People with different ideological identities differ in their values, personality, affect, and psychological motivations. These differences are observed on measures of practical and clinical importance and these differences are the central node tying together theories about the psychology of political ideology; however, they rest on a critical untested assumption: The measures are invariant across ideological groups. Here, we test this assumption across 28 constructs in data from the United States and the Netherlands. Measures are not invariant across ideological divisions. At the same time, estimates of ideological similarities and differences are largely similar before and after correcting for measurement noninvariance. This may give us increased confidence in the results from this research area, while simultaneously highlighting that some instance of noninvariance did change conclusions and that individual items are not always comparable across political groups.
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http://dx.doi.org/10.1177/1073191120983891 | DOI Listing |
Biosens Bioelectron
December 2024
Biophotonic Nanosensors Laboratory, Centro de Física Aplicada y Tecnología Avanzada (CFATA), Universidad Nacional Autónoma de México (UNAM), Querétaro, 76230, Mexico. Electronic address:
Smartphone-based colorimetric (bio)sensing is a promising alternative to conventional detection equipment for on-site testing, but it is often limited by sensitivity to lighting conditions. These issues are usually avoided using housings with fixed light sources, increasing the cost and complexity of the on-site test, where simplicity, portability, and affordability are a priority. In this study, we demonstrate that careful optimization of color space can significantly boost the performance of smartphone-based colorimetric sensing, enabling housing-free, illumination-invariant detection.
View Article and Find Full Text PDFCell Rep
January 2025
Center for Perceptual Systems, The University of Texas at Austin, Austin, TX 78712, USA; Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA. Electronic address:
The visual system adapts to maintain sensitivity and selectivity over a large range of luminance intensities. One way that the retina maintains sensitivity across night and day is by switching between rod and cone photoreceptors, which alters the receptive fields and interneuronal correlations of retinal ganglion cells (RGCs). While these adaptations allow the retina to transmit visual information to the brain across environmental conditions, the code used for that transmission varies.
View Article and Find Full Text PDFBr J Soc Psychol
January 2025
School of Psychology, The University of Adelaide, Adelaide, Australia.
This article reports the development and validation of the Episodic Empowerment Scale (EES): A manipulation check designed to measure a momentary psychological state. In Study 1, participants (n = 125) completed a selection of candidate items after being exposed to a low- or high-power manipulation. Exploratory factor analysis was used to reduce the number of items to a brief five-item measure.
View Article and Find Full Text PDFNarra J
December 2024
Curtin School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
Research focus has transitioned from interprofessional collaborative practice among qualified health practitioners to the involvement of pre-qualifying students in practicing interprofessional education. It is essential to establish outcome measures to enhance the seamless integration of interprofessional education and collaborative practice. The aim of this study was to develop a culturally appropriate quality measure for assessing interprofessional education and collaborative practice for health practitioners and students in Indonesia by performing cross-cultural validation of the collaborative practice assessment tool (CPAT).
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, Bochum 44780, Germany.
Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of the system of interest. As the construction of this data is computationally demanding, many schemes for identifying the most important structures have been proposed. Here, we compare the performance of high-dimensional neural network potentials (HDNNPs) for quantum liquid water at ambient conditions trained to data sets constructed using random sampling as well as various flavors of active learning based on query by committee.
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