scholarly journals Epidemiology of Pathological Online Games Use and Its Effect on Anxiety and Insomnia in Chinese Ethnic Minorities Adolescents

2021 ◽  
Author(s):  
Qiaoyue Wei ◽  
Shengjie Zhang ◽  
Yuli Pan ◽  
Hong Hu ◽  
Fenglan Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Short Form ◽  
Binary Logistic Regression ◽  
Online Games ◽  
Binary Logistic Regression Analysis ◽  
Special Focus ◽  
Chi Square ◽  
Chi Square Analysis

Abstract Background: The growing popularity and frequency of online games use has resulted in a large number of studies reporting various mental problems associated with its abuse in adolescents. In this article, we examined the prevalence of pathological online games use (POGU) and explored the associations of the POGU with anxiety and insomnia symptoms with minority youth in China.Methods: 1494 students completed a questionnaire of Problematic Online Gaming Questionnaire Short-Form (POGQ-SF), Generalized Anxiety Condition iems (GAD-7), and the Athens Insomnia Scale (AIS). Chi-square and binary logistic regression analysis were used to explore the association between pathological online games and anxiety/insomnia. Results: 356 (23.83%) respondents reported they had pathological online games use. Chi-square analysis shows that gender, grade, marital status of parents and exercise situation were significantly associated with POGU. Binary logistic regression analysis showed that those who had POGU were at significantly higher risk for anxiety and insomnia, compared to those who without POGU.Conclusion: We found a high incidence of POGU and a positive association among anxiety, insomnia and POGU. Thus, special focus should be paid to those who have suffered POGU. And, it is worth addressing the adverse effects of POGU on anxiety and insomnia.

scholarly journals Age at First Conception and its Determinants of Ever-Married Women in Bangladesh

2021 ◽  
Vol 9 (08) ◽  
pp. 683-395
Author(s):  
Mohammad Manjur Alam
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Married Women ◽  
Binary Logistic Regression Analysis ◽  
Demographic And Health Surveys ◽  
Chi Square ◽  
Reproductive Process ◽  
Chi Square Tests

Age at first conception is an important part of the reproductive process that identifies the level of fecundability and indicative fertility during the early stages of marriage. This study explores the age at first conception and its determinants of ever-married women in Bangladesh by extracting the recent Bangladesh Demographic and Health Surveys (BDHS 2017) data. Descriptive statistics, ANOVA, chi-square tests, and binary logistic regression analysis were carried out to analyze data. Overall, the mean age at first conception of Bangladeshi women is 17.82 ± 3.29 years with significant variations by different socio-demographic, cultural, and behavioral factors. Bivariate analysis of chi-square tests shows that all the independent variables have a significant effect on age at first conception. The finding of the binary logistic regression analysis revealed that respondents' current age, age at first cohabitation, respondents' education, and partners' education are the most important determinants of age at first conception in Bangladesh. Moreover, secondary (OR= 0.743, 95% CI = 0.622- 0.886, P < 0.01) and higher educated women (OR= 0.838, 95% CI = 0.710- 0.989, P < 0.05) were respectively less likely to having first conception at age above 17.25 years, compared to no educated women. Thus, to increase the age at the first conception and reduce the level of fertility, it is necessary to increase the age at the first marriage of the respondents and the level of education for both partners.

scholarly journals Association Between Parental Understanding of Pitch Smart Guidelines and Youth Baseball Player Injuries

2019 ◽  
Vol 7 (5) ◽  
pp. 232596711984631
Author(s):  
Luke Zabawa ◽  
Jeremy A. Alland
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Cross Sectional ◽  
Baseball Players ◽  
Increased Risk ◽  
Youth Baseball ◽  
Chi Square Analysis

Background: Injuries continue to rise among youth baseball players despite extensive research into prevention and the availability of throwing guidelines such as Pitch Smart. More research is needed to understand whether adherence to the current guidelines decreases injuries. Purpose: To understand the degree to which parents are aware of the Pitch Smart guidelines, whether parents adhere to the guidelines, and whether adherence results in decreased injuries in youth baseball players. Study Design: Cross-sectional study. Methods: An anonymous, internet-based survey consisting of 44 items was distributed to parents of adolescent baseball players affiliated with various youth baseball organizations across the midwestern United States; 15 items on the survey served as assessment questions of the Pitch Smart guidelines. Absolute and percentage correct scores were calculated and compared by use of a Student t test. A chi-square analysis was used to compare discrete data. A binary logistic regression analysis was conducted to determine whether showcase participation predicted player injury. Results: A total of 853 parents completed the survey. The mean ± SD age of the players on whom parents reported was 11.37 ± 3.5 years (range, 6-20 years). Among the cohort, 422 players regularly pitched. Regarding Pitch Smart guidelines, the percentage of correct answers by parents was 55.44% ± 0.3% for a player with a reported injury history and 62.14% ± 0.2% for a player without an injury history ( P = .012). The number of correct answers was 8.03 ± 4.0 for the group with an injury history and 9.17 ± 3.2 for the group with no history of injury ( P = .004). Binary logistic regression analysis, which controlled for age, indicated that showcase participation ( P = .001, β = 1.043 ± 0.026, R2 = 0.178) was a significant predictor of player injury. Conclusion: Pitchers are at an increased risk of injury compared with nonpitchers. Parents who are knowledgeable about the Pitch Smart throwing guidelines and actively follow them are significantly less likely to have a child with an injury. Excessive showcase participation is predictive of player injury when the analysis controls for age.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

scholarly journals Predicting organizational performance from motivation in Oromia Seed Enterprise Bale branch

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Askalech Feyisa Jobira ◽  
Abdulnasir Abdulmelike Mohammed
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Intrinsic Motivation ◽  
Organizational Performance ◽  
Logistic Regression Analysis ◽  
Extrinsic Motivation ◽  
Binary Logistic Regression ◽  
Moderate Level ◽  
Binary Logistic Regression Analysis ◽  
Study Results

AbstractMotivation is one of the most researched yet crucial topics in academia from various perspectives. Despite this, researches show mixed results about the effect of extrinsic motivation on intrinsic motivation and organizational performance. Studies in Ethiopia also lack causal analysis and theoretical underpinning that made contributions from academia very little. Hence, this research is important to assess the effect of extrinsic motivation on intrinsic motivation and organizational performance from a cognitive evaluation theory perspective. The researchers adopted an explanatory research design with a quantitative approach. The entire 119 employees of the Oromia Seed Enterprise, Bale branch were included in the study to collect primary data through a close-ended questionnaire. The collected data was processed by SPSS software version 20. The relationship analysis was addressed by correlation and binary logistic regression analysis. Seen from extrinsic and intrinsic motivation aspects, the findings of the study showed that Oromia Seed Enterprise had a moderate level of organizational performance and a moderate level of employees’ motivation. The correlation analysis result indicated that employees’ extrinsic and intrinsic motivation had a positive relationship with organizational performance. The binary logistic regression analysis also indicated that extrinsic and intrinsic motivation had a positive and significant influence on organizational performance. However, the interaction effect of intrinsic and extrinsic motivation on organizational performance was not significant, implying the absence of influence when both intrinsic and extrinsic motivations happen at the same time. Finally, the study results have a theoretical contribution for compensating the lack of actual experience in the Ethiopian organization’s context. Equally, the understanding of the moderated relationship among the study variables may encourage Oromia Seed Enterprise and its managers to develop a practical motivation system, which entertains the complex interaction of motivation variables to improve organizational performance. In addition, studies of this nature can inform policymakers to strengthen an incentive system as well as other motivation veins in the Ethiopian public organizations.

scholarly journals Binary Logistic Regression Analysis of Variables That Influence Poverty in Central Java

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Sendi Nugraha Nurdiansah ◽  
Laelatul Khikmah
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Equation Model ◽  
Binary Logistic Regression Analysis ◽  
Poor People ◽  
Regional Government ◽  
Central Java ◽  
The Government

The phenomenon of poverty is a serious problem faced by almost every country in the world. This is because poverty can affect various aspects of people's lives. One of the causes of poverty is due to lack of income and assets to meet basic needs such as food, clothing, housing, health level and acceptable education. In addition, poverty occurs because of the powerlessness of society to get out of the problems it faces. The Central Java regional government incorporated poverty issues into the Regional Medium-Term Development Plan (RPJMD) because Central Java has a high number of poor people. This was done as an effort by the Central Java government to reduce poverty. Therefore, research is needed to find out the variables that most influence poverty in order to assist the government in developing the RPJMD. To find out what factors influence poverty in Central Java with the dichotomous categorical response variable, binary logistic regression analysis was used. The results showed that based on the analysis conducted did not obtain a logistic regression equation model because there were no significant parameters because there were no variables that had a sig value <0.05. Existing variables are Number of Population, Female Head of Household, Number of Children not in School, Number of Disabled Individuals, Number of Chronic Disease Individuals, Unemployment, Non-Electricity Lighting Sources, Unprotected Drinking Water Sources, Kerosene and Wood Cooking Fuels, Location Facilities Defecation (BAB) Not Available, so there are no variables that affect the level of poverty in Central Java Province.

scholarly journals Factors affecting internet use among university students in Sarawak, Malaysia: an empirical study

2020 ◽  
Vol 7 (3) ◽  
pp. 848
Author(s):  
M. Mizanur Rahman ◽  
M. Taha Arif ◽  
Fready Luke ◽  
Santha Letchumi ◽  
Fatin Nabila ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
University Students ◽  
Logistic Regression Analysis ◽  
Internet Use ◽  
Binary Logistic Regression ◽  
Problematic Internet Use ◽  
The Internet ◽  
Binary Logistic Regression Analysis ◽  
Factors Affecting

Background: The internet has become an indispensable tool for communication, academic research, information and entertainment. However, heavy users of the internet lead to less confidence in social skills and the tendency to be isolated. The study aimed to assess the pattern of internet use and factors affecting problematic internet use among university students.Methods: This cross-sectional study conducted among the students of a university in Sarawak, Malaysia. A multistage cluster sampling technique was adapted to select the participants. Data were collected from 463 students by self-administered questionnaire. Hierarchical binary logistic regression analysis was done to determine the potential factors for problematic internet use.Results: The mean age of the students was 22 years, with a standard deviation of 1.6 years. Two-fifths (61.8%) of the students had no problematic internet use. However, 35.4% had moderate and 2.8% had severe problematic internet use. Hierarchical binary logistic regression analysis found that age of the students, year of study, duration of daily internet use and use of social networking like Skype appeared to be potential predictors of problematic internet use (p<0.05).Conclusions: This study was conducted in only one university, thus did not depict the overall scenarios of the country. The implications of the findings are still worth noting in the process of designing internet addiction studies among university students. Overall, this study has unearthed some useful insights which can serve as a guide to more elaborate studies.

scholarly journals Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

2018 ◽  
Vol 46 (9) ◽  
pp. 3656-3664 ◽  
Author(s):  
Wenbo Xu ◽  
Yang Zhao ◽  
Shiyan Nian ◽  
Lei Feng ◽  
Xuejing Bai ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Density Lipoprotein ◽  
Binary Logistic Regression ◽  
Lipoprotein Cholesterol ◽  
Binary Logistic Regression Analysis ◽  
Confounding Factors ◽  
Analysis Strategies ◽  
Glutamyl Transferase

Objective To investigate the importance of controlling confounding factors during binary logistic regression analysis. Methods Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect bilirubin, and γ-glutamyl transferase. Associations between these indexes and CHD were assessed by logistic regression, and results were compared by using different analysis strategies. Results 1) Without controlling for confounding factors, 14 indexes were directly inputted in the analysis process, and 11 indexes were finally retained. A model was obtained with conflicting results. 2) According to the application conditions for logistic regression analysis, all 14 indexes were weighed according to their variances and the results of correlation analysis. Seven indexes were finally included in the model. The model was verified by receiver operating characteristic curve, with an area under the curve of 0.927. Conclusions When binary logistic regression analysis is used to evaluate the complex relationships between risk factors and CHD, strict control of confounding factors can improve the reliability and validity of the analysis.

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