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The new methodological and technological developments of the last decade make it possible to resolve or, at least, attenuate the psychometric problems of forced-choice (FC) tests for the measurement of personality. In these tests, the person being tested is shown blocks of two or more sentences of similar social desirability, from which he or she must choose which one best represents him or her. Thus, FC tests aim to reduce response bias in self-report questionnaires. However, their use is not without risks and complications if they are not created properly. Fortunately, new psychometric models make it possible to model responses in this type of test and to optimize their construction. Moreover, they allow the construction of “on the fly” computerized adaptive FC tests (CAT-FC), in which each item is constructed on the spot, optimally matching sentences from a previously calibrated bank.

Exploratory bi-factor analysis (EBFA) is a very popular approach to estimate models where specific factors are concomitant to a single, general dimension. However, the models typically encountered in fields like personality, intelligence, and psychopathology involve more than one general factor. To address this circumstance, we developed an algorithm (GSLiD) based on partially specified targets to perform exploratory bi-factor analysis with multiple general factors (EBFA-MGF). In EBFA-MGF, researchers do not need to conduct independent bi-factor analyses anymore because several bi-factor models are estimated simultaneously in an exploratory manner, guarding against biased estimates and model misspecification errors due to unexpected cross-loadings and factor correlations. The results from an exhaustive Monte Carlo simulation manipulating nine variables of interest suggested that GSLiD outperforms the Schmid-Leiman approximation and is robust to challenging conditions involving cross-loadings and pure items of the general factors. Thereby, we supply an R package (bifactor) to make EBFA-MGF readily available for substantive research. Finally, we use GSLiD to assess the hierarchical structure of a reduced version of the Personality Inventory for DSM-5 Short Form (PID-5-SF).

Recent arguments claim that behavioral science has focused - to its detriment - on the individual over the system when construing behavioral interventions. In this commentary, we argue that tackling economic inequality using both framings in tandem is invaluable. By studying individuals who have overcome inequality, "positive deviants," and the system limitations they navigate, we offer potentially greater policy solutions.

Public toilets are a critical public health issue with a significant negative impact on people's lives. Unfortunately, the effect of negative experiences caused by public toilets on people's quality of life and life satisfaction is unknown. In this study, participants (n = 550) were asked to fill in a scale-based survey about their negative experiences with public toilets, quality of life, and life satisfaction. We found that people with toilet-dependent illnesses (36% of the sample) reported more negative experiences with public toilets than their counterparts. These negative experiences are also related to lower scores in some areas of participants' quality of life, such as their environmental, psychological, and physical health and life satisfaction, even after controlling for relevant socio-economic variables. Additionally, toilet-dependent individuals had particularly negative experiences in terms of life satisfaction and physical health than non-toilet-dependent people. We conclude that the impoverishment of quality of life linked to public toilets as an environmental inadequacy is traceable, estimable, and meaningful. This association is not only negative for ordinary people, but it is significantly negative for people with toilet-dependent illnesses. These results highlight that public toilets are essential to ensure collective well-being, particularly when considering those affected by their presence or lack thereof., (© 2023 Scandinavian Psychological Associations and John Wiley & Sons Ltd.)

The accuracy of factor retention methods for structures with one or more general factors, like the ones typically encountered in fields like intelligence, personality, and psychopathology, has often been overlooked in dimensionality research. To address this issue, we compared the performance of several factor retention methods in this context, including a network psychometrics approach developed in this study. For estimating the number of group factors, these methods were the Kaiser criterion, empirical Kaiser criterion, parallel analysis with principal components (PA PCA ) or principal axis, and exploratory graph analysis with Louvain clustering (EGA LV ). We then estimated the number of general factors using the factor scores of the first-order solution suggested by the best two methods, yielding a "second-order" version of PA PCA (PAP CA-FS ) and EGA LV (EGA LV-FS ). Additionally, we examined the direct multilevel solution provided by EGA LV . All the methods were evaluated in an extensive simulation manipulating nine variables of interest, including population error. The results indicated that EGA LV and PA PCA displayed the best overall performance in retrieving the true number of group factors, the former being more sensitive to high cross-loadings, and the latter to weak group factors and small samples. Regarding the estimation of the number of general factors, both PAP CA-FS and EGA LV-FS showed a close to perfect accuracy across all the conditions, while EGA LV was inaccurate. The methods based on EGA were robust to the conditions most likely to be encountered in practice. Therefore, we highlight the particular usefulness of EGA LV (group factors) and EGA LV-FS (general factors) for assessing bifactor structures with multiple general factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

While economic inequality continues to rise within countries, efforts to address it have been largely ineffective, particularly those involving behavioral approaches. It is often implied but not tested that choice patterns among low-income individuals may be a factor impeding behavioral interventions aimed at improving upward economic mobility. To test this, we assessed rates of ten cognitive biases across nearly 5000 participants from 27 countries. Our analyses were primarily focused on 1458 individuals that were either low-income adults or individuals who grew up in disadvantaged households but had above-average financial well-being as adults, known as positive deviants. Using discrete and complex models, we find evidence of no differences within or between groups or countries. We therefore conclude that choices impeded by cognitive biases alone cannot explain why some individuals do not experience upward economic mobility. Policies must combine both behavioral and structural interventions to improve financial well-being across populations., (© 2023. The Author(s).)

The use of Open Science practices is often proposed as a way to improve research practice, especially in psychology. Open Science can increase transparency and therefore reduce questionable research practices, making research more accessible to students, scholars, policy makers, and the public. However, little is known about how widespread Open Science practices are taught and how students are educated about these practices. In addition, it remains unknown how informing students about Open Science actually impacts their understanding and adoption of such practices. This registered report proposes the validation of a questionnaire. The aim is to survey how much psychology students know about Open Science and to assess whether knowledge of and exposure to Open Science in general-be it through university curricula or social media-influences attitudes towards the concept and intentions to implement relevant practices., Competing Interests: The authors have declared that no competing interests exist.

Amidst a worldwide vaccination campaign, trust in science plays a significant role when addressing the COVID-19 pandemic. Given current concerns regarding research standards, we were interested in how Spanish scholars perceived COVID-19 research and the extent to which questionable research practices and potentially problematic academic incentives are commonplace. We asked researchers to evaluate the expected quality of their COVID-19 projects and other peers' research and compared these assessments with those from scholars not involved in COVID-19 research. We investigated self-admitting and estimated rates of questionable research practices and attitudes towards current research status. Responses from 131 researchers suggested that COVID-19 evaluations followed partisan lines, with scholars being more pessimistic about others' colleagues' research than their own. Additionally,researchers not involved in COVID-19 projects were more negative than their participating peers. These differences were particularly notable for areas such as the expected theoretical foundations or overall quality of the research, among others. Most Spanish scholars expected questionable research practices and inadequate incentives to be widespread. In these two aspects, researchers tended to agree regardless of their involvement in COVID-19 research. We provide specific recommendations for improving future meta-science studies, such as redefining QRPs as inadequate research practices (IRP). This change could help avoid key controversies regarding QRPs' definition while highlighting their detrimental impact. Lastly, we join previous calls to improve transparency and academic career incentives as a cornerstone for generating trust in science., Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-022-02797-6., Competing Interests: Conflicts of InterestThe authors declare no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Pervading global narratives suggest that political polarization is increasing, yet the accuracy of such group meta-perceptions has been drawn into question. A recent US study suggests that these beliefs are inaccurate and drive polarized beliefs about out-groups. However, it also found that informing people of inaccuracies reduces those negative beliefs. In this work, we explore whether these results generalize to other countries. To achieve this, we replicate two of the original experiments with 10,207 participants across 26 countries. We focus on local group divisions, which we refer to as fault lines. We find broad generalizability for both inaccurate meta-perceptions and reduced negative motive attribution through a simple disclosure intervention. We conclude that inaccurate and negative group meta-perceptions are exhibited in myriad contexts and that informing individuals of their misperceptions can yield positive benefits for intergroup relations. Such generalizability highlights a robust phenomenon with implications for political discourse worldwide., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

As general factor modeling continues to grow in popularity, researchers have become interested in assessing how reliable general factor scores are. Even though omega hierarchical estimation has been suggested as a useful tool in this context, little is known about how to approximate it using modern bi-factor exploratory factor analysis methods. This study is the first to compare how omega hierarchical estimates were recovered by six alternative algorithms: Bi-quartimin, bi-geomin, Schmid-Leiman (SL), empirical iterative empirical target rotation based on an initial SL solution (SLiD), direct SL (DSL), and direct bi-factor (DBF). The algorithms were tested in three Monte-Carlo simulations including bi-factor and second-order structures and presenting complexities such as cross-loadings or pure indicators of the general factor and structures without a general factor. Results showed that SLiD provided the best approximation to omega hierarchical under most conditions. Overall, neither SL, bi-quartimin, nor bi-geomin produced an overall satisfactory recovery of omega hierarchical. Lastly, the performance of DSL and DBF depended upon the average discrepancy between the loadings of the general and the group factors. The re-analysis of eight classical datasets further illustrated how algorithm selection could influence judgments regarding omega hierarchical.

Traffic signs are an integral part of the traffic control plan and they provide road users with necessary information on the upcoming situation. This paper aims to examine the level of understanding of traffic sign imagery used in different countries and to track participants' eye movement when they encounter unfamiliar signs. Tobii eye tracking glasses were used to track gaze differences between familiar and unfamiliar traffic signs. Our findings show that sign characteristics (such as the amount of information on the sign) and the observer's knowledge of the sign meaning have a significant impact on eye behaviour. Signs containing more information (loaded with more content) and unfamiliar to the participant systematically produced the longest overall and average fixations and gazing duration. Given that longer gaze time for unfamiliar traffic signs presents a potential traffic hazard, we evaluated the need for standardization of traffic signs., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Public bathrooms are sensible locations in which individuals confront an intimate environment outside the comfort of their own home. The assessment of public bathrooms is especially problematic for people whose illnesses make them more prone to needing this service. Unfortunately, there is a lack in the evaluation of the elements that are relevant to the user's perspective. For that reason, we propose a new scale to assess these elements of evaluation of public bathrooms. We developed a scale of 14 items and three domains: privacy, ease of use and cleanliness. We tested the factor validity of this three-factor solution ( n = 654) on a sample of healthy individuals and 155 respondents with a bowel illness or other affection that reported to be bathroom-dependent. We found that bathroom-dependent people value more privacy and cleanliness more than their healthy counterparts. We additionally found a gender effect on the scale: female participants scored higher in every domain. This study provides the first scale to assess value concerning public bathrooms and to highlight the relevance of different bathrooms' aspects to users.

Background: An increasing number of international organisations and national governments have committed to well-being promotion. Unfortunately, important questions regarding how to assess well-being are still unresolved, making policy implementation and evaluation difficult., Methods: This research expanded on Huppert and So's (Soc Indic Res. 110, 837-861 2013) multidimensional subjective well-being framework by investigating the replicability of the model in two non-European regions (South America, represented by Brazil and Colombia, and Eastern Africa, represented by Uganda), and the United Kingdom. Additionally, previous limitations of the framework were also addressed. ESS Round Six items were crucially improved in terms of temporal and response scale consistency. Bayesian approximate measurement invariance was applied on a sample of 381 young adult participants to test for consistency across countries., Results: The Huppert & So (Soc Indic Res. 110, 837-861 2013) model was found to fairly replicate across non-European regions, where meaningful differences in well-being patterns across regions were observed. Additionally, estimated well-being was related with other well-being measures (Five Ways): Learn and Connect were the strongest predictors of general well-being, with Take Notice and Give being associated with specific aspects of it., Conclusions: Based on this narrow sample of young adults, it appears the ten-item measure proposed by Huppert & So (Soc Indic Res. 110, 837-861 2013) is useful for assessing population mental health outside of Europe. This is only an initial attempt to assess qualities, so further testing should be done before applying at scale for identifying policy opportunities to address well-being of populations.

Background: Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP)., Methods: To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB)., Results: The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions., Conclusions: We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

There has been increased interest in assessing the quality and usefulness of short versions of the Raven's Progressive Matrices. A recent proposal, composed of the last twelve matrices of the Standard Progressive Matrices (SPM-LS), has been depicted as a valid measure of g . Nonetheless, the results provided in the initial validation questioned the assumption of essential unidimensionality for SPM-LS scores. We tested this hypothesis through two different statistical techniques. Firstly, we applied exploratory graph analysis to assess SPM-LS dimensionality. Secondly, exploratory bi-factor modelling was employed to understand the extent that potential specific factors represent significant sources of variance after a general factor has been considered. Results evidenced that if modelled appropriately, SPM-LS scores are essentially unidimensional, and that constitute a reliable measure of g . However, an additional specific factor was systematically identified for the last six items of the test. The implications of such findings for future work on the SPM-LS are discussed., Competing Interests: “The authors declare no conflict of interest.”

Global medical travel has had an increasing trend without a comprehensive, evidence-driven policy to ensure safe and effective practice. To identify key factors that influence medical travel, we conducted a series of studies culminating with a preference and decision-making component of over 500 prospective medical travelers from a number of countries. Results indicated that quality of care was the most critical factor in the decision, followed by lower costs of procedure and shorter waiting times. Lower costs were less of a factor if the procedure was more invasive, which also increased the importance of waiting time in the decision. The most desired destinations for care were in Europe (United Kingdom, Germany) and North America (United States). Building on these insights and previous literature, we present a model that implements applications from these factors and additional insights generated across the series of studies toward an effective policy framework., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target rotation (SLi), based on the iteration of partially specified target matrices and an initial target constructed from a Schmid-Leiman (SL) orthogonalization. SLi was expected to ameliorate some of the limitations of the previously presented SL bi-factor rotations, SL and SL with target rotation (SLt), when the factor structure either includes cross-loadings, near-zero loadings, or both. A Monte Carlo simulation was carried out to test the performance of SLi, SL, SLt, and the two analytic bi-factor rotations, bi-quartimin and bi-geomin. The results revealed that SLi accurately recovered the bi-factor structures across the majority of the conditions, and generally outperformed the other rotation methods. SLi provided the biggest improvements over SL and SLt when the bi-factor structures contained cross-loadings and pure indicators of the general factor. Additionally, SLi was superior to bi-quartimin and bi-geomin, which performed inconsistently across the types of factor structures evaluated. No method produced a good recovery of the bi-factor structures when small samples (N = 200) were combined with low factor loadings (0.30-0.50) in the specific factors. Thus, it is recommended that larger samples of at least 500 observations be obtained.

Medical travel has expanded rapidly in recent years, resulting in new markets and increased access to medical care. Whereas several studies investigated the motives of individuals seeking healthcare abroad, the conventional analytical approach is limited by substantial caveats. Classical techniques as found in the literature cannot provide sufficient insight due to the nested nature of data generated. The application of adequate analytical techniques, specifically multilevel modeling, is scarce to non-existent in the context of medical travel. This study introduces the guidelines for application of multilevel techniques in public health research by presenting an application of multilevel modeling in analyzing the decision-making patterns of potential medical travelers. Benefits and potential limitations are discussed.

Background: Increased access to transportation and information has led to the emergence of more diverse patient choice and new forms of health care consumption, such as medical travel. In order for health care providers to effectively attract patients, more knowledge is needed on the mechanisms underlying decision-making of potential travelers from different countries. A particularly promising method of studying the travelers' motives is collecting data on social media., Objectives: The aim of this study was to test what factors influence decision-making of potential medical travelers and how these factors interact. Based on existing literature, the factors analyzed included quality, cost, and waiting time for 2 procedures varying in invasiveness across 12 different destination countries., Methods: Decision-making patterns were examined using a pilot questionnaire that generated a large amount of data from over 800 participants in 40 countries. Participants indicated their willingness to travel given different scenarios. Each scenario consisted of a combination of several factors. Additionally, participants were asked to indicate the reasons for their choice., Results: Individuals display high willingness to travel for medical care when combining all participants and scenarios, travel for care was chosen 66.9% of the time. Among the factors influencing their decisions, quality of the medical procedure abroad was considered most important, and cost was least important as shown by chi-square tests and corresponding odds ratios. Log-linear analyses revealed an interaction between time waiting in the local health care system and type of procedure, whereby time pressure increased the odds of agreeing to travel for the more invasive procedure. The odds of traveling to Europe and the USA were by far the highest, although participants indicated that under certain conditions they might be willing to travel to other medical destinations, such as Asia., Conclusion: Our measurements yielded several reliable insights into the factors driving medical decision-making. An essential next step would be to expand these findings with a more encompassing sample and more elaborate statistical modeling.