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Background: Anaemia is a serious global public health problem with high prevalence (>40%) in children particularly in low- and middle-income countries including Namibia with a current 46.1% prevalence rate. Aim: This study was aimed at examining the sociodemographic factors influencing the occurrence of childhood anaemia levels in Namibia. Method: A multivariate ordinal regression model was applied to statistically identify potential sociodemographic factors associated with anaemia levels among children under-5 years old using data collected from the 2013 NDHS. Results: The odds of having mild anaemia level was lower for sociodemographic characteristics such as mother's age, total children ever born, health insurance coverage, child's residency, child's age and main language spoken at home. The odds of having moderate anaemia level was higher for characteristics such as mother's age, place of residence, highest education level and child's diarrhoea status, while it was lower for characteristics such as age of head of household, total children ever born, health insurance coverage and sex of child. Similarly, the odds of having severe anaemia level was higher for characteristics such as region, place of residence, highest education level, number of household members, wealth index, health insurance coverage, child's residency and child's diarrhoea status, while it was lower for characteristics such as total children ever born and sex of child. Conclusion: It is therefore recommended that the policies and practices concerning anaemia diagnosis, treatment and prevention in the country be substantially revised by policy-makers, starting with the known prevalent causes and identified sociodemographic factors from this study. [ABSTRACT FROM AUTHOR]

Analysis of business studies often involves the quantification of qualitative data to derive meaningful insights and making informed decisions. One such challenge is the inappropriate use of the arithmetic mean in economic and financial modeling. The arithmetic mean is a widely used statistical measure of central tendency that sums up a set of values and divides it by the total number of observations. While the arithmetic mean is simple and intuitive, its appropriateness in financial and economic modeling highly depends upon the nature of the data and the specific research question being addressed. This creates a dilemma. Despite the business community traditionally emphasizing quantitative research modeling, the growth of artificial intelligence and big data make qualitative research more desirable, particularly in areas such as ESG scorecards and financial literacy surveys. This paper discusses the challenges presented with analyzing studies after quantifying qualitative data and provides examples of how ordinal regression and other techniques could be used to analyze qualitative variables. This is especially applicable in undergraduate education. [ABSTRACT FROM AUTHOR]

• Cross-country surveys on hydrogen refuelling facility acceptance were conducted. • Support rates in Japan, Spain, and Norway: 21.1 %, 45.5 %, and 31.5 %, respectively. • Violations of parallel regression assumptions were observed in the three datasets. • Green hydrogen supply and risk information provision are pivotal facilitators. • Polarising predictors contributing to divergent acceptance stances were identified. Recognising the factors that shape the public acceptance of hydrogen refuelling facilities is essential for gaining social support for the widespread implementation of hydrogen infrastructure. This study applies a cross-national research perspective to investigate the public acceptance of hydrogen refuelling facilities using stated-preference survey data. Moreover, multidimensional explanatory variables encompassing hydrogen energy awareness levels, hydrogen refuelling station configurations, residential characteristics, car ownership and use, and sociodemographics are used to interpret public acceptance. The data revealed that 21.1 %, 45.5 %, and 31.5 % of the respondents in Japan, Spain, and Norway expressed support for such facilities near their residences, respectively. An important discovery was the validation of nonproportional odds in acceptance measurements across countries, which challenges the conventional assumptions of parallel regression and suggests that distinct behavioural determinants shape positive, negative, and neutral attitudes. Consequently, a generalised ordinal logistic regression approach was applied to identify the determinants of varying acceptance levels and mitigate the biases from nonproportional odds. In addition to identifying facilitators and hindrances, the results highlight several predictors with polarising effects. These findings provide public feedback for enhancing the planning of hydrogen infrastructure and offer potential pathways for hydrogen risk communication and policymaking. [Display omitted] [ABSTRACT FROM AUTHOR]

Introduction: Oral cancer is a disease resulting from an abnormal growth cell found in the mouth region such as in lips, tongue or throat. Furthermore, oral cancer commonly occurs at the buccal mucosa (cheek), tongue, the floor of the mouth and lip. The previous study shows that many studies focus especially on detecting the factors with the highest probability of cancer but the study which emphasizes the prediction of the tumor size category is still less from the point of computational biostatistical. Objective: The aim of this research paper is to build the methodology for computing ordinal regression model through SAS language by considering the clinical pathological prognostic factors in patients who attended Hospital Universiti Sains Malaysia (HUSM) from 2005 to 2015 (based on secondary data). Method: In this paper, there are three main methodologies proposed in this study. The first and second methodology is on the ordinal regression methodology and followed by bootstrap method, and the second is a nonlinear programming (NLP) methodology, which aims to obtain fuzzy regression modeling for the prediction purposed. Results: The result from ordinal regression had shown that smoking and nerve invasion factors contributing significantly to the growth of a tumor. The significant result from this finding (output based) can be used to educate people or stakeholder of how important this factor toward patients management. Conclusion: From a statistical point of view, the integration of computing methodology can expose and provide the researcher with an integrated method of analysis. [ABSTRACT FROM AUTHOR]

This study was designed to assess the effect of puppies and juvenile dogs' attendance at puppy classes on the behavior of the dogs at their adult age. For this purpose, 80 dogs (32 of which had attended puppy classes and the other 48 had not) were evaluated using the Canine Behavioral Assessment and Research Questionnaire that was filled out by owners 1 year after the completion of the puppy training. Dogs that attended classes were categorized as puppies (≤3 months) (n = 15) or juveniles (>3 months) (n = 17). Ordinal regression models were used to estimate the influence of puppy classes on the different behavioral traits assessed by the Canine Behavioral Assessment and Research Questionnaire. The results indicated that both puppies and juveniles that have attended classes had more favorable scores for family-dog aggression, trainability, nonsocial fear, and touch sensitivity. The study showed that attending puppy class may be important for social exposure with other puppies and people which could have an association with the dog's long-term behavior. [ABSTRACT FROM AUTHOR]

Abstract With more than 460 roundabouts located in Abu Dhabi, the capital city of the United Arab Emirates, it is imperative to evaluate the safety benefits provided by those roundabouts. In this study, two approaches were used to evaluate those safety benefits. The first approach is by measuring the 85th percentile operating speeds at a sample of 18 roundabouts in Abu Dhabi to determine whether the measured operating speeds conform to what is recommended by design guides. The second approach is by using a questionnaire to measure how drivers in Abu Dhabi perceive safety when driving at roundabouts and to measure their level of knowledge regarding the rules pertaining to driving at roundabouts. The study found that operating speeds at Abu Dhabi roundabouts typically exceed those recommended by design guides. The study also found that only 4.1% of the drivers interviewed demonstrated a comprehensive understanding of the rules pertaining to driving at roundabouts. Ordinal regression modeling was used to identify driver groups in need for more awareness of the rules to negotiate roundabouts in Abu Dhabi. The study found that the driver group in most need for more awareness is typically young and middle-age male drivers (below the age of 46 years) with less driving experience in countries where roundabouts are common. The questionnaire also revealed that despite the operational benefits provided by roundabouts (in terms of reduced delay), drivers do not prefer to drive at Abu Dhabi roundabouts, which might be explained by drivers' perception of Abu Dhabi roundabouts being not safe. Highlights • Operating speeds were measured at Abu Dhabi roundabouts using radar guns. • Operating speeds at Abu Dhabi roundabouts were found to be excessive. • Most drivers have inadequate knowledge related to driving at roundabouts. • Male drivers below 46 years need better education on driving at roundabouts. • 20.5% of drivers feel safer at roundabouts than at signalized intersections. [ABSTRACT FROM AUTHOR]

Abstract Ordinal regression as an important machine learning problem has been widely applied to information retrieval and collaborative filtering. Current ordinal regression methods include perceptron based (Pranks) methods, SVM based ordinal regression (SVOR) methods, and discriminant learning ordinal regression (KDLOR) method etc. Among these methods, KDLOR performs well for rank prediction, because of its majority on discriminant information for classes. However, this method only considered the ordinal information of two adjacent classes for discriminant learning. In fact, there exists much ordinal information in any upper-lower pair class. In this paper we present an enhanced discriminant learning ordinal regression (EDLOR) method which uses global ordinal constraints on any upper-lower pair class, simultaneously realize the maximum distance of them. The results of numerical experiments on Synthetic and benchmark datasets verify the usefulness of our approach. [ABSTRACT FROM AUTHOR]

Background: Doctor's performance evaluation is an important task in mobile health (mHealth), which aims to evaluate the overall quality of online diagnosis and patient outcomes so that customer satisfaction and loyalty can be attained. However, most patients tend not to rate doctors' performance, therefore, it is imperative to develop a model to make doctor's performance evaluation automatic. When evaluating doctors' performance, we rate it into a score label that is as close as possible to the true one.Objective: This study aims to perform automatic doctor's performance evaluation from online textual consultations between doctors and patients by way of a novel machine learning method.Methods: We propose a solution that models doctor's performance evaluation as an ordinal regression problem. In doing so, a support vector machine combined with an ordinal partitioning model (SVMOP), along with an innovative predictive function will be developed to capture the hidden preferences of the ordering labels over doctor's performance evaluation. When engineering the basic text features, eight customized features (extracted from over 70,000 medical entries) were added and further boosted by the Gradient Boosting Decision Tree algorithm.Results: Real data sets from one of the largest mobile doctor/patient communication platforms in China are used in our study. Statistically, 64% of data on mHealth platforms lack the evaluation labels from patients. Experimental results reveal that our approach can support an automatic doctor performance evaluation. Compared with other auto-evaluation models, SVMOP improves mean absolute error (MAE) by 0.1, mean square error (MSE) by 0.5, pairwise accuracy (PAcc) by 5%; the suggested customized features improve MAE by 0.1, MSE by 0.2, PAcc by 3%. After boosting, performance is further improved. Based on SVMOP, predictive features like politeness and sentiment words can be mined, which can be further applied to guide the development of mHealth platforms.Conclusions: The initial modelling of doctor performance evaluation is an ordinal regression problem. Experiments show that the performance of our proposed model with revised prediction function is better than many other machine learning methods on MAE, MSE, as well as PAcc. With this model, the mHealth platform could not only make an online auto-evaluation of physician performance, but also obtain the most effective features, thereby guiding physician performance and the development of mHealth platforms. [ABSTRACT FROM AUTHOR]

Objectives: To examine the extent at which match outcome and ladder position could be explained using team performance indicators in the National Rugby League (NRL).Methods: The dataset consisted of 13 performance indicators acquired from each NRL team across the 2016 season (n=376 observations). Data was sorted according to apriori match outcome (win/loss) and ladder position (one to 16). Given the binary and categorical nature of the response variables, two analysis approaches were used; a conditional interference classification tree and ordinal regression.Results: Five performance indicators ('try assists', 'all run meters', 'offloads', 'line breaks' and 'dummy half runs') were retained within the classification tree, detecting 66% of the losses and 91% of the wins. A significant negative relationship was noted between ladder position and 'kick metres' (β (SE)=-0.002 (<0.001); 95% CI=-0.003 to <-0.001) and 'dummy half runs' (β (SE)=-0.017 (<0.012); 95% CI=-0.041 to 0.006), while a significant positive relationship was noted for 'missed tackles' (β (SE)=0.019 (0.006); 95% CI=0.006-0.032).Conclusions: A unique combination of primarily attacking performance indicators provided the greatest explanation of match outcome and ladder position in the NRL. These results could be used by NRL coaches and analysts as a basis for the development of practice conditions and game strategies that may increase their teams' likelihood of success. Beyond rugby league, this study presents analytical techniques that could be applied to other sports when examining the relationships between performance indicators and match derivatives. [ABSTRACT FROM AUTHOR]

Large-scale linear classification is widely used in many areas. Although SVM-based models for ordinal regression problem are proven to be powerful techniques, the performance with nonlinear kernels are often suffering from time consuming. Recently, linear SVC not only is shown to obtain competitive performance in most of the cases, but also it is considerably fast during the process of training and testing. However, few studies focused on linear SVM-based ordinal regression models. In this paper, we propose a new approach, called linear Nonparallel Support Vector Ordinal Regression (NPSVOR), which can deal with large-scale problems. An efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the proposed model. Our experiments are performed on large document data sets to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the changes in the virtual perception on the built heritage at the traditional core settlement of Kumbakonam Town at Tamilnadu and to analyze their implications. Specifically, the major objectives of the study are (1) to identify the architectural elements that manifest the built heritage of Kumbakonam Town and (2) to assess the contributions of these elements to the changes in the visual perception of the town. To achieve these objectives, this study adopts an empirical model that analyzes the architectural elements of the buildings in the study area. Direct observations and documentations of 373 buildings are collected to analyze those elements that contribute to the changes in the visual perception on the built heritage of Kumbakonam Town. An ordinary regression model is used to examine the characteristics of the built heritage across the chariot processional route of the town. Several architectural elements, including pilasters, horizontal cornices, arched windows, and ornamental parapets, improve the image of the town. These empirical findings support the policy framework that enhances the visual perception of Kumbakonam Town. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of International Social Research is the property of Journal of International Social Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To forecast giant panda (Ailuropoda melanoleuca) population dynamics in the wild, it is crucial to comprehend their age distribution. Traditional methods for estimating the age of panda are costly, time-consuming, and inaccurate. Additionally, these methods only forecast an age group rather than a real age, and lack a uniform standard. However, advances in deep learning and computer vision have given rise to fresh approaches to this problem. Classification models can be improved by using ordinal regression, which uses ordinal correlations across ages to reduce the non-stationary nature of aging tasks. In this study, we collected 8002 images from 272 pandas in various environments, whose ages ranged from 0 to 38. We applied a five-fold subject-exclusive (SE) protocol to train seven Convolutional Neural Networks (CNN) based on ordinal regression. Experiments were conducted on the Panda Age Dataset (PAD Full) and the Lite Panda Age Dataset (PAD Lite). The results were very encouraging and achieved a Mean Absolute Error (MAE) of 2.51 and 2.41, respectively. Our findings demonstrate that this new tool can noninvasively predict the age of giant pandas in captivity and the wild. Continued development of computer vision technology will drive progress in ecology and conservation. • Machine learning can be used to predict the age of giant pandas based on facial images. • Ordinal regression can enhance model performance based on age information. • The inference speed of model is as important as its accuracy. [ABSTRACT FROM AUTHOR]

One of the recurrent themes in open source software research is to understand the impacts of various project characteristics on its success. Open source software (OSS) projects rely on voluntary participation of developers and tend to be continually in development. Hence, an important measure of success is the time it takes for an OSS project to release a stable version to its users. However, there is little research on this success measure and how the OSS characteristics enable or delay the progress towards stable release. In this study, we use survival analysis technique on open source project data to explore the impacts of OSS characteristics on the time it takes to release stable software versions. We find that when compared to the interest of developers in the project, interest of end-users has a greater positive effect on an OSS project progress towards stable release. Our findings also suggest that the use of C and C-like programming languages or a Weak-Copyleft license for the open source project negatively impact the project's time to reach stable status. In OSS projects less than 8 months since becoming public, the use of a Strong-Copyleft license positively affects the project's progress. One of the implications of our findings is that OSS project administrators should control software change requests or form smaller developer groups to better control the delays due to higher developer interest in their projects. [ABSTRACT FROM AUTHOR]

Sovereign rating has had an increasing importance since the beginning of the financial crisis. However, credit rating agencies opacity has been criticised by several authors highlighting the suitability of designing more objective alternative methods. This paper tackles the sovereign credit rating classification problem within an ordinal classification perspective by employing a pairwise class distances projection to build a classification model based on standard regression techniques. In this work the ϵ -SVR is selected as the regressor tool. The quality of the projection is validated through the classification results obtained for four performance metrics when applied to Standard & Poors, Moody's and Fitch sovereign rating data of U27 countries during the period 2007–2010. This validated projection is later used for ranking visualization which might be suitable to build a decision support system. [ABSTRACT FROM AUTHOR]

Highlights: [•] The problem of constructing an organ allocation model via machine learning techniques is assessed. [•] The combination of donor, recipient, and surgery characteristics helps the prediction of graft survival after transplantation. [•] An ordinal classifier has been proved to perform well for the 4-category classification problem assessed. [•] An extended supranational experimental design for liver transplantation allocation could be feasible considering more countries. [Copyright &y& Elsevier]

Despite the scientific evidence of climate change, in the arenas of public opinion and policy decisionmaking, climate change is portrayed by the news media as a contentious issue. Previous studies examining the influence of global warming climate contrarians on news coverage of climate change have focused on such aspects as the frequency of interviews, sources of funding for global warming contrarians, and analyses of the arguments put forth by them. The purpose of this study is to examine the extent of their influence in creating a news frame of global climate change, using a combination of content analysis, frame analysis and ordinal regression. Data from English-language print media from news sources from nine countries were analyzed. The study finds that inclusion of climate contrarians in news stories about global warming has a significant impact on the framing of the issue, and that scientists promoting the consensus view on climate change need to employ linguistic devices such as metaphors to counter the climate contrarian frame. [ABSTRACT FROM AUTHOR]

The QWERTY effect suggests a consequence to word meaning deriving from the placement of letters on a QWERTY keyboard. Jasmin and Casasanto (2012) reported that words formed primarily of letters from the left side of the keyboard were more aversive in nature, whereas those on the right side were more attractive (right-side advantage, RSA); they concluded that those individuals branding new products could ensure a positive affect by attending to the balance of letters. I tested this hypothesis on arguably the most important branding decision an individual can make, the naming of a baby, by associating name popularity against RSA. Names and their rank among the top 1000 names reported to the Social Security Administration were gathered for each decadal interval between 1880 and 2010 (n = 28,000 names). I found no evidence for the QWERTY effect in child names (βRSA = 0·007; 95% CI = [-0·014, 0·027]). Instead, gender-specific patterns in name popularity were related to length of name (βName Length = 0·079 [0·058, 0·099]). Parents should not be concerned that positive affect is dictated by the QWERTY effect. [ABSTRACT FROM AUTHOR]

Abstract: Chronic child undernutrition is a persistent problem in developing countries and has been the focus of hundreds of studies where the primary intent is to improve targeting of public health and economic development policies. In national level cross-sectional studies undernutrition is measured as child stunting and the goal is to assess differences in prevalence among population subgroups. Several types of regression modeling frameworks have been used to study childhood stunting but the literature provides little guidance in terms of statistical properties and the ease with which the results can be communicated to the policy community. We compare the results from quantile regression and ordinal regression models. The two frameworks can be linked analytically and together yield complementary insights. We find that reflecting on interpretations from both models leads to a more thorough analysis and forces the analyst to consider the policy utility of the findings. Guatemala is used as the country focus for the study. [Copyright &y& Elsevier]

The Student Satisfaction Survey was necessitated by the notion that students have needs and rights to participate in quality programmes and to receive satisfactory services. The main objective was to assess the satisfaction experienced by students in the various departments they are in contact with at the Polytechnic (Toshimori, et. al. 2011; Lester, 2006; Chau-Kuang and Hughes, 2004). The outcome variable for student satisfaction with academic support services was measured on an ordered, categorical, and four-point Likert scale-'very dissatisfied', 'dissatisfied', 'satisfied', and 'very satisfied'. Explanatory variables included two demographic variables: gender and departments, and 42 questionnaire items related to the satisfaction of department involvement, curriculum contents, support services, facilities, and extra-curricular activities at the polytechnic (Sarah and Charlene, 2010; Toshimori, et. al. 2011) Proportional stratified sampling technique was used to select the sample 0f 1044 for the survey. In order to study the effects of explanatory variables on all levels of the ordered categorical outcome, an ordinal regression was used to model students' satisfaction derived from the academic support services provided by the polytechnic. The overall impression given by students was that they were highly satisfied with the services of all the departments they came in contact with at the Polytechnic. They were also highly satisfied with the quality of teaching and the helpfulness of library staff. However, they expressed low satisfaction about the non-availability of some books on their reading list in the library, lack of reading space in the library, late arrangement for examinations, and late release of examination results. Recommendations have been made for the attention of departments and the intervention of management so that suitable action plans can be developed to address issues that give cause for respondents' low satisfaction [ABSTRACT FROM AUTHOR]

Abstract: Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH)⩾12.7cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBH⩾50cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification of large snags than the RF imputation approach. Adjusting the decision threshold to account for unequal size for presence and absence classes is more straightforward for the logistic regression than for the RF imputation approach. Overall, model accuracies were poor in this study, which can be attributed to the poor predictive quality of the explanatory variables and the large range of forest types and geographic conditions observed in the data. [Copyright &y& Elsevier]

Abstract: Ordinal regression shares properties of both classification and regression, since the goal function maps the instance space into the integer set. In this paper, we studied generalization properties of ordinal regression algorithms, and we focused on the performance of goal function effecting on individual examples. The strong (weak) loss stabilities and strong (weak) score stabilities for standard regression algorithms we defined only consider changing one element of training set. With the definitions of stabilities for ordinal regression algorithms and the clipped loss function, four kinds of generalization bounds are given in this paper. [Copyright &y& Elsevier]

The physical environment of the classroom plays an important role in students' motivation and performance. The purpose of this study is to show how to improve the quality of the classroom by capturing the students' feelings/emotions. For this aim, a Kansei Engineering algorithm was proposed. Collected data were analyzed using an ordinal regression analysis which was suggested versus a linear regression analysis oft en used in the literature. Firstly, we investigated which classroom components can be improved. The results revealed that the teacher's desk, lighting, ventilation, and the curtain were the most important components in terms of their contribution to students' motivation. Secondly, we determined which design goals (features) of the significant components contribute to students' motivation. Our approach in this study provides an algorithm to measure students' feelings and demonstrate how to use them to improve the classroom environment. [ABSTRACT FROM AUTHOR]

Automatic classification of cervical cells plays a critical role in the Computer-assisted Cytology Test (CCT) system. The efficiency of the CCT system can be promoted by sacrificing the microscopic image resolution to speed up the microscopic image acquisition. In this case, the low resolution of the cell image will severely deteriorate the performance of available Convolutional Neural Networks (CNN) based classification methods. Inspired by the positive effect of super-resolution in addressing classification or recognition tasks, we propose a cervical cell classification algorithm leveraging simultaneous super-resolution, which is achieved using Generative Adversarial Network (GAN) techniques. Our framework is designed in an end-to-end manner wherein the classification loss is back-propagated into the super-resolution network during training. Moreover, we perform ordinal regression with smooth L1 loss to further improve the classification results. Extensive experiments have verified the effectiveness of our method. Our simultaneous super-resolution based method achieves 93.5% classification accuracy on the 6-class Heer dataset, outperforming the method using only the state-of-the-art classifier by an obvious margin of 3.2%. Besides, our ordinal regression method significantly improves the MAE (Mean Absolute Error) by 0.0143 and 1-off accuracy by 0.95% on the 4-class Heer dataset. For the Herlev dataset, our method yields the classification accuracy of 98.1% and 97.6% for the 2-class and 7-class problems, which is still competitive even with low-resolution input. • Proposing SR-CLS networks to facilitate classification utilizing simultaneous super-resolution. • Adopting CycleGAN structure to ensure the stability of the network. • Back-propagating the classification loss into the super-resolution network to facilitate the classification performance. • Performing ordinal regression with smooth L1 loss to further promote the classification performance. [ABSTRACT FROM AUTHOR]

Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q² statistic is introduced for testing the omnibus null hypothesis in DOMR. A simulation study is discussed that examines the actual Type I error rate and power of Q² in comparison to the LSMR omnibus F test under normality and non-normality. Results suggest that Q² has favorable sampling properties as long as the sample size-to-predictors ratio is not too small, and Q² can be a good alternative to the omnibus F test when the response variable is non-normal. [ABSTRACT FROM AUTHOR]