Development and Validation of the Digital Sensitivity Scale for Adults: Cross-Sectional Observational Study.

Background: The COVID-19 pandemic has accelerated the digitalization of modern society, extending digital transformation to daily life and psychological evaluation and treatment. However, the development of competencies and literacy in handling digital technology has not kept pace, resulting in a significant disparity among individuals. Existing measurements of digital literacy were developed before widespread information and communications technology device adoption, mainly focusing on one's perceptions of their proficiency and the utility of device operation.

A response time-based mixture item response theory model for dynamic item-response strategies.

Educational researchers have a long-lasting interest in the strategies examinees employ when responding to items in an assessment. Mixture item response theory (IRT) modeling is a popular class of approaches to studying examinees' item-response strategies. In the present study, we introduce a response time (RT)-based mixture IRT model for flexible modeling of examinee-and-item-specific item-response strategies.

How does item wording affect participants' responses in Likert scale? Evidence from IRT analysis.

Researchers often combine both positively and negatively worded items when constructing Likert scales. This combination, however, may introduce method effects due to the variances in item wording. Although previous studies have tried to quantify these effects by using factor analysis on scales with different content, the impact of varied item wording on participants' choices among specific options remains unexplored.

A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data.

The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early intervention and can lead to higher success rates in treatment. Self-reported feelings by patients represent a valuable biomarker for predicting depression as they can be expressed in a lower-dimensional network form, offering an advantage in visualizing the interactive characteristics of depression-related feelings. Furthermore, the network form of data expresses high-dimensional data in a compact form, making the data easy to use as input for the machine learning processes.

A Recent Development of a Network Approach to Assessment Data: Latent Space Item Response Modeling for Intelligence Studies.

This article aims to provide an overview of the potential advantages and utilities of the recently proposed Latent Space Item Response Model (LSIRM) in the context of intelligence studies. The LSIRM integrates the traditional Rasch IRT model for psychometric data with the latent space model for network data. The model has person-wise latent abilities and item difficulty parameters, capturing the main person and item effects, akin to the Rasch model.

The mediating effect of attentional impulsivity between mindfulness and problematic smartphone use.

Objective: Problematic smartphone use has been linked to lower levels of mindfulness, impaired attentional function, and higher impulsivity. This study aimed to identify the psychological mechanisms of problematic smartphone use by exploring the relationship between addictive smartphone use, mindfulness, attentional function and impulsivity.

Methods: Ninety participants were evaluated with the smartphone addiction proneness scale and classified into the problematic smartphone use group (n = 42; 24 women; mean age: 27.

Are teachers meeting students' needs in untracked science classrooms? Evidence based on a causal inferential approach.

Tracking has been criticized for relegating disadvantaged students to lower track courses in which students encounter a greater lack of instructional support. While an end to tracks through detracking is a possible solution, there are concerns that detracking will create more heterogeneous classrooms, making it harder for teachers to provide adequate support to their students. Using the 2015 PISA dataset, this study conducts a causal inferential analysis to understand the differences in student perceptions of teaching in tracked and untracked environments.

Conditional Dependence across Slow and Fast Item Responses: With a Latent Space Item Response Modeling Approach.

There recently have been many studies examining conditional dependence between response accuracy and response times in cognitive tests. While most previous research has focused on revealing a general pattern of conditional dependence for all respondents and items, it is plausible that the pattern may vary across respondents and items. In this paper, we attend to its potential heterogeneity and examine the item and person specificities involved in the conditional dependence between item responses and response times.

How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence.

How social networks influence human behavior has been an interesting topic in applied research. Existing methods often utilized scale-level behavioral data (e.g.

A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times.

Traditional measurement models assume that all item responses correlate with each other only through their underlying latent variables. This conditional independence assumption has been extended in joint models of responses and response times (RTs), implying that an item has the same item characteristics fors all respondents regardless of levels of latent ability/trait and speed. However, previous studies have shown that this assumption is violated in various types of tests and questionnaires and there are substantial interactions between respondents and items that cannot be captured by person- and item-effect parameters in psychometric models with the conditional independence assumption.

Commentary: Explore Conditional Dependencies in Item Response Tree Data.

Item response tree (IRTree) models are widely used in various applications for their ability to differentiate sets of sub-responses from polytomous item response data based on a pre-specified tree structure. Lyu et al. (Psychometrika) article highlighted that item slopes are often lower for later nodes than earlier nodes in IRTree applications.

Modern applications of cross-classified random effects models in social and behavioral research: Illustration with R package PLmixed.

Cross-classified random effects models (CCREMs) have been developed for appropriately analyzing data with a cross-classified structure. Despite its flexibility and the prevalence of cross-classified data in social and behavioral research, CCREMs have been under-utilized in applied research. In this article, we present CCREMs as a general and flexible modeling framework, and present a wide range of existing models designed for different purposes as special instances of CCREMs.

Identifying and Supporting Academically Low-Performing Schools in a Developing Country: An Application of a Specialized Multilevel IRT Model to PISA-D Assessment Data.

Performance-targeted interventions are an important tool in improving educational outcomes and are often applied at the school level, where low-performing schools are selected for participation. In this paper, we aim to identify low-performing schools in Cambodia that are in need of support on improving students' abilities in formulating math problems. Using data from the PISA for Development project, we present an application of a structured multilevel mixture item response theory (IRT) model that utilizes strategic constraints in order to achieve our research aims.

Relationships between changing communication networks and changing perceptions of psychological safety in a team science setting: Analysis with actor-oriented social network models.

A growing evidence base suggests that complex healthcare problems are optimally tackled through cross-disciplinary collaboration that draws upon the expertise of diverse researchers. Yet, the influences and processes underlying effective teamwork among independent researchers are not well-understood, making it difficult to fully optimize the collaborative process. To address this gap in knowledge, we used the annual NIH mHealth Training Institutes as a testbed to develop stochastic actor-oriented models that explore the communicative interactions and psychological changes of its disciplinarily and geographically diverse participants.

Analyzing differences between parent- and self-report measures with a latent space approach.

We explore potential cross-informant discrepancies between child- and parent-report measures with an example of the Child Behavior Checklist (CBCL) and the Youth Self Report (YSR), parent- and self-report measures on children's behavioral and emotional problems. We propose a new way of examining the parent- and child-report differences with an interaction map estimated using a Latent Space Item Response Model (LSIRM). The interaction map enables the investigation of the dependency between items, between respondents, and between items and respondents, which is not possible with the conventional approach.

Fostering interdisciplinary collaboration: A longitudinal social network analysis of the NIH mHealth Training Institutes.

Background/objective: Growing recognition that collaboration among scientists from diverse disciplines fosters the emergence of solutions to complex scientific problems has spurred initiatives to train researchers to collaborate in interdisciplinary teams. Evaluations of collaboration patterns in these initiatives have tended to be cross-sectional, rather than clarifying temporal changes in collaborative dynamics. Mobile health (mHealth), the science of using mobile, wireless devices to improve health outcomes, is a field whose advancement needs interdisciplinary collaboration.

Mapping Unobserved Item-Respondent Interactions: A Latent Space Item Response Model with Interaction Map.

Classic item response models assume that all items with the same difficulty have the same response probability among all respondents with the same ability. These assumptions, however, may very well be violated in practice, and it is not straightforward to assess whether these assumptions are violated, because neither the abilities of respondents nor the difficulties of items are observed. An example is an educational assessment where unobserved heterogeneity is present, arising from unobserved variables such as cultural background and upbringing of students, the quality of mentorship and other forms of emotional and professional support received by students, and other unobserved variables that may affect response probabilities.

Effects of polystyrene in the brackish water flea Diaphanosoma celebensis: Size-dependent acute toxicity, ingestion, egestion, and antioxidant response.

Owing to the increasing usage of plastics, their debris is continuously deposited in marine environments, resulting in deleterious effects on aquatic organisms. Although it is known that microplastics disturb the cellular redox status, knowledge of molecular in marine cladocerans is still lacking. In the present study, we investigated the acute toxicity of different-sized polystyrene (PS) beads (0.

Modeling Within-Item Dependencies in Parallel Data on Test Responses and Brain Activation.

In this paper, we propose a joint modeling approach to analyze dependency in parallel response data. We define two types of dependency: higher-level dependency and within-item conditional dependency. While higher-level dependency can be estimated with common latent variable modeling approaches, within-item conditional dependency is a unique kind of information that is often not captured with extant methods, despite its potential to shed new insights into the relationship between the two types of response data.

Trivariate Theory of Mind Data Analysis with a Conditional Joint Modeling Approach.

Theory of mind (ToM) is an essential social-cognitive ability to understand one's own and other people's mental states. Neural data as well as behavior data have been utilized in ToM research, but the two types of data have rarely been analyzed together, creating a large gap in the literature. In this paper, we propose and apply a novel joint modeling approach to analyze brain activations with two types of behavioral data, response times and response accuracy, obtained from a multi-item ToM assessment, with the intention to shed new light on the nature of the underlying process of ToM reasoning.

MIMIC Models for Uniform and Nonuniform DIF as Moderated Mediation Models.

In this article, the authors describe how multiple indicators multiple cause (MIMIC) models for studying uniform and nonuniform differential item functioning (DIF) can be conceptualized as mediation and moderated mediation models. Conceptualizing DIF within the context of a moderated mediation model helps to understand DIF as the effect of some variable on measurements that is not accounted for by the latent variable of interest. In addition, useful concepts and ideas from the mediation and moderation literature can be applied to DIF analysis: (a) improving the understanding of uniform and nonuniform DIF as direct effects and interactions, (b) understanding the implication of indirect effects in DIF analysis, (c) clarifying the interpretation of the "uniform DIF parameter" in the presence of nonuniform DIF, and (d) probing interactions and using the concept of "conditional effects" to better understand the patterns of DIF across the range of the latent variable.

Evaluation on types of invariance in studying extreme response bias with an IRTree approach.

In recent years, item response tree (IRTree) approaches have received increasing attention in the response style literature for their ability to partial out response style latent variables as well as associated item parameters. When an IRTree approach is adopted to measure extreme response styles, directional and content invariance could be assumed at the latent variable and item parameter levels. In this study, we propose to evaluate the empirical validity of these invariance assumptions by employing a general IRTree model with relaxed invariance assumptions.