for CM. However, the current supporting evidence is sporadic and unclear. RASopathy-patients also display a broad spectrum of congenital heart disease (CHD). More than 15 genes encode components of the RAS/MAPK signaling pathway that are essential for the cell cycle and play regulatory roles in proliferation, differentiation, growth, and metabolism. These genes are linked to the molecular genetic pathogenesis of these syndromes. However, genetic heterogeneity for a given syndrome on the one hand and alleles for multiple syndromes on the other make classification difficult in diagnosing RAS/MAPK-related diseases. Although there is some genetic homogeneity in most RASopathies, several RASopathies are allelic diseases. This allelism points to the role of critical signaling nodes and sheds light on the overlap between these related syndromes. Even though considerable progress has been made in understanding the pathophysiology of RASopathy with the identification of causal mutations and the functional analysis of their pathophysiological consequences, there are still unidentified causal genes for many patients diagnosed with RASopathies. 

Computerized assessment provides rich multidimensional data including trial-by-trial accuracy and response time (RT) measures. A key question in modeling this type of data is how to incorporate RT data, for example, in aid of ability estimation in item response theory (IRT) models. To address this, we propose a joint model consisting of a two-parameter IRT model for the dichotomous item response data, a log-normal model for the continuous RT data, and a normal model for corresponding paper-and-pencil scores. Then, we reformulate and reparameterize the model to capture the relationship between the model parameters, to facilitate the prior specification, and to make the Bayesian computation more efficient. Further, we propose several new model assessment criteria based on the decomposition of deviance information criterion (DIC) the logarithm of the pseudo-marginal likelihood (LPML). The proposed criteria can quantify the improvement in the fit of one part of the multidimensional data given the other parts. Finally, we have conducted several simulation studies to examine the empirical performance of the proposed model assessment criteria and have illustrated the application of these criteria using a real dataset from a computerized educational assessment program.