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With the recent upsurge of populism in developed and transition democracies, researchers have started measuring it as an attitude. Several scales have been proposed for this purpose. However, there is little direct comparison between the available alternatives. Scholars who wish to measure populist attitudes have little information available to help select the best scale for their purposes. In this article, we directly compare seven populist attitudes scales from multiple perspectives: conceptual development, questionnaire design, dimensionality, information, cross-national validity, and external validity. We use original survey data collected online from nine countries in Europe and the Americas, with around 250 participants per country, in which all seven batteries of questions were present. Results show that most scales have important methodological and validity limitations in at least one of the dimensions tested, and should not be used for cross-national comparative research. We recommend populist attitudes items that work better at capturing populism, and more generally provide guidelines for researchers who want to compare different scales that supposedly measure the same construct.

Understanding when and why White racial attitudes change is important for understanding their politics. Critically, surveys reveal Whites’ views of Black Americans have been changing recently, an important result given conventional wisdom that these are stable orientations. I test four possible explanations for these shifting views: genuine attitude change, social desirability, partisan expressive responding, and changing racial attitude measure performance. Importantly, these explanations produce observationally equivalent survey toplines. To adjudicate between them, I use the measurement equivalence framework and examine how Whites answer the racial resentment measure. Evidence from multigroup confirmatory factor analysis models supports genuine attitude change. Substantively, this suggests these changes may have important political implications. Methodologically, it suggests partisan expressive responding may have limits, indicates social desirability pressures have not changed how Whites answer at least one racial attitude measure, and offers additional validity evidence for the racial resentment measure.

People with autism spectrum disorder (ASD) experience stigmatization rooted in negative attitudes or prejudice toward them due to social awkwardness. However, little is known about implicit attitudes toward ASD, especially differences in attitudes compared to those of more visible conditions; physical disabilities. In this study, we implemented implicit association tests (IATs) to assess implicit attitudes. Sixty-three university students participated in IATs and answered questionnaires that measured explicit attitudes, social desirability, knowledge about—and familiarity with—disorders. The results demonstrated that implicit attitude toward ASD was significantly less negative than toward physical disabilities. Regarding the discrepancy, not socially awkward behavior but appearance of people with ASD can be evaluated as ‘in-group’ members and lead to less negative attitude compared with physical disabilities.

We compared the psychometric qualities of seven indirect attitude measures across three attitude domains (race, politics, and self-esteem) with a large sample ( N  = 23,413). We compared the measures on internal consistency, sensitivity to known effects, relationships with indirect and direct measures of the same topic, the reliability and validity of single-category attitude measurement, their ability to detect meaningful variance among people with nonextreme attitudes, and their robustness to the exclusion of misbehaving or well-behaving participants. All seven indirect measures correlated with each other and with direct measures of the same topic. These relations were always weak for self-esteem, moderate for race, and strong for politics. This pattern suggests that some of the sources of variation in the reliability and predictive validity of the indirect measures is a function of the concepts rather than the methods. The Implicit Association Test (IAT) and Brief IAT (BIAT) showed the best overall psychometric quality, followed by the Go–No-Go association task, Single-Target IAT (ST-IAT), Affective Misattribution Procedure (AMP), Sorting Paired Features task, and Evaluative Priming. The AMP showed a steep decline in its psychometric qualities when people with extreme attitude scores were removed. Single-category attitude scores computed for the IAT and BIAT showed good relationships with other attitude measures but no evidence of discriminant validity between paired categories. The other measures, especially the AMP and ST-IAT, showed better evidence for discriminant validity. These results inform us on the validity of the measures as attitude assessments, but do not speak to the implicitness of the measured constructs.

A growing body of research in sociology uses the concept of cultural schemas to explain how culture influences beliefs and actions. However, this work often relies on belief or attitude measures gleaned from survey data as indicators of schemas, failing to measure the cognitive associations that constitute schemas. In this article, we propose a concept-association-based approach for collecting data about individuals’ schematic associations, and a corresponding method for modeling concept network representations of shared cultural schemas. We use this method to examine differences between liberal and conservative schemas of poverty in the United States, uncovering patterns of associations expected based on previous research. Examining the structure of schematic associations provides novel insights to long-standing empirical questions regarding partisan attitudes toward poverty. Our method yields a clearer picture of what poverty means for liberals and conservatives, revealing how different concepts related to poverty indeed mean fundamentally different things for these two groups. Finally, we show that differences in schema structure are predictive of individuals’ policy preferences.

Background The lack of artificial intelligence applications in nursing education and the nursing profession in Turkey and the need for strategies for integrating artificial intelligence into the nursing profession continues. At this point, there is a need to transform the negative attitudes and anxiety that may occur in nurses. Objectives It was aimed to reorganize the professional transformation in this parallel by analyzing the effect of digital leadership perception, which is explained as how nurses approach digital technologies and innovations and their awareness of how and with which methods they can use these technologies on artificial intelligence anxiety and attitude in the nursing profession. Design The study was designed as descriptive, correlational, and cross‐sectional. Participants The research was conducted by reaching 439 nurses working in hospitals operating in three different regions of Turkey by simple random sampling method. Methods In the first part of the data collection tool used in this study, digital leadership scale, artificial intelligence use anxiety, and artificial intelligence attitude scales were used, including questions determining the demographic information of nurses, their relationship with technology, artificial intelligence usage status and its importance in the profession. Results It was determined that 29.8% of the nurses had a good relationship with technology, 66.3% knew about using artificial intelligence in health, and 27.3% wanted it to be more involved in their lives. It was determined that nurses' perceptions of digital leadership were at a medium level of 46.9% and a high level of 41.7%, 82.7% had a positive attitude towards artificial intelligence, and 82.7% had low or medium level anxiety when their artificial intelligence anxiety status was examined. There was a significant and negative relationship between digital leadership and AI anxiety (r = −0.434; p < 0.01), a significant and positive relationship between digital leadership and AI attitude (r = 0.468; p < 0.01), and a significant and negative relationship between AI attitude and AI anxiety (r = −0.629; p < 0.01). Finally, it was determined that nurses' perception of digital leadership indirectly affected AI anxiety through AI attitude (β = −0.230, 95% CI [−0.298, −0.165]). Conclusion It is suggested that the anxiety and attitude towards artificial intelligence can be transformed positively with the effect of digital leadership, and in this parallel, the digital leadership phenomenon should be evaluated as a practical implementation strategy in integrating artificial intelligence into the nursing profession. Clinical Relevance Our study showed that artificial intelligence attitude has a mediating role in the indirect effect of the perception of digital leadership in nursing on AI anxiety. It was determined that nurses' digital leadership perception, artificial intelligence anxiety, and artificial intelligence attitude differed significantly with demographic variables.

Although past research suggests authoritarianism may be a uniquely right-wing phenomenon, the present two studies tested the hypothesis that authoritarianism exists in both right-wing and left-wing contexts in essentially equal degrees. Across two studies, university (n = 475) and Mechanical Turk (n = 298) participants completed either the RWA (right-wing authoritarianism) scale or a newly developed (and parallel) LWA (left-wing authoritarianism) scale. Participants further completed measurements of ideology and three domain-specific scales: prejudice, dogmatism, and attitude strength. Findings from both studies lend support to an authoritarianism symmetry hypothesis: Significant positive correlations emerged between LWA and measurements of liberalism, prejudice, dogmatism, and attitude strength. These results largely paralleled those correlating RWA with identical conservative-focused measurements, and an overall effect-size measurement showed LWA was similarly related to those constructs (compared to RWA) in both Study 1 and Study 2. Taken together, these studies provide evidence that LWA may be a viable construct in ordinary U.S. samples.

In our society, persons with disabilities are often subject to stereotyping and they may give rise to feelings of fear and rejection. These reactions refer to attitudes, that is, the more or less positive evaluations of an object (Eagly & Chaiken, 1993). Scientific literature has been greatly interested in attitudes—toward persons with disabilities (Breen, 2018; Palad et al., 2016) and the education of students with disabilities (Desombre et al., 2019; Donath et al., 2023)—and the extent to which those attitudes are likely to predict inclusive behaviours (Ajzen, 1991). This purpose of this article is to take stock of the tools available to measure explicit attitudes toward persons with disabilities in various contexts. This systematic literature review was carried out using the Prisma method and the databases of APA PsycInfo, APA PsycArticles and the Psychology and Behavioral Sciences Collection; it groups together articles published in French and in English between January 2000 and June 2021. In total, 77 articles out of 837 were selected for review. The analysis of these articles allowed us to identify 22 tools to measure attitudes toward persons with disabilities. Comparisons between these tools are their uses are discussed in this article. (PsycInfo Database Record (c) 2025 APA, all rights reserved) (Source: journal abstract)

The external validity of causal findings is a focus of long-standing debates in the social sciences. Although the issue has been extensively studied at the conceptual level, in practice few empirical studies include an explicit analysis that is directed toward externally valid inferences. In this article, we make three contributions to improve empirical approaches for external validity. First, we propose a formal framework that encompasses four dimensions of external validity: $ X $ -, $ T $ -, $ Y $ -, and C-validity (populations, treatments, outcomes, and contexts). The proposed framework synthesizes diverse external validity concerns. We then distinguish two goals of generalization. To conduct effect-generalization—generalizing the magnitude of causal effects—we introduce three estimators of the target population causal effects. For sign-generalization—generalizing the direction of causal effects—we propose a novel multiple-testing procedure under weaker assumptions. We illustrate our methods through field, survey, and lab experiments as well as observational studies.

Artificial intelligence (AI) education is becoming increasingly important worldwide. However, there has been no measuring instrument for diagnosing the students’ current perspective. Thus the aim of this study was to develop an instrument that measures student attitudes toward AI. The instrument was developed by verifying the reliability and validity by 8 computer education PhD using a sample of 305 K-12 students. This scale made students’ attitudes toward AI operational and quantifiable. Accordingly, educators can use it to diagnose the current status of students or verify the effectiveness of new AI education methods.