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Abstract Differential item functioning (DIF) screening has long been suggested to ensure assessment fairness. Traditional DIF methods typically focus on the main effects of demographic variables on item parameters, overlooking the interactions among multiple identities. Drawing on the intersectionality framework, we define intersectional DIF as deviations in item parameters that arise from the interactions among demographic variables beyond their main effects and propose a novel item response theory (IRT) approach for detecting intersectional DIF. Under our framework, fixed effects are used to account for traditional DIF, while random item effects are introduced to capture intersectional DIF. We further introduce the concept of intersectional impact, which refers to interaction effects on group-level mean ability. Depending on which item parameters are affected and whether intersectional impact is considered, we propose four models, which aim to detect intersectional uniform DIF (UDIF), intersectional UDIF with intersectional impact, intersectional non-uniform DIF (NUDIF), and intersectional NUDIF with intersectional impact, respectively. For efficient model estimation, a regularized Gaussian variational expectation-maximization algorithm is developed. Simulation studies demonstrate that our methods can effectively detect intersectional UDIF, although their detection of intersectional NUDIF is more limited. 

Abstract Data harmonization is an emerging approach to strategically combining data from multiple independent studies, enabling addressing new research questions that are not answerable by a single contributing study. A fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across studies. In this study, we focus on a regularized explanatory multidimensional item response theory model (re-MIRT) for establishing measurement equivalence across instruments and studies, where regularization enables the detection of items that violate measurement invariance, also known as differential item functioning (DIF). Because the MIRT model is computationally demanding, we leverage the recently developed Gaussian Variational Expectation–Maximization (GVEM) algorithm to speed up the computation. In particular, the GVEM algorithm is extended to a more complicated and improved multi-group version with categorical covariates and Lasso penalty for re-MIRT, namely, the importance weighted GVEM with one additional maximization step (IW-GVEMM). This study aims to provide empirical evidence to support feasible uses of IW-GVEMM for re-MIRT DIF detection, providing a useful tool for integrative data analysis. Our results show that IW-GVEMM accurately estimates the model, detects DIF items, and finds a more reasonable number of DIF items in a real world dataset. The proposed method has been integrated intoRpackageVEMIRT(https://map-lab-uw.github.io/VEMIRT). 

TikTok has gained immense popularity among teenagers, offering access to numerous user-generated content. Notably, food videos have emerged as a prominent theme on this short-form video social platform. The casual and enjoyable nature of short food videos on TikTok belies their potential influence on one of teenagers' most immediate and regular health practices--eating. Understanding how teenagers interact with these videos, their subsequent actions, and the resulting impact on their food practices and eating habits have the potential to provide insight into their broader lifestyle choices and their interactions within their social circles, including parents, friends, and other people online. By examining how teenagers use TikTok food videos online and offline, we gain a deeper understanding of the intricate relationship between social media, teenage lifestyle, and social dynamics surrounding food practices. In this research, we conducted 15 semi-structured interviews with teenagers aged 13 to 19, investigating their consumption of TikTok food videos and the actions inspired by them. By examining the multifaceted influence of TikTok food videos from a temporal perspective, this study contributes to the reflections of teens' use of TikTok food videos and their inspired food practices in the short and long term, online and offline. We propose design and theoretical implications to support teenagers' health. These insights have the potential to extend to various contexts, helping educators, policymakers, and designers in fostering healthy lifestyles among teenagers. 

Background/Objectives: Hydrophobic semi-solid or liquid biodegradable polymers have shown unique advantages as injectable matrices for sustained release of a wide range of drugs. Here we report the design, synthesis, and characterization of a new low-melt liquid copolymer based on poly(ε-caprolactone) (PCL) and establish its utility as a versatile delivery platform. Methods: The copolymer, mPA20, consisting of short PCL blocks connected via acid-labile acetal linkages, was synthesized using a one-pot reaction and its properties were comprehensively characterized. Results: mPA20 is an amorphous, injectable liquid at physiological temperature and can undergo pH-sensitive hydrolytic degradation. mPA20 bearing methacrylate end groups can be photo-crosslinked into solid matrices with tunable mechanical properties. A hydrophobic fluorophore, Nile Red (NR), was solubilized in mPA20 without any solvent. Sustained release of NR into aqueous medium was achieved using mPA20, either as an injectable liquid depot or a photo-crosslinked solid matrix. Further, mPA20 self-emulsified in water to form nanodroplets, which were subsequently photo-crosslinked into nanogels. Both the nanodroplets and nanogels mediated efficient intracellular delivery of NR with no cytotoxicity. Conclusions: mPA20, a new photo-crosslinkable, hydrophobic liquid copolymer with pH-sensitive degradability, is highly adaptable as either an injectable or implantable depot or nanoscale carrier for the controlled release and intracellular delivery of poorly soluble drugs. 

Modern assessment demands, resulting from educational reform efforts, call for strengthening diagnostic testing capabilities to identify not only the understanding of expected learning goals but also related intermediate understandings that are steppingstones on pathways to learning goals. An accurate and nuanced way of interpreting assessment results will allow subsequent instructional actions to be targeted. An appropriate psychometric model is indispensable in this regard. In this study, we developed a new psychometric model, namely, the diagnostic facet status model (DFSM), which belongs to the general class of cognitive diagnostic models (CDM), but with two notable features: (1) it simultaneously models students’ target understanding (i.e., goal facet) and intermediate understanding (i.e., intermediate facet); and (2) it models every response option, rather than merely right or wrong responses, so that each incorrect response uniquely contributes to discovering students’ facet status. Given that some combination of goal and intermediate facets may be impossible due to facet hierarchical relationships, a regularized expectation–maximization algorithm (REM) was developed for model estimation. A log-penalty was imposed on the mixing proportions to encourage sparsity. As a result, those impermissible latent classes had estimated mixing proportions equal to 0. A heuristic algorithm was proposed to infer a facet map from the estimated permissible classes. A simulation study was conducted to evaluate the performance of REM to recover facet model parameters and to identify permissible latent classes. A real data analysis was provided to show the feasibility of the model. 

Understanding the relationship between multiscale morphology and electronic structure is a grand challenge for semiconducting soft materials. Computational studies aimed at characterizing these multiscale relationships require the complex integration of quantum-chemical (QC) calculations, all-atom and coarse-grained (CG) molecular dynamics simulations, and back-mapping approaches. However, the integration and scalability of these methods pose substantial computational challenges that limit their application to the requisite length scales of soft material morphologies. Here, we demonstrate the bottom-up electronic coarse-graining (ECG) of morphology-dependent electronic structure in the liquid-crystal-forming semiconductor, 2-(4-methoxyphenyl)-7-octyl-benzothienobenzothiophene (BTBT). ECG is applied to construct density functional theory (DFT)-accurate valence band Hamiltonians of the isotropic and smectic liquid crystal (LC) phases using only the CG representation of BTBT. By bypassing the atomistic resolution and its prohibitive computational costs, ECG enables the first calculations of the morphology dependence of the electronic structure of charge carriers across LC phases at the ~20 nm length scale, with robust statistical sampling. kinetic Monte Carlo (kMC) simulations reveal a strong morphology dependence on zero-field charge mobility among different LC phases as well as the presence of two-molecule charge carriers that act as traps and hinder charge transport. We leverage these results to further evaluate the feasibility of developing truly mesoscopic, field-based ECG models in future works. The fully CG approach to electronic property predictions in LC semiconductors opens a new computational direction for designing electronic processes in soft materials at their characteristic length scales.