This article examines wording effects when positive and negative worded items are included in psychological assessment. Wordings effects have been analyzed in the literature using statistical approaches based on population homogeneity assumptions (i.e. CFA, SEM), commonly adopting the bifactor model to separate trait variance and wording effects. This article presents an alternative approach by explicitly modeling population heterogeneity through a latent profile model, based on the idea that a subset of individuals exhibits wording effects. This kind of mixture model allows simultaneously to classify respondents, substantively characterize the differences in their response profiles, and report respondents' results in a comparable manner. Using the Rosenberg's self-esteem scale data from the LISS Panel ( = 6,762) in three studies, we identify a subgroup of participants who respond differentially according to item-wording and examine the impact of its responses in the estimation of the RSES measurement model, in terms of global and individual fit, under one-factor and bifactor models.The results of these analyses support the interpretation of wording effects in terms of a theoretically-proposed differential pattern of response to positively and negatively worded items, introducing a valuable tool for examining the artifactual or substantive interpretations of such wording effects.
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http://dx.doi.org/10.1080/00273171.2021.1925075 | DOI Listing |
PLoS One
January 2025
Institute of Natural Antioxidants and Anti-Inflammation, Dali University, Dali, Yunnan, China.
Oxidative damage, oxidative inflammation, and a range of downstream diseases represent significant threats to human health. The application of natural antioxidants and anti-inflammatory agents can help prevent and mitigate these associated diseases. In this study, we aimed to investigate the effectiveness of walnut green husk (WNGH) as an antioxidant and anti-inflammatory agent in an in vitro setting.
View Article and Find Full Text PDFA previous study employing fMRI measures of retrieval-related cortical reinstatement reported that young, but not older, adults employ 'retrieval gating' to attenuate aspects of an episodic memory that are irrelevant to the retrieval goal. We examined whether the weak memories of the older adults in that study rendered goal-irrelevant memories insufficiently intrusive to motivate retrieval gating. Young and older participants studied words superimposed on rural or urban scenes, or on pixelated backgrounds.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, United States.
Background: Interstitial cystitis/bladder pain syndrome (IC/BPS) is a multifactorial, chronic syndrome involving urinary frequency, urgency, and bladder discomfort. These IC/BPS symptoms can significantly impact individuals' quality of life, affecting their mental, physical, sexual, and financial well-being. Individuals sometimes rely on peer-to-peer support to understand the disease and find methods of alleviating symptoms.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia.
Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision.
View Article and Find Full Text PDFJ Biomed Inform
January 2025
University of Manchester, United Kingdom.
Objective: Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities-mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identify these entities, prompting the development of specialised computational solutions. This paper systematically reviews and presents the methodologies developed for Discontinuous Named Entity Recognition in clinical texts, highlighting their effectiveness and the challenges they face.
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