Psychometric Research & Development (United Kingdom)

We present a comprehensive study of Ultraluminous Infrared Galaxies (ULIRGs), leveraging data from the IRAS Faint Source Catalogue (FSC) and the spectroscopic catalog in the Sloan Digital Sky Survey (SDSS) DR16. Our meticulous cross-matching technique significantly enhances the reliability of ULIRG identification, resulting in the identification of 283 reliable ULIRGs, including 102 new detections, while discarding 120 previously reported false sources. Covering a redshift range of $z = 0.018 - 0.996$, with a median redshift of $\bar{z} = 0.259$, our uniform sample reveals apparent interaction features in approximately 40\% of ULIRGs, increasing to 92\% for those with $z < 0.1$. Through optical spectra analysis, it is indicated that over 58\% of ULIRGs host an AGN, which is twice as high as the detections based solely on infrared colors. Moreover, a pronounced excess of radio emissions associated with AGN activity results in a steeper radio-far-infrared correlation. Notably, Type I ULIRGs exhibit properties similar to those of narrow-line Seyfert 1 galaxies (NLS1s), with an elevated incidence rate of \ion{Mg}{2} BALs (16.7\%), surpassing that of typical optically selected quasars by over tenfold, consistent with current evolutionary models. We anticipate that forthcoming telescopes such as the China Space Station Telescope (CSST) and Leighton Chajnantor Telescope (LCT) will provide deeper insights into ULIRG morphology, dust distribution, molecular gas, and AGN activity.

This study examined the incremental values of academic predictors that may be adopted as nursing school admission criteria. The findings revealed that using additional content areas of mathematics, reading, and English in conjunction with science contributes significantly to the prediction of early nursing school success. This study demonstrates an incremental validity approach that provides valuable guidelines to inform the selection of effective, accurate, and cost-effective admission tools for screening qualified nursing candidates.

In educational measurement, responses of students on items are used not only to measure the ability of students, but also to evaluate and compare the performance of schools. Analysis should ideally account for the multilevel structure of the data, and school-level processes not related to ability, such as working climate and administration conditions, need to be separated from student and school ability. However, in educational studies such as Programme for International Student Assessment, Trends in International Mathematics and Science Study, and COOL5–18, this is hardly ever done. This study presents a model that simultaneously accounts for the nested structure, controls student ability for processes at school level, classifies schools to monitor and compare schools, and tests for school-level item bias.

An historical and theoretical review is provided of so called item response theory (IRT), more accurately described as item response modelling (IRM). This paper looks at 50 years of IRM and finds a disappointing lack of advance. It is shown how a linear model framework, involving different response transformations, unifies separate approaches to the study of test item responses.