Popular Measures Research Articles

Diversification Quotients: Quantifying Diversification via Risk Measures

We establish the first axiomatic theory for diversification indices using six intuitive axioms: nonnegativity, location invariance, scale invariance, rationality, normalization, and continuity. The unique class of indices satisfying these axioms, called the diversification quotients (DQs), are defined based on a parametric family of risk measures. A further axiom of portfolio convexity pins down DQs based on coherent risk measures. The DQ has many attractive properties, and it can address several theoretical and practical limitations of existing indices. In particular, for the popular risk measures value at risk and expected shortfall, the corresponding DQ admits simple formulas, and it is efficient to optimize in portfolio selection. Moreover, it can properly capture tail heaviness and common shocks, which are neglected by traditional diversification indices. When illustrated with financial data, the DQ is intuitive to interpret, and its performance is competitive against other diversification indices. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: X. Han is supported by the National Natural Science Foundation of China [Grants 12301604, 12371471, and 12471449). L. Lin is supported by the Hickman Scholarship from the Society of Actuaries. R. Wang is supported by the Natural Sciences and Engineering Research Council of Canada [Grants CRC-2022-00141 and RGPIN-2024-03728] and the Sun Life Research Fellowship. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00513 .

How you ask matters: evidence-based assessment connecting decentering, reappraisal, and self-reported wellbeing in a post-secondary sample

Effective and efficient Wellbeing measurement is essential within the social sciences and public health. Wellbeing is described as a three-factor construct composed of Life Satisfaction, Positive Affect, and Negative Affect, yet there are few measurement models validated for the increasingly popular use of longitudinal, app-based assessment. We explored Wellbeing measurement in a postsecondary student sample, including two mechanistic indicators described in Mindfulness-to-Meaning Theory: Decentering and Positive Reappraisal. Across two studies, we compared and validated popular measurement models for each construct. The most parsimonious Wellbeing model indicated only a two-factor structure comprised of positive (e.g., happiness, life satisfaction, and flourishing) and negative dimensions (e.g., anger, sadness, and anxiety). A third study revealed that a three-factor structure for Wellbeing was only supported when sampling a greater diversity of positive emotions than the earlier studies. Furthermore, while the Mindfulness-to-Meaning pathway to Wellbeing was replicated, only some operationalizations of Decentering and Reappraisal accounted for variance in Wellbeing. Concrete recommendations for the longitudinal assessment are provided. This research contributes not only to our understanding of Wellbeing, but also informs its optimal assessment in longitudinal research such as clinical trials and experience sampling studies.

Reliability generalization meta-analysis of the internal consistency of the Big Five Inventory (BFI) by comparing BFI (44 items) and BFI-2 (60 items) versions controlling for age, sex, language factors

IntroductionThe Big Five Inventory (BFI) is a popular measure that evaluates personality on the Big-Five model. Apart from its utilization across cultures, the literature did not reveal any meta-analysis for the reliability of the different versions of the BFI and its translations. The current study carried out a reliability generalization meta-analysis (REGEMA) to establish the reliability of the BFI across cultures and languages.MethodsWe searched 30 databases for the relevant studies from 1991 to mid-November 2024. The studies that we intended to include in our meta-analysis required to have utilized the BFI (44 items) and the BFI-2 (60 items) and have reported Cronbach's alpha or McDonald's omega reliability estimates. Our coded variables included BFI version, sample size, population type, age, gender, clinical state, and reliability. A total of 57 studies (datapoints) published in 34 research articles (involving 43,715 participants; 60.24% women; Mean age = 30.08) from various cultures and languages were finally included. These studies used BFI and BFI-2 in Arabic, Chinese, Croatian, Czech, Danish, Dutch, English, French, German, Indonesian, Italian, Japanese, Malay, Norwegian, Polish, Portuguese, Russian, Serbian, Spanish, Swahili, and Turkish. Data analysis was conducted using the metafor and meta packages in R. The average correlation was computed using a random-effects model and reliability coefficients indicated effect size. I2 and Cochran's Q tests were used to examine heterogeneity, with prediction intervals suggesting genuine influences around the pooled estimate. Using funnel plots, regression-based tests (e.g., Egger's regression, rank correlation), and trim-and-fill imputation, publication bias was adjusted to estimate unbiased effects.ResultsWe calculated the individual and combined reliability of the BFI and BFI-2 across languages and cultures. The results revealed the reliability of all five factors used in the BFI/BFI-2. The BFI estimates provide the following results: openness is estimated at 0.77 (95% CI: 0.75; 0.80); conscientiousness is estimated at 0.80 (95% CI: 0.78; 0.82); extraversion is also estimated at 0.80 (95% CI: 0.79; 0.82); agreeableness is estimated at 0.73 (95% CI: 0.71; 0.76); and neuroticism is estimated at 0.80 (95% CI: 0.79; 0.82). The BFI-2 estimates are as follows: openness is estimated at 0.83 (95% CI: 0.82; 0.84); conscientiousness is estimated at 0.86 (95% CI: 0.85; 0.87); extraversion is estimated at 0.85 (95% CI: 0.84; 0.86); agreeableness is also estimated at 0.80 (95% CI: 0.79; 81); and neuroticism is estimated at 0.89 (95% CI: 0.88; 0.89).ConclusionThe current meta-analysis represents the first reliability analysis of the BFI and the first comparison between its two different versions, the BFI (44 items) and the BFI-2 (60 items). The generalized reliability of both the BFI and BFI-2 were established. The findings confirm that the BFI and BFI-2 have good reliability across all five factors.

Examining the effect of personality on user acceptance of conditionally automated vehicles

Automated vehicle acceptance (AVA) research has grown substantially in the past few years. There is a paucity of research on the role of the big five personality traits on attitudes towards automated vehicles (AVs) and AVA. This is a critical shortcoming given that personality is considered a critical factor explaining technology adoption. Our major theoretical contribution is the integration of the most popular personality measure – the big five – and one of the most influential technology acceptance models – Unified Theory of Acceptance and Use of Technology (UTAUT2). A questionnaire was administered to 9,339 respondents from nine countries to predict the behavioral intention to use conditionally automated vehicles (CondAVs). The original UTAUT2 was extended by trust and driver engagement and the big five personality traits openness, conscientiousness, extraversion, agreeableness, and neuroticism. Structural equation modeling was applied to examine the direct effects of these constructs on behavioral intention and the indirect effects of the personality traits on the independent constructs of the extended UTAUT2. The results have shown positive effects of social influence, trust, and performance expectancy on the behavioral intention to use CondAVs. Most of the hypotheses pertaining to the role of the personality traits on the UTAUT2 constructs were supported, but the effects were relatively small (< 0.25). Our findings support the usefulness of UTAUT2 in evaluating the success of AVs, providing crucial insights into the factors driving the acceptance of CondAVs. The cross-country analysis provides further insights into the role of an individual’s personality for AVA. Our study yields important implications for practitioners. Given the small effect sizes of personality, designing CondAVs around the personalities of their customers during development and commercialization may be ineffective to promote trust and acceptance.

Wi-Fi signal for soil moisture sensing

Measuring soil moisture is essential in various scientific and engineering disciplines. Over recent decades, numerous technologies have been employed for in situ monitoring of soil moisture. Currently, dielectric-based sensors are the most popular measurement technology and provide acceptable accuracy for various measurement purposes. However, these sensors are relatively expensive, and alternative technologies, which are cheaper, are not accurate enough for scientific purposes. Recently, the idea of using a Wi-Fi signal to measure soil moisture has been presented. Theoretically, the use of Wi-Fi technology in soil sensing follows the same concepts as the previous dielectric sensors. The main advantage of Wi-Fi technology is the possibility of providing a relatively accurate and cost-effective solution for soil moisture measurement. In this work, we try to investigate the possibility of using Wi-Fi signal characteristics for soil sensing. Therefore, a series of small-scale laboratory and field experiments were conducted to test the concept. The results of these experiments were promising, showing strong linear relationships between Wi-Fi signal properties (received signal strength indicator, RSSI) and soil water content, with R2 values ranging between 0.92 and 0.99, indicating a strong correlation. They also illustrate the possibility of using this technology to develop an inexpensive and accurate device for measuring soil moisture. However, observations from the experiments also point to problematic factors involving the hardware and software used in the measurements. It is important to control these factors in the next steps to develop a reliable measurement device.

Instant payments and brazilian pix: lessons from the indian experience in the 2010’s

Instant payments are part of an ecosystem of financial innovations, promoting quick and easy access to funds. Through Nonlinear Distributed Lag Autoregressive Models (NARDL), this article aims to draw lessons from the Indian experience with the Unified Payments Interface (UPI), for the Brazilian Pix. The empirical results show that the substitutes for instant payments (credit and debit cards) have a complementary character, while the measure of relative popularity of mobile banking (MB/WLESS) and degree of sophistication of the financial system (M1/GDP) has immediate effects on these flows. Short-term negative asymmetric shocks had greater impacts on instant payment systems.

Unsupervised machine learning in financial anomaly detection: clustering algorithms vs. dedicated methods

The article presents the application of selected clustering algorithms for detecting anomalies in financial data compared to several dedicated algorithms for this problem. To apply clustering algorithms for anomaly detection, the Determine Abnormal Clusters Algorithm (DACA) was developed and implemented. This parameterized script (DACA) allows clusters containing anomalies to be automatically detected on the basis of defined distance measures. This kind of operation allows clustering algorithms to be quickly and efficiently adapted to anomaly detection. The prepared test environment has allowed for the comparison of selected clustering algorithms. K-Means, Hierarchical Cluster Analysis, K-Medoids, and anomaly detection: Stochastic Outlier Selection, Isolation Forest, Elliptic Envelope. The research has been carried out on real financial data, in particular on the income declared in the asset declarations of the targeted professional group. The experience of financial experts has been used to assess anomalies. Furthermore, the results have been evaluated according to a number of popular classification and clustering measures. The highest result for the investigated financial problem was provided by the K-Medoids algorithm in combination with the DACA script. It is worthwhile to conduct future research on the introduced solutions as an ensemble method.

Validation and cutoff score for the single-item sleep quality scale.

The Sleep Quality Scale (SQS) is a popular single-item measure of sleep quality that has been used in research and clinical settings. The aim of the current paper is to validate the SQS and provide a cutoff score. We conducted two studies to validate the SQS and provide a cutoff score to simplify the interpretation of SQS scores. We collected data from 747 physicians and nurses working in Oman. In Study 1, conducted during the first two weeks of April 2020, a significant positive relationship was observed between the SQS and The World Health Organization Wellbeing Index (r. = 0.90) and a significant negative relationship was found with the Generalized Anxiety Disorder scale (r = .88) and the Intolerance of Uncertainty Scale-12 (r = .88). In Study 2, conducted between October 12 and November 19, 2020, we proposed a cutoff score for the SQS using the Insomnia Severity Index cutoff score of ≥ 10. The estimated area under the receiver operating characteristics curve was 0.881 (95% CI = 0.84-0.92). A cutoff score of ≤ 6 was identified with a sensitivity of 87.50 (95% CI = 0.78-0.94), and a specificity of 74.10 (95% CI = 0.67-0.81). The findings of this study suggest that the single-item SQS is valid and an acceptable substitute for longer scales for initial assessment of sleep quality. The suggested cutoff value should help in easily classifying individuals into poor sleepers and good sleepers.

Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods.

Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among people worldwide. Thus, investigating methods of measuring and mitigating bias within these AI-powered tools is necessary. In natural language processing applications, the word embedding association test (WEAT) is a popular method of measuring bias in input embeddings, a common area of measure bias in AI. However, certain limitations of the WEAT have been identified (ie, their nonrobust measure of bias and their reliance on predefined and limited groups of words or sentences), which may lead to inadequate measurements and evaluations of bias. Thus, this study takes a new approach at modifying this popular measure of bias, with a focus on making it more robust and applicable in other domains. In this study, we introduce the SD-WEAT, which is a modified version of the WEAT that uses the SD of multiple permutations of the WEATs to calculate bias in input embeddings. With the SD-WEAT, we evaluated the biases and stability of several language embedding models, including Global Vectors for Word Representation (GloVe), Word2Vec, and bidirectional encoder representations from transformers (BERT). This method produces results comparable to those of the WEAT, with strong correlations between the methods' bias scores or effect sizes (r=0.786) and P values (r=0.776), while addressing some of its largest limitations. More specifically, the SD-WEAT is more accessible, as it removes the need to predefine attribute groups, and because the SD-WEAT measures bias over multiple runs rather than one, it reduces the impact of outliers and sample size. Furthermore, the SD-WEAT was found to be more consistent and reliable than its predecessor. Thus, the SD-WEAT shows promise for robustly measuring bias in the input embeddings fed to AI language models.

Quantile dependence: A generalization of upper and lower tail dependence

A popular measure of association is the tail dependence coefficient, which measures the strength of dependence in the tail of a bivariate distribution. In this paper, we introduce the concept of quantile dependence, which extends the idea of tail dependence and can be used to measure the dependence of specific quantiles of two random variables in a particular area of the distribution's domain. We analyze the characteristics of the proposed quantile dependence coefficient and provide various examples to demonstrate our findings.

Live research dialogue on the benefits, costs and utility of synthetic data for researchers

Objectives To explore use cases and characteristics of synthetic data that make it useful for research. To discuss governance frameworks essential for the routine creation, dissemination, and use of synthetic data. Drawing on the experience of international participants, explore measures to mitigate risks to data privacy and public perception relating to synthetic data creation and use whilst maximising its utility. ApproachPresenters provided context and insights on definitions and interpretations of synthetic data, including debates about fidelity and disclosure risk; known benefits of synthetic data and use cases; and its costs and challenges from the perspective of data owners, data providers and the public. This informed discussions in breakout groups aligned to the objectives. Participants self-identified as data service providers/processors, academic researchers, data owners, and/or ‘other’. Results Characteristics identified as important across a variety of use cases included accessibility, structure and format to match the real data, and documentation. The most popular measure to mitigate risks was clear and detailed documentation (metadata, codebooks, user guides, limitations, creation methods etc). Training was identified as an important benefit and use case of synthetic data across the participant groups. The most popular challenge identified by data service providers/processors and ‘other’ was lack of governance and standards; whilst for researchers, this was the verification of the synthetic data and the potential for it to be incorrectly interpreted. ConclusionsThere is a demand across stakeholder groups for synthetic data for a range of uses. Synthetic datasets should be accompanied by clear and detailed documentation and provided within an agreed governance framework.

Mapping common and distinct brain correlates among cognitive flexibility tasks: concordant evidence from meta-analyses.

Cognitive flexibility allows individuals to switch between different tasks, strategies, or ideas; an ability that is important for everyday life. The Wisconsin card sorting test (WCST) and task switching paradigm (TSP) are popular measures of cognitive flexibility. Although both tasks require switching, the TSP requires participants to memorize switching rules and retrieve them when they view a cue (rule-retrieval), whereas the classic WCST requires participants to discover the switching rule via trial-and-error (rule-discovery). Many functional magnetic resonance imaging studies have examined brain responses to these tasks. Extant meta-analyses show concordance in activation in a widespread set of areas including frontal, parietal, and cingulate cortices. Critically, past meta-analyses have not specifically examined brain correlates associated with rule derivation (i.e., rule-discovery vs. rule-retrieval) in cognitive flexibility tasks. We examine for the first time common and distinct concordance in brain responses to rule-discovery (i.e., WCST) and rule-retrieval (i.e., TSP), as well as TSP subtypes using quantitative meta-analyses. We analyzed data from 69 eligible articles with a total of 1617 young-adult participants. Conjunction results show concordance in common fronto-parietal areas predominantly in the left hemisphere. Contrast analyses show that rule-discovery required increased involvement in multiple cortical and subcortical regions such as frontopolar (Brodmann Area 10), parietal, insular cortex, thalamus and caudate nucleus predominantly in the right hemisphere. No significant differences in concordance were observed among the three, task switching paradigm sub-types. We propose a neuroanatomical model of cognitive flexibility and discuss theoretical and practical applications.

Research examining microcirculatory dynamics in diseases that cause oedema using skin blood flow and skin reperfusion pressure

Oedema is a build-up of fluid in the tissues in the body. It can develop due to diseases including heart failure and kidney disease. It usually occurs in the limbs, where it can cause discomfort, joint stiffness or pain. However, fluid can also accumulate in the brain, which is called cerebral oedema or in the blood vessels serving the lungs, called pulmonary oedema. A type of oedema called pitting oedema is more prevalent than the other type, non-pitting oedema. In order to treat the condition, the underlying cause must be resolved. Treatment can involve diuretics to help the release of fluids, positioning to encourage draining or compression to force fluids away. Researchers at the Faculty of Rehabilitation at Reiwa Health Sciences University led by Dr Haruki Kogo are examining the microcirculatory dynamics of diseases that cause oedema using skin blood flow and skin reperfusion pressure. They have developed a device that can diagnose oedema and confirmed the reproducibility and validity of the device. They have registered the trademark and plan to commercialise the device for use in clinical and research settings. The device is able to measure the depth of the surface imprint in pitting oedema patients, simplifying the evaluation of pitting oedema, which will be extremely useful in clinical settings. The researchers tested the device across two hospitals, comparing the results with results obtained from the popular circumference measurement method and ultrasound imaging measurement. The team found that there was a strong correlation between measurements from the device and the tissue thickness measurement derived from the ultrasound. This confirmed that the device is a reliable gauge for the measurement of oedema.

Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models.

Volatility in financial markets refers to the variation in asset prices over time. High volatility indicates increased risk, making its evaluation essential for effective risk management. Various methods are used to assess volatility, with the standard deviation of log-returns being a common approach. However, this implicitly assumes that log-returns follow a Gaussian distribution, which is not always valid. In this paper, we explore the use of (differential) entropy to evaluate the volatility of financial log-returns. Estimation of entropy is obtained using a Gaussian mixture model to approximate the underlying density of log-returns. Following this modeling approach, popular risk measures such as Value at Risk and Expected Shortfall can also be computed. By integrating Gaussian mixture modeling and entropy into the analysis of log-returns, we aim to provide a more accurate and robust framework for assessing financial volatility and risk measures.

A new Bayesian method for estimation of value at risk and conditional value at risk

AbstractValue at Risk (VaR) and Conditional Value at Risk (CVaR) have become the most popular measures of market risk in Financial and Insurance fields. However, the estimation of both risk measures is challenging, because it requires the knowledge of the tail of the distribution. Therefore, Extreme Value Theory initially seemed to be one of the best tools for this kind of problems, because using peaks-over-threshold method, we can assume the tail data approximately follow a Generalized Pareto distribution (GPD). The main objection to its use is that it only employs observations over the threshold, which are usually scarce. With the aim of improving the inference process, we propose a new Bayesian method that computes estimates built with all the information available. Informative prior Bayesian (IPB) method employs the existing relations between the parameters of the loss distribution and the parameters of the GPD that models the tail data to define informative priors in order to perform Metropolis–Hastings algorithm. We show how to apply IPB when the distribution of the observations is Exponential, stable or Gamma, to make inference and predictions. .Afterwards, we perform a thorough simulation study to compare the accuracy and precision of the estimates computed by IPB and the most employed methods to estimate VaR and CVaR. Results show that IPB provides the most accurate, precise and least biased estimates, especially when there are very few tail data. Finally, data from two real examples are analysed to show the practical application of the method.

An International Survey of the Diagnosis and Management of Ménière's Disease Amongst Otolaryngology Consultants.

Ménière's Disease (MD) is a disease that may be difficult to diagnose and manage. Our UK survey showed variability in the practice of UK Otolaryngology consultants. We hence surveyed Otolaryngology consultants internationally, to assess their confidence levels in diagnosing MD, their use of the AAO-HNS guidelines and current diagnostic and treatment modalities. An online questionnaire was distributed internationally over four weeks. The questionnaire asked respondents to anonymously rank their confidence in diagnosing MD, identify the minimum investigations required to make a diagnosis, describe their use of the AAO-HNS criteria, share their preferred treatment modalities for acute attacks, and state their 1st and 2nd-line preventative treatment options. A total of 173 responses were collected with 77% of respondents reporting high levels of confidence in diagnosing MD. Most respondents stated the minimum tests required were "History, Otoscopy, Clinical Vestibular testing, and Pure Tone Audiometry" although some chose as few as 1 test. Regarding the use of the AAO-HNS criteria, responses ranged from "always" (20.2%) to "never" (22.5%). Cinnarizine was the first-line treatment for acute attacks followed by betahistine. Betahistine (30.1%) and dietary restrictions (28.3%) were recommended almost equally as first-line preventative measures. The most popular second-line measure was intratympanic steroids injection (30.1%). Our survey revealed disparities in the diagnosis of MD and its management, like the results of our previously conducted UK survey. This suggests the need for an international consensus regarding the diagnosis and subsequent management strategies for this disease.

Statistical inference for Hicks–Moorsteen productivity indices

AbstractThe statistical framework for the Malmquist productivity index (MPI) is now well-developed and emphasizes the importance of developing such a framework for its alternatives. In this paper, we try to fill this gap in the literature for another popular measure, known as the Hicks–Moorsteen productivity index (HMPI). Unlike MPI, the HMPI has a total factor productivity interpretation in the sense of measuring productivity as the ratio of aggregated outputs to aggregated inputs and has other useful advantages over MPI. In this work, we develop a novel framework for statistical inference for HMPI in various contexts: when its components are known or when they are replaced with non-parametric envelopment estimators. This will be done for a particular firm’s HMPI as well as for the simple mean (unweighted) HMPI and the aggregate (weighted) HMPI. Our results further enrich the recent theoretical developments of nonparametric envelopment estimators for the various efficiency and productivity measures. We also examine the performance of these theoretical results for both the unweighted and weighted mean of HMPI for a finite sample, using Monte-Carlo simulations and also provide an empirical illustration along with the computation code.

Synthesis of a new quaternary ammonium salt for efficient inhibition of mild steel corrosion in 15 % HCl: Experimental and theoretical studies

The corrosion phenomenon and its economic impacts can hardly be ignored in any application. This study synthesized a quaternary ammonium salt (3) containing hydrophobic dodecyl and electron-rich diallylbenzyl amine moieties to be used in 15 % HCl as a corrosion inhibitor of mild steel. Several techniques, such as 1H and 13C NMR, IR, TGA, and elemental analysis, have been used to characterize inhibitor 3. Popular corrosion measurement techniques, namely weight loss, potentiodynamic polarization techniques, and electrochemical impedance spectroscopy, have been used to determine the efficiency of inhibitor 3. At 303 K and a moderately low concentration of 50 ppm, the quaternary ammonium salt-based inhibitor demonstrated a maximum efficiency of ≈95.0 %. At elevated temperatures of 313, 323, and 333 K, the inhibition efficacy values were recorded as 91.3, 82.8, and 75.0 %, respectively. Adsorption isotherm study revealed that the adsorption of inhibitor 3 followed Langmuir adsorption isotherm. The value of ΔGads° was found to be – 40.19 kJ mol−1, indicating that inhibitor 3 became adsorbed via a mixed physi-chemisorption mechanism. A very high adsorption constant (Kads) of 1.53 × 105 L mol−1 suggested strong adsorption of inhibitor 3. The difference in activation energy (Ea) value of 42.4 kJ mol−1 between the control and the inhibited solution indicated an efficient adsorption of inhibitor 3. The ability of inhibitor 3 to retard both anodic and cathodic half-cell reactions was proved via open circuit potential and Tafel curves studies. Detailed discussions on the change in corrosion current densities (icorr), polarization (Rp) and charge transfer (Rct) resistances have been offered to discuss the inhibition efficiency. Water contact angle measurement showed a drastic increase in mild steel surface hydrophobicity following inhibitor 3 adsorption. SEM-EDX and XPS studies confirmed the definitive presence of inhibitor 3 on the mild steel surface. Density functional theory (DFT) studies revealed the frontier molecular orbitals with which the metal surface interacts. A detailed corrosion inhibition mechanism has been offered in the context of adsorption isotherm and DFT studies.

Where is rural? An analysis of the agreement between quantitative measures of rurality

Government agencies and researchers often rely on quantitative measures to map where rural places are and who belongs to rural communities. To better understand the consistency, or lack thereof, across different quantitative rural measures, we compare four common measures applied at the county level for the contiguous United States. Our purpose was to (1) quantify and map the degree of rural agreement among these selected rural measures and (2) describe key characteristics of counties classified as rural through urban. We found that different measures yielded little agreement on rural places with consensus of only 114 of 3108 counties. County variables contributing to good separability in rural to urban classifications included remoteness, population density, percent of the population aged 65 and over, employment in the agricultural sector, and both developed and grassland landcover. Closer examination of the underlying characteristics of the 114 consensus rural counties provides an opportunity to reflect on the nature of popular rural measures and the variables they use. If these counties are not representative of how researchers conceptualize “rural” across US regions, it is motivation for working toward new and/or spatially adaptive rural measures that better represent the communities that policy and research intends to help.

Crafting an ultrashort workplace incivility scale and determining cutoffs for varied risk levels through item response theory

Workplace incivility (WI), characterized by disrespectful or rude behavior in the workplace, is linked to poor mental health and overall well-being. The Workplace Incivility Scale (WIS) is a popular 7-item measure for assessing WI. There is a current need for an ultrashort conceptually clear version of the WIS. In addition, the cutoffs for varied at-risks of WI, requiring intervention, remains unknown. Using data from 426 employees across diverse organizations, we employed item response theory (IRT) to create an ultrashort WIS and establish cutoffs for high, moderate, low, and at-not risk of WI. Confirmatory factor analyses were utilized to validate WIS construct validity. In supporting convergent validity, WIS was correlated with workplace bullying and poor health-related quality of life. All items demonstrated adequate severity threshold parameters with very high discrimination and good reliability parameters except for item WIS7. We successfully developed an ultrashort and valid 3-item WIS, specifically comprising items WIS2–4, and identified cutoffs for varying levels of WI risk. This streamlined measure aims to reduce response burden and foster a healthier organizational culture. In essence, cutoff points streamline the classification process, allowing for quicker and more standardized identification of at-risk employees. This concise, valid, and reliable ultrashort WIS holds potential for use in intervention studies conducted by organizational and occupational health psychologists, ultimately promoting employee well-being and cultivating a positive workplace environment. The current study further advances the existing theoretical framework rooted in the social interactionist perspective by delineating WI as a distinct and independent construct.