Terms Of Model Fit Research Articles

Coupled thermo–geophysical inversion for permafrost monitoring

Abstract. This study explores an alternative way of deriving soil thermal properties from surface geophysical measurements. We combined ground surface temperature time series with time lapse geoelectrical acquisitions measured from the ground surface in a fully coupled inversion scheme to calibrate a heat conduction model. The quantitative link between the thermal and geoelectrical parts of the modeling framework is the temperature-dependent unfrozen water content, which is also the main factor influencing electrical response of the ground. The apparent resistivity data were incorporated into the coupled framework without being inverted separately, thus reducing the uncertainty inevitably associated with inverted resistivity models. We show that geoelectrical time lapse data are useful as alternative calibration data and can provide as good results as borehole temperature measurements. The fully coupled modeling framework using field data achieved performance comparable to calibration on borehole temperature records in terms of model fit within 0.6 ∘C, inversion convergence metrics, as well as the predictive performance of the calibrated model.

Mapping Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) to Recovering Quality of Life (ReQoL) to estimate health utilities

BackgroundThe Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) is a widely used non-preference-based measure of mental health in the UK. The primary aim of this paper is to construct an algorithm to translate the SWEMWBS scores to utilities using the Recovering Quality of Life Utility Index (ReQoL-UI) measure.MethodsService users experiencing mental health difficulties were recruited in two separate cross-sectional studies in the UK. The following direct mapping functions were used: Ordinary Least Square, Tobit, Generalised Linear Models. Indirect (response) mapping was performed using seemingly unrelated ordered probit to predict responses to each of the ReQoL-UI items and subsequently to predict using UK tariffs of the ReQoL-UI from SWEMWBS. The performance of all models was assessed by the mean absolute errors, root mean square errors between the predicted and observed utilities and graphical representations across the SWEMWBS score range.ResultsAnalyses were based on 2573 respondents who had complete data on the ReQoL-UI items, SWEMWBS items, age and sex. The direct mapping methods predicted ReQoL-UI scores across the range of SWEMWBS scores reasonably well. Very little differences were found among the three regression specifications in terms of model fit and visual inspection when comparing modelled and actual utility values across the score range of the SWEMWBS. However, when running simulations to consider uncertainty, it is clear that response mapping is superior.ConclusionsThis study presents mapping algorithms from SWEMWBS to ReQoL as an alternative way to generate utilities from SWEMWBS. The algorithm from the indirect mapping is recommended to predict utilities from the SWEMWBS.

The 10-Item Short Form of the German Experiences in Close Relationships Scale (ECR-G-10)-Model Fit, Reliability, and Validity.

The aim of the present work was the development and validation of a short form of the Experiences in Close Relationship Scale (ECR) in German. Three studies were conducted. In study 1, the best items for the short form were selected from the item pool of the original version based on ant colony optimization (ACO), a recently developed probabilistic approach. Data from three samples collected at a university, an online portal, and a psychosomatic clinic with a total of 1470 participants were analyzed. A 10-item solution resulted, measuring avoidance and anxiety with five items each. This solution showed a good model fit and acceptable reliability in all three samples. The two new short scales were independent of each other. In study 2, the 10-item solution was validated by correlating the new short scales with external criteria. Data from previous studies that included student, community, and clinical samples were reanalyzed. Both short scales showed expected correlations with measures of romantic relationships, personality, psychopathology, and childhood trauma, indicating convergent and discriminant validity. The significant correlations were moderate to strong. In study 3, the selected ten items alone and several content-related scales were presented online to 277 participants, most of them students. The good results in terms of model fit, reliability, and validity observed in studies 1 and 2 could be replicated here. The new short form, called ECR-G-10, allows the measurement of attachment avoidance and anxiety in an economic way in research and clinical practice.

A Bayesian Nonparametric Modeling Approach to Settlement Patterns of Pastoralists Population in Kenya

Pastoralists' settlement patterns in Kenya have been studied for decades using various statistical and mathematical models. However, traditional models have often relied on restrictive assumptions, such as the normality of the data or the linearity of relationships. In this paper, we apply a Bayesian nonparametric approach to model the settlement patterns of pastoralists in Kenya, allowing for more flexible and realistic representations of the data. We first collected settlement data for pastoralists in Kenya and compiled a database of environmental covariates, such as distance to water sources, vegetation cover, and road networks. We then applied a Bayesian nonparametric clustering method to identify distinct settlement patterns and tested the performance of the model against other commonly used clustering techniques. Our results indicate that the Bayesian nonparametric approach outperforms other clustering techniques in terms of model fit and accuracy in identifying distinct settlement patterns. Additionally, we conducted a spatial regression analysis to investigate the relationship between settlement patterns and environmental covariates, revealing that distance to water sources and road networks are significant predictors of settlement patterns. Overall, our study highlights the usefulness of Bayesian nonparametric methods in modelling settlement patterns of pastoralists in Kenya and provides valuable insights into the relationship between environmental factors and settlement patterns.

Computational Model of the Transition from Novice to Expert Interaction Techniques

Despite the benefits of expert interaction techniques, many users do not learn them and continue to use novice ones. This article aims at better understanding if, when and how users decide to learn and ultimately adopt expert interaction techniques. This dynamic learning process is a complex skill-acquisition and decision-making problem. We first present and compare three generic benchmark models, inspired by the neuroscience literature, to explain and predict the learning process for shortcut adoption. Results show that they do not account for the complexity of users’ behavior. We then introduce a dedicated model, Transition, combining five cognitive mechanisms: implicit and explicit learning, decay, planning and perseveration. Results show that our model outperforms the three benchmark models both in terms of model fitting and model simulation. Finally, a post-analysis shows that each of the five mechanisms contribute to goodness-of-fit, but the role of perseveration is unclear regarding model simulation.

Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference

The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management.

CGAN-EB: A non-parametric empirical Bayes method for crash frequency modeling using conditional generative adversarial networks as safety performance functions

In this paper, a new non-parametric empirical Bayes approach called CGAN-EB is proposed for approximating empirical Bayes (EB) estimates in traffic locations (e.g., road segments) which benefits from the modeling advantages of deep neural networks, and its performance is compared in a simulation study with the traditional approach based on negative binomial model (NB-EB). The NB-EB uses negative binomial model in order to model the crash data and is the most common approach in practice. To model the crash data in the proposed CGAN-EB, conditional generative adversarial network is used, which is a powerful deep neural network based method that can model any types of distributions. A number of simulation experiments are designed and conducted to evaluate the CGAN-EB performance in different conditions and compare it with the NB-EB. The results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model (i.e. data conform to the assumptions of the NB model) and outperforms NB-EB in experiments reflecting conditions frequently encountered in practice, specifically low sample means, and when crash frequency does not follow a log-linear relationship with covariates.

On the integration of decision trees with mixture cure model.

The mixture cure model is widely used to analyze survival data in the presence of a cured subgroup. Standard logistic regression-based approaches to model the incidence may lead to poor predictive accuracy of cure, specifically when the covariate effect is non-linear. Supervised machine learning techniques can be used as a better classifier than the logistic regression due to their ability to capture non-linear patterns in the data. However, the problem of interpret-ability hangs in the balance due to the trade-off between interpret-ability and predictive accuracy. We propose a new mixture cure model where the incidence part is modeled using a decision tree-based classifier and the proportional hazards structure for the latency part is preserved. The proposed model is very easy to interpret, closely mimics the human decision-making process, and provides flexibility to gauge both linear and non-linear covariate effects. For the estimation of model parameters, we develop an expectation maximization algorithm. A detailed simulation study shows that the proposed model outperforms the logistic regression-based and spline regression-based mixture cure models, both in terms of model fitting and evaluating predictive accuracy. An illustrative example with data from a leukemia study is presented to further support our conclusion.

Cure rate models for heterogeneous competing causes.

Cure rate models have been widely studied to analyze time-to-event data with a cured fraction of patients. In this type of model, the number of concurrent causes is assumed to be a random variable. However, in practice, it is natural to admit that the distribution of the number of competing causes is different from individual to individual. Our proposal is to assume that the number of competing causes belongs to a class of a finite mixture of competing causes distributions. We assume the number of malignant cells follow a mixture of two power series distributions and suppose that the time to the event of interest follows a Weibull distribution. We consider the proportion of the cured number of competing causes depending on covariates, allowing direct modeling of the cure rate. The proposed model includes several well-known models as special cases and defines many new special models. An expectation-maximization algorithm is proposed for parameter estimation, where the expectation step involves the computation of the expected number of concurrent causes for each individual. A Monte Carlo simulation is performed to assess the behavior of the estimation method. In order to show the potential for the practice of our model, we apply it to the real medical data set from a population-based study of incident cases of cutaneous melanoma diagnosed in the state of São Paulo, Brazil, illustrating that the model proposed can outperform traditional models in terms of model fitting.

The Psychometric Properties of the DASS-21 and Its Association with Problematic Internet Use among Chinese College Freshmen.

During transitional periods, college freshmen may experience mental health issues. The Depression, Anxiety, and Stress Scale-21-item version (DASS-21) is commonly used for mental health assessment in China. However, evidence is lacking regarding its applicability with freshmen as a demographic. Debates also exist regarding its factor structure. This study aimed to evaluate the DASS-21's psychometric properties with Chinese college freshmen and investigate its association with three kinds of problematic Internet use. A convenience sampling method was used to recruit two samples of freshmen-one of 364 (female 248; mean age 18.17 years) and the other of 956 (female 499; mean age 18.38 years) participants. McDonald's ω and confirmatory factor analysis were conducted to evaluate both the scale's internal reliability and construct validity. The results indicated acceptable reliability, with a one-factor structure inferior to a three-factor structure in terms of model fit. Furthermore, it was demonstrated that problematic Internet use was significantly and positively associated with depression, anxiety, and stress among Chinese college freshmen. Based on the prerequisite of measurement equivalence across two samples, the study also found that freshmen's problematic Internet use and psychological distress were likely to be affected by the strict measures put in place during the COVID-19 pandemic.

Adjusted fence height: an improved phenotype for the genetic evaluation of show jumping performance in Warmblood horses

BackgroundShow jumping is one of the most popular disciplines in the horse sector, which makes success in show jumping competitions an important breeding goal for many studbooks. Therefore, the genetic evaluation of show jumping performance is of major interest and this is the case for two Belgian Warmblood studbooks: the Belgian Warmblood horse and Zangersheide. In this study, first an improved phenotype for show jumping performance was developed, i.e. adjusted fence height based on a new non-arbitrary method to scale ranking and competition level, which are two major components of success in competitions. Second, we assessed the importance of including a rider effect in genetic models for show jumping performance, this effect being under debate in sport horse breeding. Third, genetic models based on elementary performances and one model based on a summarized performance were compared in terms of model fit, heritabilities and the stability of estimated breeding values to define the most suitable one for the genetic evaluation of show jumping performance.ResultsIn this study, more than 600,000 Belgian competition records and almost 81,000 horses were used. Genetic evaluations were developed based on elementary performances (Blom-transformed ranking and adjusted fence height) and on a summarized performance (highest level achieved). Estimated heritabilities of Blom-transformed ranking, adjusted fence height and highest level achieved were 0.09, 0.12 and 0.39, respectively. Including a rider effect improved the models for genetic evaluations. Estimated genetic correlations between the studied models were moderate to high (rg = 0.60–0.99). With the best fit model, the accuracy of the estimated breeding value (EBV) for adjusted fence height reached 0.70 for a larger number of stallions and for stallions that tended to be younger.ConclusionsWe recommend breeders to implement this new phenotype ‘adjusted fence height’ in breeding programs. It is moderately to highly correlated with Blom-transformed ranking and highest level achieved, a proxy for lifetime success, and is available for selection candidates from an early age onwards.

Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China

For buyers, investors and urban policy, understanding drivers of community-level house prices across space and across time, are important for urban management and economic planning. In this study, we interrogated two housing market datasets, one from 2015, the other from 2019, for Wuhan, China, in order to uncover diversities and similarities in the spatial relationships between house price and contextual data; and in the context of increasingly volatile markets. A non-stationary approach was adopted with basic geographically weighted regression (GWR) and multiscale GWR (MGWR), where only the latter enables relationships to vary at their own spatial scale. In terms of model fit, both MGWR (adj. R2: 0.94 and 0.97, for 2015 and 2019, respectively) and GWR (adj. R2: 0.87 and 0.81) represented an improvement over the usual linear regression (adj. R2: 0.11 and 0.09) and the spatial lag mode (adj. R2: 0.21 and 0.27). Similarly marked improvements for GWR and for MGWR were found using corrected Akaike Information Criterion (AICc) based fit diagnostics. However, of more importance and via MGWR, the spatially varying drivers of house price were found to operate at a range of spatial scales, that in turn changed in strength and significance between the two study years. Such insights allow for spatially- and temporally-aware decision- and policy-making for housing price control and urban planning, given China’s housing markets can be increasing prone to strong growth coupled with severe depressions.

Spatial variation of ridesplitting adoption rate in Chicago

• Ridesplitting adoption rate (RAR) presents spatial heterogeneity across regions. • Factors affecting the RAR during different time periods are examined. • Subway station density is positive with the RAR in the southern area. • Potential substitutive and complementary effects between ridesplitting and transit. • Percent zero-vehicle households is negative with the RAR for most regions. Ridesplitting, a form of ride-hailing service where passengers with similar travel routes are matched to the same driver, can reduce the negative effects of solo ride-hailing trips and bring various environmental and social benefits. However, limited efforts were made to examine the spatial variation of ridesplitting trips, which was not conducive to the formulation of ridesplitting policies. To fill the gap, this work investigates the spatial variation of ridesplitting adoption rate (the proportion of ride-hailing trips with shared trip authorized, RAR) and its association with built environment and socio-economic factors at the census tract level, using the ride-hailing trip data in Chicago. To addressing the spatial heterogeneity, geographically weighted regression models are established to detect the factors influencing the RAR during different time periods, such as weekday, weekend, weekday morning peak and evening peak. Modeling results show that GWR models outperform the traditional global models in terms of model fit. The census tract level factors including subway station density, frequency of transit, land use mix, homicide density, percent female, the share of nonwhite, and percent zero-vehicle households have impacts on RAR, and the coefficient estimates of each explanatory variable vary across regions. The research results can help urban planners and transportation network companies develop refined policies to promote shared ride-hailing trips.

A Comparative Research on Data Analysis with Factorial ANOVA, Logistic Regression and CHAID Classification Tree Methods

When the data structure is large and complex, the extraction of information hidden within the data is called data mining. In the context of data mining, there are numerous methods developed for statistical data analysis. When these methods are classified as conventional-classical methods and current methods, factorial ANOVA (FANOVA) and Logistic Regression (LR) methods are shown as conventional methods, while decision trees called Classification Tree (CT) and Regression Tree (RT) can be shown as current methods. The method to be used in statistical data analysis is directly related to the researcher’s hypothesis (i.e. purpose) and variable type. Therefore, the choice of data analysis method is important. In this regard, studies in which methods are examined comparatively are guiding. In this study, a dataset on which inferences could be made by ANOVA, LR, and CT methods was analyzed. With this dataset, the relationship between the birth type (single-twin) as dependent variable and the yield year and maternal age as independent variables in an Awassi sheep flock was examined. The findings of each method were interpreted in its own specific way. The methods were compared in terms of explaining the similarities and differences of the information they presented and the relationship between dependent and independent variables.It was concluded that each method offered different inferences based on purpose and perspective. It is believed that it is the right approach for researchers to determine the data analysis method appropriate to their goals by taking into account the data structure.

Modeling Covid-19 Deaths in Nigeria Using Exponential-Gamma Distribution

This work examines the performance of Exponential-Gamma distribution and the existing Exponetial and Gamma in relative to the distribution that will best fit the data on Covid-19 death in Nigeria from March 2020 to April 2021. The parameters of the distribution were estimated using the method of maximum likelihood estimates. The newly developed Exponential-Gamma distribution was compared with the exiting Exponential and Gamma distributions using the log-likelihood function, Akaike information criterion (AIC), and Bayesian information criterion (BIC) as the criteria for selecting the best fit model. The results show that the newly developed Exponential-Gamma distribution performed better than the existing Exponential and Gamma distributions in contrast in terms of model fit, this showed that the newly developed Exponential-Gamma distribution is more flexible and precise in analyzing the Covid-19 data other than the existing Exponential and Gamma distributions.

Modelling the relationship between load and repetitions to failure in resistance training: A Bayesian analysis

ABSTRACT To identify the relationship between load and the number of repetitions performed to momentary failure in the pin press exercise, the present study compared different statistical model types and structures using a Bayesian approach. Thirty resistance-trained men and women were tested on two separate occasions. During the first visit, participants underwent assessment of their one-repetition maximum (1-RM) in the pin press exercise. On the second visit, they performed sets to momentary failure at 90%, 80% and 70% of their 1-RM in a fixed order during a single session. The relationship between relative load and repetitions performed to failure was fitted using linear regression, exponential regression and the critical load model. Each model was fitted according to the Bayesian framework in two ways: using an across-subjects pooled data structure and using a multilevel structure. Models were compared based on the variance explained (R2) and leave-one-out cross-validation information criterion (LOOIC). Multilevel models, which incorporate higher-level commonalities into individual relationships, demonstrated a substantially better fit (R2: 0.97–0.98) and better predictive accuracy compared to generalised pooled-data models (R2: 0.89–0.93). The multilevel 2-parameter exponential regression emerged as the best representation of data in terms of model fit, predictive accuracy and model simplicity. The relationship between load and repetitions performed to failure follows an individually expressed exponential trend in the pin press exercise. To accurately predict the load that is associated with a certain repetition maximum, the relationship should therefore be modelled on a subject-specific level.

Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems

Abstract. The vegetation's response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate–carbon cycle feedbacks requires improving our understanding of both the immediate and long-term plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e. droughts and heatwaves) and structural lags in the systems (such as delays between rainfall and peak plant water content or between a precipitation deficit and down-regulation of productivity) have largely been overlooked in the development of terrestrial biosphere models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years. Using data from 12 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr−1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). By focussing our analysis on a single continent (and predominately on a single genus), we reduced the degrees of variation between each site, providing a novel chance to explore the unique characteristics that might drive the importance of memory. Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. λE was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.93. When considered at a continental scale, both fluxes were more predictable when memory effects (expressed as lagged climate predictors) were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. Consistent with prior studies, the importance of environmental memory in predicting fluxes increased as site water availability declined (ρ=-0.73, p<0.01 for NEE, ρ=-0.67, p<0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a model of k-means clustering plus regression to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in individual-site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems.

Evaluating the psychometric properties of the German adaptation of the client attachment to therapist scale

BackgroundThe present study examines the psychometric properties of the German adaptation of the Client Attachment to Therapist Scale (CATS). The validity of the scale as originally proposed has recently been brought into question, as patients were identified as “pseudosecure”.MethodsWe examined the measure’s factorial structure, as well as reliability and validity towards related measures using a clinical sample of N = 354 participants.ResultsWe found the original model, consisting of 36 items to be lacking in terms of model fit and construct validity. A shortened 12-item version exhibited markedly improved model fit and reliability. Correlations to related constructs demonstrated that none of the scale’s validity was lost by shortening it. Furthermore, we showed scalar invariance across groups of age and sex.ConclusionsThe shortened CATS-S can be recommended for future use in clinical research in German-speaking populations as a valid, reliable, and economical alternative to the longer version.

Turkish Validity-Reliability Study of the Celiac Disease-Specific Pediatric Quality of Life Scale.

Celiac disease is an autoimmune enteropathy triggered by the presence of gluten. There are Celiac Disease Dutch-Child Quality of Life Scale, Celiac-Specific Pediatric Quality of Life Scale for children/adolescents patients to measure the quality of life. In this study, due to lack of quality of life scales for children with celiac in Turkey, we aimed at Turkish adaptation of the Celiac-Specific Pediatric Quality of Life Scale. This methodological study was conducted in Ankara University Faculty of Medicine, Cebeci Hospital Hospital between July 2019 and July 2020. A total of 192 children were included. Reliability was demonstrated by the Cronbach's alpha coefficient. Structural validity was evaluated using explanatory factor analysis and confirmatory factor analysis. The Statistical Package for Social Sciences (SPSS) 22.0 and Amos were used in analyses. In 8-12 age groups; Cronbach's alpha was 0.92 in negative emotions dimension, 0.88 in school dimension, and 0.74 in enjoyment dimension. In explanatory factor analysis, Kaiser-Meyer-Olkin measure of sampling adequacy value was 0.698, Bartlett's test of sphericity was significant (P < .001). Variance explained was 75.8%. In confirmatory factor analysis, X2/df was 3.26, root mean square error of approximation value was 0.07, comparative fit index value was 0.96. In 13-18 age groups; Cronbach's alpha was 0.87 in social dimension, 0.84 in uncertainty dimension, 0.78 in isolation dimension, and 0.83 in limitations dimension. In explanatory factor analysis, Kaiser-Meyer-Olkin measure of sampling adequacy was 0.684, Bartlett's test of sphericity was significant (P < .001). Variance explained was 68.6%. In confirmatory factor analysis, X2/sd value was 3.78, root mean square error of approximation value was 0.061, and comparative fit index value was 0.961. Cronbach's alpha values of the groups were found to be above 0.70. Kaiser-Meyer-Olkin values were above 0.5 in terms of sample size, Bartlett's tests for sphericity were significant in terms of correlations between variables, root mean square error of approximation values were below 0.08, comparative fit index and goodness of fit index values were above 0.95 in terms of model fit. If the scales have been found to be valid and reliable, it is recommended for use in Turkey.

Measurement Invariance of the Short Home Attachment Scale: A Cross-Cultural Study.

The home environment is a particularly significant part of life that is supposed to satisfy inhabitants’ needs, form their identity, and contribute to psychological wellbeing. The construct of home attachment is especially relevant for students as a most mobile social group. This study is devoted to the validation of the Short Home Attachment Scale (SHAS) in a student sample from five countries (Armenia, India, Indonesia, Russia, and Ukraine). A total of 1,349 (17–26 years; Mage = 19.82, SDage = 2.14; 78% females) university students participated in the study and filled in the 14 items of HAS. In order to avoid redundant items with high error covariances damaging the model, a new scale—the SHAS was developed by eliminating seven items. The shortened scale has satisfactory structure validity in terms of model fit in all countries except Indonesia; internal reliability values were acceptable in all countries. Measurement invariance across countries was tested with Multi-Group Confirmatory Factor Analysis (MG CFA) and Alignment Analysis. MG CFA confirmed both configurational and metric invariance. The invariance of item factor loadings, as well as item intercepts, was also confirmed by the Alignment Analysis. The mean scores varied across cultures, with the highest in India and the lowest in Russia. The final version of SHAS is a valid, reliable tool that may be recommended for use in cross-cultural research. However, the SHAS factor structure robustness in the Indonesian population should be investigated thoroughly.