Ordinal Logistic Regression Method Research Articles

Predicting construction schedule overruns during COVID-19 using ordinal logistic regression

PurposeThe effects of the COVID-19 outbreak on the construction industry were formidable and far-reaching, as the construction sector is a major contributor to the gross domestic product (GDP), which balances various sectors of the global economy, and to infrastructure growth, which is a primary gauge of a nation’s advancement. The outbreak led to workforce disruptions, worker deficits, dwindling efficiency, elongated project durations, and scarce opportunities for training and mentorship, and despite endeavors to mitigate these challenges, construction timelines experienced significant interruptions. Various researchers have pinpointed contributing elements, but few have constructed a predictive model to gauge the degree of impact.Design/methodology/approachTherefore, this research intends to fill by introducing an ordinal logistic regression method to forecast the impacts of a pandemic or other similar type of crisis. To achieve this, an online survey was developed and distributed to collect the perceptions of the construction engineers and managers about the diverse contributors to the exceeding project timelines during the COVID-19 pandemic outbreak.FindingsFindings from this study indicate that financial liquidity, modifications to original plans, delays in securing governmental clearances, and a shortage of competent labor have medium-to-high levels of impact on project schedules.Originality/valueThis study will furnish decision-makers with crucial knowledge that will give them the tools to refine their strategies and judiciously allocate resources to overcome the unique hurdles encountered by various construction segments and will enhance the industry's capability to respond more effectively to challenges inherent in this type of crisis.

Digitization and Citizens’ Satisfaction with Land Services in Rwanda

Purpose: The purpose of the study was to examine whether there is a relationship between digitization and service quality of land services and to establish if the relationship is significant. Methodology: The study used a quantitative research approach to collect the data. A probability sampling technique was employed to choose a sample of 2,229 respondents from a population of 47,260 land service customers. A bivariate correlation analysis was conducted for all questions and the Pearson correlation coefficient was statistically significant with a value less than 0.05. Using Cronbach’s Alpha calculation, reliability was tested and produced a value of 0.946 > 0.7 which demonstrates excellent reliability of the tool. An ordinal logistic regression method was applied to model the relationship between one outcome variable and seven predictor variables. Findings: the research findings show that the seven elements of digitization have a significant influence on the satisfaction of customers with land services. The element with the highest influence is the digitization of land services preserves the security of land information effectively (6.555); digitization of land services reduces the cost of land transfer services (6.476); digitization of land services increases quality and consistency in land services provision (6.096); digitization of land transfer services offers easy access to files and information (6.049); digitization of land services minimizes corruption (5.937); digitization of land services reduces human error (5.863) and digitization of land services reduces the time to process land documents (5.745). Contribution to Theory, Policy, and Practice: Establishing if digitization can measure service quality is a new aspect that the study adds to the body of knowledge. The researchers recommend that digitization be added to the dimensions that measure service quality in ways that take into consideration study contexts

An analytical approach to understanding construction cost overruns during COVID-19

PurposeThe building sector is vital to a nation’s economy, as it has a major influence on economic activity and growth, job creations and the advancement of infrastructure. Intricate challenges that are inherent in crises such as the COVID-19 outbreak lead to material scarcities, project delays, labor shortages, escalated expenses, funding challenges, regulatory obstacles and dwindling investment funds, all of which culminate in costs that are in excess of those budgeted. While numerous studies have explored the ramifications of COVID-19 on project budgets, there is little, if any, data available on forecasting the magnitude of this impact.Design/methodology/approachThis investigation seeks to bridge this knowledge deficiency by devising a predictive tool grounded in an ordinal logistic regression method. An online survey was designed and disseminated to gauge the views of construction field experts about the diverse contributors to excessive costs during the viral outbreak, and a predictive tool, crafted from the survey participants’ feedback.FindingsFindings showed that smaller-scale enterprises and contractor-centric establishments faced greater adversities than medium-to-large ones and consultancy-or-owner-type entities.Originality/valueThe insights from this research shed light on the amplified risk of higher project costs amid health crises or analogous events, underlining the imperative need for fortified risk management approaches to bolster project outcomes. By factoring in demographics, this research offers policymakers a refined lens through which to customize interventions and promote balanced and enduring advancement in the construction industry.

Influencing factors of green building practitioners’ competition perceptions: evidence from China

The concept of green sustainability has driven innovation in green construction technologies (GCTs), fostering heightened competition in the construction market. However, there is limited research on the factors that influence the perceptions of green building practitioners (GBPs) regarding the intense market competition. To address this, based on 407 questionnaires collected in China, this study employs the ordinal logistic regression (ORL) method to investigate the relationship between heterogeneity, psychological capital and industry pressure, and GBPs’ perceptions of market competition intensity. The results reveal that (1) GBPs’ competition perceptions (CPs) are generally high and (2) Individual and occupational differences in GBPs’ CPs are significantly different. Notably, higher education levels correlating to diminished CPs, and managers exhibit greater sensitivity compared to non-managers. (3) Perceived value and company’s environmental responsibility are the most influencing factors that significantly affect GBPs’ CPs. This study offers implications for enhancing GBPs’ understanding of GCTs and industrial competition, thus contributing to improving decision-makers’ strategies for establishing competitive advantages. Additionally, it aids in formulating market competition rules, thereby fostering the transformation of the construction industry.

Implementation of the Ordinal Logistic Regression Method for Air Quality Classification Based on the Air Pollution Standard Index

Air pollution is a serious problem in many cities around the world, caused by human and natural activities. Jakarta, as an Indonesian economic and transportation hub, faces serious air pollution challenges. This study uses the Ordinal Logistics Regression Method to develop an optimal classification model for identifying air quality based on the air pollution index. The aim is to contribute to dealing with air contamination and improve understanding of the use of such methods in the classification of air quality. Data used from 2012 to 2021 covered parameters such as PM10, SO2, CO, O3, and NO2. Oversampling is done using SMOTE to address imbalances in the datasets used. The data used is divided into two parts: training data and validation test data, with an 80:20 ratio. The model training and testing process is carried out with a variety of scenarios, including parameter significance tests using probability tests and Wald tests, cross-validation with fold numbers 5, 10, and 15, as well as evaluation using a confusion matrix. Models are used for Mord libraries such as Ordinalridge, LogisticAT, logisticIT, and LogisticSE. There were a total of 72 model testing experiments to find the best model of the six data model outputs. The test results showed that the optimal model was obtained with a data deletion scenario of null values, data oversampling using the LogisticIT model, and K-fold = 5, with a training accuracy of 0.8628 and validation data test accuracy of 0.8599, as well as each precision value of 0.86, recall value of 0.86, and F1-score of 0.86. The model's performance was satisfactory in handling different data variations, according to the evaluation. These results show that the model is able to generalize data well and is reliable in predicting air quality accurately.

PERBANDINGAN ORDINAL FOREST DAN REGRESI LOGISTIK ORDINAL

There are many methods used to identify the important predictors and predict the value of the ordinal response variable. However, currently, there are prediction methods for ordinal response variables that do not use the ordinal nature of the response variables. This study uses the ordinal forest method and as a comparison the ordinal logistic regression method. The accuracy and kappa value of the ordinal forest method in each replication (5 replications) is always greater than the ordinal logistic regression. Furthermore, the value of accuracy and kappa of each group based on GRDP in the ordinal forest method is always greater than the ordinal logistic regression. So that the ordinal forest method is better used in data on village development index in North Maluku Province in 2020.

Factors Influencing Thai Muslims' Willingness to Donate Cash Waqf to Religious Projects

Purpose — Muslim communities in Thailand have lately used cash waqf as a creative strategy to raise seed money from Muslim contributors to implement religious projects such as building religious institutions like mosques and Islamic schools. The factors influencing this unique kind of giving have, to date, rarely been studied. This research attempts to examine the motivational and economic elements that impact people’s willingness to donate (WTD) cash waqf to such religious projects. Design/Methodology/Approach — The study utilised a Contingent Valuation Method (CVM) and an ordinal logistic regression method to examine the effects of various independent variables on the WTD. A validated structured questionnaire was utilised to collect primary data from 404 cash waqf donors in the southernmost provinces of Thailand. Findings — When comparing donors with medium income levels to those with higher salary levels, the findings show that donors in the higher salary category were more likely to generate a higher WTD propensity. The probability of donors’ WTD was positively influenced by any reductions in debt levels. Furthermore, factors such as belief in the afterlife, the salience of one’s Muslim identity, and the experience of a warm glow were found to have a positive impact on individuals’ WTD cash waqf to religious projects. Originality/Value — This study utilised the Contingent Valuation Method (CVM) to examine the cash waqf practices, which has received limited attention in previous scholarly works. Furthermore, the variables employed in this research serve to broaden the scope of understanding in the realm of Islamic philanthropy, surpassing the confines of the attitude-intention framework. Research Limitations/Implications — The factors used in this study encompass the internal drives of potential donors. In reality, a number of external circumstances, such as the validity of the waqf project and the donor’s accessibility to other options, may have an impact on the donor’s choice. Additionally, the scope of this study was restricted to the southern border provinces of Thailand. It would be of interest to conduct additional research that broadens the sample size and geographical scope to include other regions within the country or even other countries with diverse cultural and socioeconomic contexts. This would enable the generalisation of the findings obtained from this study. Practical Implications — Waqf fundraising organisations should focus on boosting awareness among high-income, low-debt people. This population group donates more than middle- and lower-class people. Mosques and religious organisations can use a strategy to help donors reassess the spiritual benefits of waqf giving in relation to the afterlife, the reinforcement of their Muslim identity, and the emotional satisfaction of giving. These motivators will increase the likelihood of mosque donations.

An Analysis of the DIF Sources of ABIDE Mathematics Self-Efficacy Scale by means of a Latent Class Approach

This study aims to identify the sources of Differential Item Functioning (DIF) using the Mixture Ordinal Logistic Regression (Mixture OLR) method, a contemporary approach for detecting DIF. To analyze mathematics self-efficacy, data from a scale comprising 9 items were obtained from 5000 8th-grade students as part of the ABIDE-2016 project. The study compared the presence and extent of DIF by gender using two methods and examined the sources of DIF for items displaying DIF with Mixture OLR. The OLR analysis revealed that five items exhibited DIF at level A, but no DIF was observed with Mixture OLR. Furthermore, it was found that the magnitude of DIF (B) for an item showing DIF at level A changed due to Mixture OLR. The results indicate that the homogeneity of the data affects both the number of items displaying DIF and the magnitude of DIF. Three items did not exhibit significant DIF according to both methods. One significant finding in the study highlights the moderating effect of latent class on item 8, where DIF was observed. However, the source of DIF was not related to gender but rather stemmed from different ecological variables. An analysis of latent class characteristics revealed that students with significant DIF effects had lower absenteeism and fewer siblings. Additionally, students in this class had greater access to books at home and participated in more out-of-school mathematics courses. Surprisingly, these students were found to engage less in social activities. Various factors can influence how students respond to test items, potentially leading to DIF. These factors may include cultural background, gender, social environment, school, teacher, family interest/attitude toward the child, and home climate. Therefore, when developing and administering tests, it is crucial to test for data homogeneity and consider the impact of these variables, in addition to gender, to identify any sources of DIF in test items.

Power Analysis for Ordinal Analyses of the Modified Rankin Scale and an Online and Downloadable Tool for Practical Use.

Several methods for conducting power analysis of studies with outcomes across the full ordinal modified Rankin Scale are proposed in the literature. No systematic comparison of accuracy, agreement, and sensitivity to small changes in hypothesized effect sizes for these methods is available. Our aim is to conduct such a systematic comparative analysis and to introduce a comprehensive freely available online tool to facilitate appropriate power analyses for ordinal outcomes. We performed simulation studies utilizing the control arm modified Rankin Scale distributions from the AVERT (A Very Early Rehabilitation Trial), EXTEND (Extending the Time for Thrombolysis in Emergency Neurological Deficits), and HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) studies, as well as a uniform distribution, in combination with hypothetical treatment effects. We systematically evaluated published power formulas for Ordinal Logistic Regression and Tournament Methods (generalized odds ratio; Win Probability; Win Ratio; and Wilcoxon-Mann-Whitney U test). We also developed an online and downloadable Shiny R app facilitating sample size calculation for, and ordinal analysis of, modified Rankin Scale data. Power formulas for Tournament Methods performed well, while the formula for ordinal logistic regression was inaccurate. Tang's Wilcoxon-Mann-Whitney U test sample size formula exhibited the highest accuracy. All methods, including ordinal logistic regression, had almost identical empirical power for a given sample size. All power methods exhibited sensitivity to small changes in hypothesized effect size. The developed freely available online app supports analytical and visualization requirements for all investigated methods for power and statistical analyses of ordinal modified Rankin Scale outcomes. As Tournament Method sample size formulas are assumption-free and accurately calculate power, stroke researchers should use these methods when designing studies with outcomes measured on the full or partially collapsed modified Rankin Scale as well as other ordinal scales, even if they intend to use ordinal logistic regression for analysis. Conducting sensitivity analyses of the effect size assumptions are essential for appropriate sample size estimation. Our developed tool supports both of these recommendations (https://www.thembc.com.au/tournamentmethods).

A SPATIAL STUDY OF LAND AND FOREST FIRE-PRONE AREAS IN SITUBONDO REGENCY, EAST JAVA PROVINCE

The increasing area of land burned in 2021 makes the government urgent to map areas prone to forest fires in Situbondo Regency. This study analyzes areas prone to forest and land fires using the SMCA method. The research analysis used variables of land cover type, the greenness of vegetation, vegetation humidity, land surface temperature, and human factors. The human elements in question are accessibility (distance from the road network) and distance from human activities (distance from settlements, fields, and plantations). The conclusion analysis of forest fire-prone areas is divided into three classes that are high, medium, and low. From the vulnerability model that has emerged, it was found that most of Situbondo Regency have a high grade of forest fire vulnerability with an area of 652.66 km² (39.08%). The areas with the level of vulnerability of the middle, low, and non-vulnerable classes, respectively, are 532.12 km² (31.87%), 306.46 km² (18.35%), and 178.65 km² (10, 70%). The results of statistical tests using the ordinal logistic regression method show that natural factors for forest and land fires had a higher level of influence (? = 4.824) on forest and land fire vulnerability compared to human factors (? = 1.051).
 Keywords: Forest and Land Fires; GMA method; Natural Factors; Human Factor

Impact of pandemic on women’s purchase decision for feminine hygiene products: a Covid-19 lockdown perspective

The uncontrolled spread of the COVID-19 pandemic has forced nations to shut their borders and impel the lockdown. Although the pandemic has affected the nations economically and socially, the nationwide lockdown has also dramatically affected the supply and demand for feminine hygiene products. It became interesting for the researcher to investigate the effect of COVID-19 lockdown in the purchase decision, availability, and perception of women for feminine hygiene products. The study was conducted employing a quantitative approach. A self-administered questionnaire was used to elicit responses from females using a convenience sampling technique and interpret the data ordinal logistic regression method. The findings of this study indicated that within the lockdown period, females with consistent income started buying a slight high quantity of feminine hygiene products so that they can store them for an emergency; it has also been seen that they find it difficult to shop for these products from the stores as in the initial period of lockdown as these products were not put into essential item list. So shortage was noted. However, females observed these products as important products for storage; This study will assist the policymakers and private ownerships in better insight into females’ attitudes toward feminine hygiene products and provide timely resources and services to the affected population during a pandemic situation.

Klasifikasi Kadar Glukosa Darah Keluaran Alat Non-invasif Menggunakan Regresi Logistik Ordinal dengan Peringkasan Luas

Diabetes Mellitus (DM) is the silent killer because its symptoms tend to go unnoticed. Blood glucose level checks must be performed periodically to control blood glucose levels for DM and non-sufferers. The IPB Non-Invasive Biomarking Team developed a non-invasive monitoring device to check blood. The tool uses the spectroscopy principle and produces an output in the form of a residual value of light intensity. A method is needed to predict the category of blood glucose levels based on the measurement results of non-invasive tools. Classification modeling is one of the methods that can be used to analyze the relationship between the blood glucose level class of invasive measurement results and the residual value of the intensity of non-invasive measurement results. One of the commonly used classification methods is ordinal logistic regression. This method generates the final model of a cumulative opportunity logit function with an opportunity value for each class as a differentiator between classes. Light spectrum-based data used as predictor X changes often provide changes that correlate with each other. The principal component analysis reduces its dimensions to become a new set of changes that do not correlate. A good data summarization approach at the preprocessing stage is also necessary to provide good modeling. Several summarization methods have been carried out in previous studies. Graph area summation in the period is the best summarization method because it can take advantage of the general data information. This study uses the ordinal logistic regression method as a modeling method by applying principal component analysis and graph area summation applied to 2017 data and 2019 data. Classification modeling in the 2017 data had a balanced accuracy value of 64,64%. Classification modeling in the 2019 data produced a balanced accuracy value of 57,57%. The design used in the 2017 tool and the 2019 tool is different, causing the residual intensity graph of the non-invasive measurement results to be read differently. The 2017 data model is better applied to homogeneous data and the 2019 data model is better applied to heterogeneous data.
 
 Keywords: blood glucose levels, non-invasive tool, graph area summation, ordinal logistic regression, principal component
 analysis.

Using Logistic Regression Models and Artificial Neural Networks to Study the Factors Affecting the Academic Achievement of University Student

This study aimed to identify the most important factors affecting the academic achievement of a university student and then build a predictive model using these factors to predict the student's academic status. The ordinal logistic regression method was used to identify the influencing factors, and neural network technique to build the prediction model. It was applied to a sample of 188 students at Al-Yamamah University in the Kingdom of Saudi Arabia. The analyzed data were collected using a questionnaire of three main axes: representing student related factors, family, and academic factors. The results of the logistic regression model showed that 10 variables had a significant effect on the student's academic achievement: age, cumulative average at high school, the distance of residence, work besides studying, daily study hours, frequent absences, fear of exams, the economic level of the family, parents divorce, and family follow-up. As for the MLP network model with architecture (18-28-3), whose inputs were the significant variables chosen by the logistic model, the values of all performance indicators were supportive of the model's quality and predictive ability, where the correct classification rate values in the training data reached 96%, validity 93% and testing 90%. The results of the classification table were also high, with the correct classification of the three categories of the target variable: 100% good, 91.95% fair, 82.35% poor. The total percentage of correct classification was 94.68%. All these indicators confirm the validity and feasibility of using this model as an initial tool for predicting the academic status of a university student.

Statistical Analysis of Ordinal Response Variable: A Comparative Study

Response variables in biological phenomena vary between three types: numerical response variables, ordinal categorical response variables, and nominal categorical response variables. In statistical studies, handling ordinal variables varies in accordance with the perspective of the statistical approach to the response variable. Ordinal variables can be adopted as nominal categorical variables, which neglect the ordinal property of the categories. Ordinal variables can also be treated. as an ordinal categorical variable (discrete variable), in which case the ranking information can be utilized in establishing the predicted models. In this study, the most important statistical methods that can be used to analyze data with an ordinal response variable have been investigated. Among these methods are the Multiple Regression Method, and The Ordinal Logistic Regression Method. The mechanism of building models and parameter estimations were theoretically exhibited, as well as reading the statistical significance of the regression coefficients in all the models in the study. The application was carried out on a real sample of patients with osteoporosis. Where multiple models were built to determine the most important factors affecting the likelihood of developing the disease. The best model was diagnosed according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The results of the statistical analysis demonstrated the superiority of the ordinal logistic regression model over the multiple linear regression model in its explanation of the relationship between the response variable and the covariates.

Do smokers' harm perceptions of cigarillos differ by modified use of the tobacco product? Findings from waves 3 and 4 of the PATH study.

The harm perceptions of individuals who smoke either blunts or the Black & Mild (B&M) brand, which is often "freaked," have seldom been investigated. Since these practices could affect users' perceived health risks of the cigarillo, this study was intended to compare such risks among cigarillo users by modified use of the tobacco product. Adult cigarillo and blunt smokers were selected from the Population Assessment of Tobacco and Health Study for cross-sectional (Wave 4; n = 3,331) and longitudinal (Waves 3-4; n = 1,898) analyses of predictors of general and relative harm perceptions of cigarillos. Ordinal and multinomial logistic regression methods were employed for testing whether adjusted odds of perceiving cigarillo harms were greater in blunt versus cigarillo smokers. In a separate model (n = 1,258), preference for B&M cigarillos was examined as a correlate of harm perception due to the perceived health benefits of removing the inner tobacco liner. Cross-sectional estimates indicated that blunt smokers, mixed cigarillo/blunt smokers, and those who preferred B&M brand perceived significantly more risk than conventional cigarillo smokers. Longitudinal estimates indicated that among those who underwent a transition in use, former blunt and former cigarillo smokers had significantly greater odds of reporting an increase in perceived harm compared to new users. The change in blunt smokers' harm perceptions upon transitioning in blunt use suggests acknowledgment of the harm of using cigarillos in some form. Given their high perceived risk of cigarillos, blunt smokers might be receptive to replacing the tobacco product with a nontobacco wrap. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Комплексний аналіз сироваткової концентрації матриксної металопротеїнази 1 та тканинного інгібітора металопротеїнази 1 у дітей дошкільного віку з рекурентними респіраторними інфекціями

Purpose - to systematically examine the state of interrelatedness between the serum concentration of matrix metalloproteinase 1 and the tissue inhibitor of metalloproteinase 1, the frequency of acute respiratory infections (ARIs), and a heterogeneous set of other clinical and laboratory parameters in preschool children. Materials and methods. Forty children (21 boy and 33 girls) aged 1 to 6 years old, undergoing inpatient treatment on acute respiratory infection, were involved in the clinical study. They were studied serum concentrations of MMP-1 and TIMP-1, C-reactive protein, certain blood leukocyte fractions, erythrocyte sedimentation rate, the content of several macro- and microbiological elements in their hair as well as a number of clinical signs. Also, two integral indicators of ARIs recurrence were calculated, namely, the infectious index (InI) and resistance index, as well as integral indices of inflammation and dolichostenomelia. The statistical processing of the primary digital material obtained was performed by IBM SPSS Statistics 27 licensed program. Results. The younger children had higher serum MMP-1 concentrations (p=0.007) and MMP-1/TIMP-1 ratio (p=0.008) compared to the older children. The girls showed higher values of MMP-1 (p=0.008) and MMP-1/TIMP-1 ratio (p=0.012) than the boys. The strong positive correlation between MMP-1 and InI in the children (ρ=0.514; p=0.001; 95% CI [0.232; 0.716]) was demonstrated. Using ordinal logistic regression method, a significant and simultaneously multidirectional dependence of InI categorical index on MMP-1 (B=1.078; p=0.013; 95% CI [0.223; 1.933]) and the age category of the examined children (B=-1.942; 95% CI [-3.757; -0.126]) was established. Conclusions. It is assumed that more frequent ARIs in children are accompanied by higher levels of serum MMP-1. It is not unlikely that there is differential «tuning» of MMP-1 activity in response to varying frequency of ARIs during the previous year. The degree of influence of the predictor, namely MMP-1, in ordinal regression models is determined by both the significance of this predictor and the variant of its combination with other studied covariates. The study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the local ethics committees of the institutions mentioned in the paper. An informed parental consent was obtained for the study in children. No conflict of interests was declared by the authors.

Dietary Quality and Relationships with Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) among United States Adults, Results from NHANES 2017-2018.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a new definition for the evidence of hepatic steatosis and metabolic dysfunctions. The specific role of the dietary factors in the development and progress of the disease are not well illuminated. Thus, we conducted this study on the associations between dietary quality assessed by five dietary quality indexes (Dietary Inflammatory Index, DII; Mediterranean diet, MED; Dietary Approach to Stop Hypertension, DASH; Alternate Healthy Eating Index diet, AHEI; Healthy Eating Indices, HEI) and MAFLD phenotypes. This study was extracted from the latest NHANES 2017-2018 wave. Demographic information, health status, lifestyles, and dietary habits were reported in the questionnaire. Multivariate logistic regression and multivariate ordinal logistic regression methods were applied to explore the associations between dietary quality indexes and MAFLD or MAFLD with liver fibrosis. The weighted prevalence of Non-MAFLD, MAFLD without fibrosis, and MAFLD with fibrosis were 47.05%, 36.67%, and 16.28%, respectively, at the cutoff value of a median Controlled Attenuation Parameter (CAP) 248 dB/m and a median Liver Stiffness Measurement (LSM) 6.3 kPa. When the diagnostic cutoff values of CAP changed to 285 dB/m, the weighted prevalence of Non-MAFLD, MAFLD without liver fibrosis, and MAFLD with fibrosis turned to 64.62%, 22.08%, and 13.30%, respectively. All five dietary quality indexes, including DII, HEI-2015, AHEI, DASH, and MED, were all significantly associated with MAFLD phenotypes. DII was positively associated with MAFLD phenotypes, while other four dietary quality indexes, including HEI-2015, AHEI, DASH, and MED, were significantly associated with lower risk of MAFLD phenotypes. MAFLD is becoming a threatening public health concern among adult Americans and dietary quality is markedly associated with MAFLD phenotypes.

Sekizinci Sınıf Öğrencilerinin Fen Başarısı Üzerinde Etkili Olan Değişkenlerin Ordinal Lojistik Regresyon Yöntemiyle İncelenmesi

The aim of this study was to investigate the factors affecting the science success of eighth-grade students using the Ordinal Logistic Regression method. In this study, information collected from a total of 4224 students, 2202 (52.1%) male and 2022 (47.9%) female, who participated in the ABIDE 2016 application was used. The scores obtained from the science course of the students entering the ABIDE 2016 application were transformed into categorical ones according to the determined threshold values. In the analysis, the categorical science success score was used as the predicted variable, and 15 variables that were thought to affect science success were used as predictive variables. The data obtained later were examined by the Ordinal Logistic Regression method. As a result of the examination, the factors affecting science success were determined as peer bullying, perceived self-efficiency, value given to the course, parental attention, family pressure, father’s educational status, mother's educational status, monthly income, having a computer or tablet, having a room of one's own, student's educational goal and participation in science support courses. The findings were found to be remarkably similar to those found in the literature. This indicates that the OLR method is quite effective in predicting training data. Such research is expected to be handled objectively and without bias.

Investigation on typical occupant behavior in air-conditioned office buildings for South China’s Pearl River Delta

The excessive simplification of occupant behavior is considered as the most important factor that affects the uncertainty of building performance simulation, thus affects the reliability and generalizability of simulation-based design and forecast. In this paper, occupant behavior in air-conditioned office buildings of the Pearl River Delta (PRD) region was investigated and defined. Copies of 873 questionnaires about the occupant behavior in air-conditioned office buildings in the PRD region were collected to study the relationship between indoor environment quality and adaptive behaviors. Eight typical office occupant schedules were defined via K-means clustering method. A probability prediction model of cooling temperature set-point was established by using the Ordinal Logistic Regression method. According to the different control modes of air conditioning, window, blind and lighting equipment, four types of typical behavior patterns were proposed using the K-prototype clustering method, which could be developed into 20 typical occupant behavior styles of office buildings in the PRD region.

THE IMPACT OF THE SOFT TERRORISM CONCEPT ON KOREANS AND JAPANESE

The development of social media and Internet technologies has increased the speed of news, and people have gained immediate access to information on many issues. However, it has also affected terrorist organizations by changing their methods, strategies, and visions. With these new methods and strategies, today’s terrorist organizations negatively affect the psychology of South Koreans and Japanese, who are not targets of global terrorism. Recent studies have shown that a significant proportion of Japanese and South Koreans are worried about terrorism despite their limited history of terrorism compared with the rest of the world. This research suggests that the fear of terrorism in these countries could be explained using the concept of soft terrorism. This study performed a quantitative analysis of the seventh wave of the World Values Survey using the ordinal logistic regression (OLR) method, finding that the fear of terrorism is attributed to active media sources such as the Internet for South Koreans and passive media sources such as social media for the Japanese.