What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data.

It is widely believed that a joint factor analysis of item responses and response time (RT) may yield more precise ability scores that are conventionally predicted from responses only. For this purpose, a simple-structure factor model is often preferred as it only requires specifying an additional measurement model for item-level RT while leaving the original item response theory (IRT) model for responses intact. The added speed factor indicated by item-level RT correlates with the ability factor in the IRT model, allowing RT data to carry additional information about respondents' ability. However, parametric simple-structure factor models are often restrictive and fit poorly to empirical data, which prompts under-confidence in the suitablity of a simple factor structure. In the present paper, we analyze the 2015 Programme for International Student Assessment mathematics data using a semiparametric simple-structure model. We conclude that a simple factor structure attains a decent fit after further parametric assumptions in the measurement model are sufficiently relaxed. Furthermore, our semiparametric model implies that the association between latent ability and speed/slowness is strong in the population, but the form of association is nonlinear. It follows that scoring based on the fitted model can substantially improve the precision of ability scores.

Different Responses to Social Capital Among Black People and White People: What Racial Differential Item Functioning Reveals for Racial Health Equity.

Social capital has been conceptualized as features of social organization, such as networks, and norms that facilitate coordination and cooperation for mutual benefit. Because of long-standing anti-Black structural oppression in the United States, social capital may be associated with health differently for Black people than for other racial/ethnic groups. Our aim was to examine the psychometric properties of social capital indicators, comparing responses from Black and White people to identify whether there is differential item functioning (DIF) in social capital according to race. DIF examines how items are related to a latent construct and whether this relationship differs across groups such as different racial groups. We used data from respondents to the Southeastern Pennsylvania Household Health Survey in 2004, who lived in Philadelphia (n = 2,048), a city with a large Black population. We used item response theory analysis to test for racial DIF. We found DIF across the items, indicating measurement error, which could be related to the way these items were developed (i.e., based on cultural assumptions tested in mainstream White America). Hence, our findings underscore the need to interrogate the assumptions that underly existing social capital items through an equity-based lens, and to take corrective action when developing new items to ensure that they are racially and culturally congruent.

Urban Heat Island Mitigation: GIS-Based Analysis for a Tropical City Singapore.

To reduce the pace of climate change and achieve the goals set in Paris Agreement by 2030, Association of Southeast Asian Nations (ASEAN) countries have started to prioritize sustainability as one of their top agendas. Numerous studies have demonstrated that one of the most important issues that must be addressed to halt climate change is the urban heat island (UHI). Given the different mitigation strategies available, the focus of our study here is to assess the influence of green spaces and Green Mark commercial buildings on Singapore's temperature distribution using non-exhaustive factors related to energy consumption and efficiency. Additionally, this paper examines the effectiveness of green spaces and commercial buildings in reducing the rate of temperature change. This study uses ArcGIS software to map data, perform spatial analysis through cloud-based mapping, and produce visual representations with geographic information systems (GIS) to promote greater insight on the formulation of goals and policy making for strategic management. In comparison to non-commercial districts, our findings show that commercial districts have the lowest percentage of temperature change, an estimated 1.6 percent, due to a high concentration of green spaces and Green Mark commercial buildings. Our research also helps to close the research gaps in determining the efficacy of Green Mark commercial buildings, skyrise greeneries, gardens, and national parks. It also helps to minimize the bottleneck of expensive building costs and environmental damage that would have occurred from a design flaw found too late in the urban planning and construction process.

Semiparametric Factor Analysis for Item-Level Response Time Data.

Item-level response time (RT) data can be conveniently collected from computer-based test/survey delivery platforms and have been demonstrated to bear a close relation to a miscellany of cognitive processes and test-taking behaviors. Individual differences in general processing speed can be inferred from item-level RT data using factor analysis. Conventional linear normal factor models make strong parametric assumptions, which sacrifices modeling flexibility for interpretability, and thus are not ideal for describing complex associations between observed RT and the latent speed. In this paper, we propose a semiparametric factor model with minimal parametric assumptions. Specifically, we adopt a functional analysis of variance representation for the log conditional densities of the manifest variables, in which the main effect and interaction functions are approximated by cubic splines. Penalized maximum likelihood estimation of the spline coefficients can be performed by an Expectation-Maximization algorithm, and the penalty weight can be empirically determined by cross-validation. In a simulation study, we compare the semiparametric model with incorrectly and correctly specified parametric factor models with regard to the recovery of data generating mechanism. A real data example is also presented to demonstrate the advantages of the proposed method.

Emergent Bilinguals With Specific Reading Comprehension Deficits: A Comparative and Longitudinal Analysis.

This study centered on emergent bilingual (EB) students with specific reading comprehension deficits (S-RCD), that is, with poor reading comprehension despite solid word identification skills. The participants were 209 students in Grades 2 to 4, including both EBs and English monolinguals (EMs) with and without S-RCD. Mean comparisons indicated that EBs and EMs with S-RCD showed weaknesses relative to typically developing (TD) readers in oral language, word identification, inference making, and reading engagement, but not in executive functioning. Longitudinal analyses indicated that across two academic years S-RCD persisted for 41% of EBs and EMs alike. Altogether, the study extends research on EBs with S-RCD by identifying variables beyond oral language that may account for their reading comprehension difficulties and providing insight into the extent to which their reading comprehension and word identification performance levels evolve during elementary school. Furthermore, the findings point to the importance of early identification and intervention for weaknesses in reading comprehension and its component elements in both EBs and EMS.