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The objective of this article was to find an optimal decision rule for identifying polytomous items with large or moderate amounts of differential functioning. The effectiveness of combining statistical tests with effect size measures was assessed using logistic discriminant function analysis and two effect size measures: R[superscript 2] and conditional log odds ratio in delta scale (?[subscript LR]). Four independent variables were manipulated: (a) different sample sizes for the reference and focal groups (1,000/500, 1,000/250, 500/250), (b) impact between reference and focal group (equal-ability distribution, i.e., no impact; or different-ability distribution, i.e., impact), (c) the percentage of differential item functioning (DIF) items in a test (0%, 12%, i.e., only the first three items of the test; 20%, i.e., the first five items of the test; 32%, i.e., the first eight items of the test), and (d) direction of DIF (one-sided and both-sided). The magnitudes of DIF were indirectly manipulated through the percentage of DIF items and DIF direction, and they were simulated to be moderate or large. The results show that the false positive rates were low when an effect size decision rule was used in combination with a statistical test, and they were very low when R[superscript 2] effect size criteria were applied. With respect to power, when a statistical test was used in conjunction with effect size criteria to determine whether an item exhibited a meaningful magnitude of DIF, we found when using the ?[subscript LR] decision rule that the percentage of meaningful DIF items was higher with greater amounts of DIF. Examining DIF by means of blended statistical tests, in other words, those incorporating both the "p" value and effect size measures, can be recommended as a procedure for classifying items displaying DIF.

Tests are important tools that help psychologists to make decisions about people. In order to propose actions aimed at improving the use of tests, it is important to determine what factors relate to psychologists' opinions about testing practices. In this study we assess the role of three factors: gender, age, and job sector (public vs. private). In addition, we compare practitioners' opinions with those reported by professors of test-related subjects (psychometrics, and psychological and psycho-educational assessment). After analyzing a sample of 1,248 members of the Spanish Psychological Association and 95 professors from different universities, results show some differences in opinions across all the factors analyzed. Results and future challenges are discussed. [ABSTRACT FROM AUTHOR]

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The COVID-19 pandemic had a detrimental effect on mental health since its start in 2020 and current data on mental health is limited. This study provides recent longitudinal data on psychological distress among a country-wide sample of adults. We recruited and surveyed 1,956 adults in Spain in April 2021 and August 2022 on sociodemographic- and pandemic-related psychological distress using the General Distress 21-item version of the Depression, Anxiety, and Stress Scale (DASS). Paired sampled t-tests assessed DASS scores by sex from April 2021 to August 2022; and one-way ANOVAs assessed DASS scores across sociodemographic characteristics. Results showed that psychological distress slightly improved across the total sample from April 2021 to August 2022; though females, young adults, students, and individuals with lower income experienced more psychological distress. Increases in severe stress scores were found particularly among men. Our data provides an overview of the psychological distress of Spanish adults 2.5 years into the pandemic and provides novel evidence that though life has resumed a sense of normalcy after the COVID-19 pandemic, the mental health of key populations (e.g., females, young adults, students, low-income) is still suffering and further intervention and resources are needed., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Rosen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The argument-based validation scheme guides assessment of the consequences of testing. The distinction between semantic inference and political inference allows us to combine the validation of the consequences in a single validation scheme. The validation process should produce evidence about the assumptions that support both types of inference. After presenting the validation scheme, we provide examples of its use in the assessment of the testing of two applications: the use of high-stake tests in educational contexts and the validation of adjustments made in standardized tests for people with disabilities. Finally, we propose procedures for the validation of consequences and we discuss the suitability of the argument-based validation scheme for the validation of the consequences of testing in Spain.