A Note on Ising Network Analysis with Missing Data.

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e.

Pairwise stochastic approximation for confirmatory factor analysis of categorical data.

Pairwise likelihood is a limited-information method widely used to estimate latent variable models, including factor analysis of categorical data. It can often avoid evaluating high-dimensional integrals and, thus, is computationally more efficient than relying on the full likelihood. Despite its computational advantage, the pairwise likelihood approach can still be demanding for large-scale problems that involve many observed variables.

DIF Analysis with Unknown Groups and Anchor Items.

Ensuring fairness in instruments like survey questionnaires or educational tests is crucial. One way to address this is by a Differential Item Functioning (DIF) analysis, which examines if different subgroups respond differently to a particular item, controlling for their overall latent construct level. DIF analysis is typically conducted to assess measurement invariance at the item level.

DIF Statistical Inference Without Knowing Anchoring Items.

Establishing the invariance property of an instrument (e.g., a questionnaire or test) is a key step for establishing its measurement validity.

Rotation to Sparse Loadings Using [Formula: see text] Losses and Related Inference Problems.

Researchers have widely used exploratory factor analysis (EFA) to learn the latent structure underlying multivariate data. Rotation and regularised estimation are two classes of methods in EFA that they often use to find interpretable loading matrices. In this paper, we propose a new family of oblique rotations based on component-wise [Formula: see text] loss functions [Formula: see text] that is closely related to an [Formula: see text] regularised estimator.

Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework.

Latent variable models have been playing a central role in psychometrics and related fields. In many modern applications, the inference based on latent variable models involves one or several of the following features: (1) the presence of many latent variables, (2) the observed and latent variables being continuous, discrete, or a combination of both, (3) constraints on parameters, and (4) penalties on parameters to impose model parsimony. The estimation often involves maximizing an objective function based on a marginal likelihood/pseudo-likelihood, possibly with constraints and/or penalties on parameters.

Inhibition of TRPA1 Attenuates Oxidative Stress-induced Damage After Traumatic Brain Injury via the ERK/AKT Signaling Pathway.

Neuron apoptosis is a feature of secondary injury after traumatic brain injury (TBI). Evidence implies that excess calcium (Ca) ions and reactive oxidative species (ROS) play critical roles in apoptosis. In reaction to increased ROS, the anti-oxidative master transcription factor, Transient receptor potential Ankyrin 1 (TRPA1) allows Ca ions to enter cells.

Reassessing Diabetes and APOE Genotype as Potential Interacting Risk Factors for Alzheimer's Disease.

To assess whether diabetes alone or in association with Apolipoprotein E (APOE) ε4 genotype increases the risk of Alzheimer's Disease (AD) diagnosis. A retrospective cohort study of 33,456 participants from the National Alzheimer's Coordinating Center database. Participants with one or two APOE ε4 alleles had 2.

Establishment and validation of a prediction model for intraparenchymal hematoma expansion in patients with cerebral contusion: A reliable Nomogram.

Background And Objective: Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). Traumatic intraparenchymal hematoma (TICH) expansion severely affects the patient's prognosis. In this study, the baseline data, imaging features, and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a nomogram predictive model assessing the risk factors for TICH expansion.

A Note on Likelihood Ratio Tests for Models with Latent Variables.

The likelihood ratio test (LRT) is widely used for comparing the relative fit of nested latent variable models. Following Wilks' theorem, the LRT is conducted by comparing the LRT statistic with its asymptotic distribution under the restricted model, a [Formula: see text] distribution with degrees of freedom equal to the difference in the number of free parameters between the two nested models under comparison. For models with latent variables such as factor analysis, structural equation models and random effects models, however, it is often found that the [Formula: see text] approximation does not hold.

A Continuous-Time Dynamic Choice Measurement Model for Problem-Solving Process Data.

Problem solving has been recognized as a central skill that today's students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A popular tool for the measurement of problem solving is simulated interactive tasks, which require students to uncover some of the information needed to solve the problem through interactions with a computer-simulated environment.

A Note on Exploratory Item Factor Analysis by Singular Value Decomposition.

We revisit a singular value decomposition (SVD) algorithm given in Chen et al. (Psychometrika 84:124-146, 2019b) for exploratory item factor analysis (IFA). This algorithm estimates a multidimensional IFA model by SVD and was used to obtain a starting point for joint maximum likelihood estimation in Chen et al.

A Latent Gaussian process model for analysing intensive longitudinal data.

Intensive longitudinal studies are becoming progressively more prevalent across many social science areas, and especially in psychology. New technologies such as smart-phones, fitness trackers, and the Internet of Things make it much easier than in the past to collect data for intensive longitudinal studies, providing an opportunity to look deep into the underlying characteristics of individuals under a high temporal resolution. In this paper we introduce a new modelling framework for latent curve analysis that is more suitable for the analysis of intensive longitudinal data than existing latent curve models.

Skeletal representations of shape in human vision: Evidence for a pruned medial axis model.

A representation of shape that is low dimensional and stable across minor disruptions is critical for object recognition. Computer vision research suggests that such a representation can be supported by the medial axis-a computational model for extracting a shape's internal skeleton. However, few studies have shown evidence of medial axis processing in humans, and even fewer have examined how the medial axis is extracted in the presence of disruptive contours.

Statistical Analysis of Complex Problem-Solving Process Data: An Event History Analysis Approach.

Complex problem-solving (CPS) ability has been recognized as a central 21st century skill. Individuals' processes of solving crucial complex problems may contain substantial information about their CPS ability. In this paper, we consider the prediction of duration and final outcome (i.

Latent Class Analysis of Recurrent Events in Problem-Solving Items.

Computer-based assessment of complex problem-solving abilities is becoming more and more popular. In such an assessment, the entire problem-solving process of an examinee is recorded, providing detailed information about the individual, such as behavioral patterns, speed, and learning trajectory. The problem-solving processes are recorded in a computer log file which is a time-stamped documentation of events related to task completion.

Mutual Information Reliability for Latent Class Analysis.

Latent class models are powerful tools in psychological and educational measurement. These models classify individuals into subgroups based on a set of manifest variables, assisting decision making in a diagnostic system. In this article, based on information theory, the authors propose a mutual information reliability (MIR) coefficient that summaries the measurement quality of latent class models, where the latent variables being measured are categorical.

An improved stochastic EM algorithm for large-scale full-information item factor analysis.

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including an adaptive-rejection-based Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization in the M step, and diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000, Bernoulli, 6, 457), as well as advanced sampling and optimization techniques.

Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis.

Joint maximum likelihood (JML) estimation is one of the earliest approaches to fitting item response theory (IRT) models. This procedure treats both the item and person parameters as unknown but fixed model parameters and estimates them simultaneously by solving an optimization problem. However, the JML estimator is known to be asymptotically inconsistent for many IRT models, when the sample size goes to infinity and the number of items keeps fixed.

A reinforcement learning approach to personalized learning recommendation systems.

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. With the latest advances in information technology and data science, personalized learning is becoming possible for anyone with a personal computer, supported by a data-driven recommendation system that automatically schedules the learning sequence. The engine of such a recommendation system is a recommendation strategy that, based on data from other learners and the performance of the current learner, recommends suitable learning materials to optimize certain learning outcomes.

Dietary patterns are not associated with overweight and obesity in a sample of 8900 Chinese preschool children from four cities.

Globally, the prevalence of childhood obesity has substantially increased at an alarming rate. This study investigated associations between dietary patterns and overweight/obesity in 3- to 6-year-old children. Recruited children were from four prefecture-level cities in Eastern China.

The role of miR-328 in high glucose-induced endothelial-to-mesenchymal transition in human umbilical vein endothelial cells.

Aims: Endothelial-to-mesenchymal transition (EndMT) contribute to diabetic cardiac fibrosis, the underlying mechanisms are poorly understood. In the study, we aimed to investigate the role of miR-328 in EndMT mediated by high glucose (HG) and the signaling pathways implicated in human umbilical vein endothelial cells (HUVECs).

Materials And Methods: EndMT of HUVECs was determined by immunofluorescent staining and western blot of the markers CD31 and α-SMA.

Robust Measurement via A Fused Latent and Graphical Item Response Theory Model.

Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large.