scholarly journals Gender and Age Differences in the Study Plan of University Students

2020 ◽  
Vol 14 (01) ◽  
pp. 62 ◽  
Author(s):  
Hilary I Okagbue ◽  
Sheila A Bishop ◽  
Anjoreoluwa E Boluwajoko ◽  
Adaeze M Ezenkwe ◽  
Glory N Anene ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Undergraduate Students ◽  
Logistic Regression Model ◽  
Simple Random Sampling ◽  
Chi Square ◽  
Ogun State ◽  
Gender And Age ◽  
Study Plan

<p class="0abstract">Effective study plan is a predictor of good academic performance. However, there are few evidences available on the role of gender and age in the study plan for students. This paper investigated the role of gender and age in the adoption of study plan that can guarantee success. A questionnaire was designed and administered to undergraduate students of a world class privately funded university located in Ogun State, Nigeria. Simple random sampling was used and 294 students responded. Chi-square test of independence revealed that gender and age are not associated with frequency of study, study environment, study content preferences and study motivation. There is no Gender difference in the preference of study type, factors that drive, motivation for study and satisfaction with the study plan whereas, age is significantly associated. The logistic regression model was significant and correctly classified 66.3% of satisfaction with the study plan. Gender was not significant and age of students can predict their satisfaction with their study plan. Older students have more odds to be satisfied with their study plan. As students progressed from year one to the final year, they tend to adopt a study plan that can help them obtain high grades and graduate with good result. Artificial Neural Network correctly classified 71.4% of satisfaction using only age as the only factor because, only age contributed significantly to the logistic regression model. Timely academic advising or mentorship is advocated especially for freshers.</p>

A STUDY ON PREVALENCE OF POSTPARTUM DEPRESSION AT TERTIARY HOSPITAL, WEST BENGAL.

2021 ◽  
pp. 33-36
Author(s):  
Chandrima Maity ◽  
Debasish Sanyal ◽  
Arati Biswas ◽  
Sudarsan Saha
Keyword(s):  
Logistic Regression ◽  
Postpartum Depression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Depression Scale ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Background Information ◽  
Stepwise Logistic Regression ◽  
Postpartum Mothers

The investigators assessed the prevalence of Postpartum Depression (PPD), its clinical features and relationship of PPD with socio-demographical and obstetrical factors. The samples were selected from the OPD and IPD, of a Medical college in Kolkata.. Observational study was performed on 500(N=500) postpartum mothers who were selected by using Simple Random Sampling Technique within the six weeks of postpartum period. Data were collected by using the Structured Questionnaire for background information, Edinburgh Postnatal Depression Scale (Bengali Version of EPDS) for postpartum depression. Data analysis was performed using Descriptive Statistics, Chi-square, Logistic Regression and Decision Tree. A total of 112 (Prevalence Rate 22.4%) postpartum mothers had PPD. Stepwise logistic regression model correctly classied 92.2% of women who developed PPD. Using logistic regression model, postpartum depression is best predicted by: No. of Postpartum days p< 0.001***, Age of the mother p<0.024**, Religion p<0.003**, Type of family p<0.020**, Education of the mother p<0.001***, Monthly Income of the family p<0.001***, No of other living children p<0.001***, Pregnancy outcome p<0.033**, Any complication during pregnancy / delivery/ postpartum p< 0.001*** and Problems with family members p< 0.001***. The study recommends that evaluation should be carried out for Postpartum Depression and its risk factors to prevent and treat PPD in a timely manner.

scholarly journals Effects of the Agri-environmental Program Implemented in Sultan Reeds Area of Kayseri, Turkey

2019 ◽  
Vol 7 (12) ◽  
Author(s):  
Ismet Boz
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Agricultural Practices ◽  
Farm Size ◽  
Chi Square ◽  
Factors Affecting ◽  
Environmental Program ◽  
Chi Square Test ◽  
Sustainable Agricultural Practices

This study was initiated to evaluate the effects of agri-environment program implemented in the Sultan reeds area of Kayseri province, Turkey. The specific objectives of the study were to compare the farmers who enrolled in the program with those who didn’t enroll regarding their application of different sustainable agricultural practices, and to determine factors affecting their enrolment in the program. The main comparative indicators were selected from different sustainable agricultural practices either promoted by the agri-environmental program or not promoted but considered very useful for the locality. Two stratified samples of farmers (enrolled and not enrolled) were selected based on their farm size. Chi-square tests of independence were used to compare farmers on the selected sustainable agricultural practices. Logistic regression model was used to determine factors affecting the enrolment of the agri-environment program. The findings of the chi-square test showed that enrolled farmers use grow more forage legumes, are more conscious about pesticides use and chemical applications, and they use more pressurized irrigation systems. Findings of the logistic regression model sowed that using rental land negatively, but contacts with extension personnel, and using long term loans for farming investments positively influenced the enrolment of the agri-environment program. Governmental effort must concentrate on these issues when promoting agri-environmental programs in the region.

Evaluating negative attitudes of the students and shoppers towards halal cosmetics products

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Mominul Islam
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Chi Square ◽  
Negative Attitudes ◽  
Content Type ◽  
Animal Fat ◽  
Chi Square Test

Purpose This study aims to reveal how consumers and shoppers are negative toward alcohol, animal fat, producers and certification issues concerned with halal cosmetics products. Design/methodology/approach In total, 527 students of 4 public universities and a medical college across Bangladesh took part in a survey and 150 shoppers from 2 cities participated in the face to face interview with the structured questionnaires. Frequency distribution was used for categorical and numerical data, and the chi-square test with a binary logistic regression model has tested the association between gender and attitudes toward halal cosmetics. Besides, narratives of Sharīʿah regarding alcohol, meat, fat and halal certification have helped understand the halal issue. Findings In total, 83% of the respondents perceived negative attitudes against haram animal fat followed by alcohol (74%) and animal fat (64%). The chi-square test shows that consumers held a significant association toward haram animal fat, (p-value 0.000) alcohol, (p-value 0.000) non-Muslim producers (p-value 0.000) and non-Muslim countries (p-value 0.026). Imperatively, the binary logistic regression model has found a significant negative association to haram animal fat (ß2 −0.295) and alcohol (ß1 −0.200). Practical implications Marketers ought to avoid haram animal fat in halal cosmetics besides focusing on alcohol freeness. Also, non-Muslim marketers need to be extra cautious in showcasing their identities. However, Islamic marketers will enjoy a competitive advantage in the halal market because of their demographic factors. Social implications Islamic principles on alcohol, meat, fat and certification potentially can help other stakeholders sense the halal norms. Originality/value This study has blended the elements of Sharīʿah with empirical evidence to shed light on the fundamental and trust factors for the marketing of halal cosmetics products.

An improved procedure for estimation of malignant breast cancer prevalence using partially rank ordered set samples with multiple concomitants

2015 ◽  
Vol 26 (6) ◽  
pp. 2552-2566 ◽  
Author(s):  
Armin Hatefi ◽  
Mohammad Jafari Jozani
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Ordered Set ◽  
Model Fitting ◽  
Simple Random Sampling ◽  
Breast Cancer Study ◽  
Training Samples ◽  
Malignant Breast

Rank-based sampling designs are widely used in situations where measuring the variable of interest is costly but a small number of sampling units (set) can be easily ranked prior to taking the final measurements on them and this can be done at little cost. When the variable of interest is binary, a common approach for ranking the sampling units is to estimate the probabilities of success through a logistic regression model. However, this requires training samples for model fitting. Also, in this approach once a sampling unit has been measured, the extra rank information obtained in the ranking process is not used further in the estimation process. To address these issues, in this paper, we propose to use the partially rank-ordered set sampling design with multiple concomitants. In this approach, instead of fitting a logistic regression model, a soft ranking technique is employed to obtain a vector of weights for each measured unit that represents the probability or the degree of belief associated with its rank among a small set of sampling units. We construct an estimator which combines the rank information and the observed partially rank-ordered set measurements themselves. The proposed methodology is applied to a breast cancer study to estimate the proportion of patients with malignant (cancerous) breast tumours in a given population. Through extensive numerical studies, the performance of the estimator is evaluated under various concomitants with different ranking potentials (i.e. good, intermediate and bad) and tie structures among the ranks. We show that the precision of the partially rank-ordered set estimator is better than its counterparts under simple random sampling and ranked set sampling designs and, hence, the sample size required to achieve a desired precision is reduced.

scholarly journals Detect mTBI in pediatric populations using a tablet-based software BrainCheck

2020 ◽  
Author(s):  
Siao Ye ◽  
Brian Ko ◽  
Huy Phi ◽  
David M. Eagleman ◽  
Benjamin Flores ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Brain Injury ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Neuropsychological Testing ◽  
Chi Square ◽  
Neurocognitive Test ◽  
Pediatric Populations ◽  
Pencil And Paper ◽  
And Control

AbstractDespite its high frequency of occurrence, mild traumatic brain injury (mTBI), or concussion, is difficult to recognize and diagnose, particularly in pediatric populations. Conventional methods to diagnose mTBI primarily rely on clinical questionnaires and sometimes include imaging such as computed tomography (CT) or pencil and paper neuropsychological testing. However, these methods are time consuming, require administration/interpretation from health professionals, and lack adequate test sensitivity and specificity. We explore the use of BrainCheck, a computerized neurocognitive test that is available on iPad, iPhone or computer desktop, for mTBI assessment. The BrainCheck battery consists of 6 gamified traditional neurocognitive tests that assess areas of cognition vulnerable to mTBI such as attention, processing speed, executing functioning, and coordination. We administered BrainCheck to 10 participants diagnosed with mTBI at the emergency department (ED) of Children’s hospital within 96 hours of admittance to the ED, and 126 normal controls at a local high school. Statistical analysis included Chi-Square tests, Analysis of Variance (ANOVA), independent sample t-tests, and Hochberg tests to examine differences between mTBI, diagnoses by current gold standard clinical exam, and control groups on each assessment in the battery. Significant metrics from these assessments were used to build a logistic regression model that distinguishes mTBI from non-mTBI participants. Receiver operator score (ROC) analysis of our logistic regression model found a sensitivity of 84% and specificity of 80%. BrainCheck has potential in distinguishing mTBI from non-mTBI participants, by providing a shorter, gamified test battery to assess cognitive function after brain injury, while also providing a method for tracking recovery with the opportunity to do so remotely from a patient’s home.

Investigating the Role of Opening Partners While Chasing on the Outcome of Twenty20 Cricket Matches

2020 ◽  
Vol 45 (2) ◽  
pp. 222-232
Author(s):  
Priyanka Talukdar
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
The Other ◽  
Explanatory Variables ◽  
Pressure Index ◽  
Other Hand

In cricket, irrespective of the format of the game, batting always happens in pairs. The two batsmen who bat together are called as batting partners. The pair of batsmen who come to bat at the beginning of any innings are called opening batsmen or opening partners. In Twenty20 cricket, the opening partners must start their innings with a definite strategy. In one hand, they have the advantage of only two fielders outside the 30-yard circle for the first six overs (technically called as the powerplay overs), and so both openers are expected to play high scoring shots and attempt to score runs quickly. On the other hand, the odds against them are the ball is new, so is the pitch and the bowlers are fresh and energetic. When any one of the opening batsmen loses his wicket, the partnership comes to an end. This study tries to figure out the influence of the opening partnership of the second innings on the outcome of Twenty20 matches. Pressure Index (developed by earlier researchers), effects of venue or ground and target score are used as explanatory variables in the logistic regression model to check if the performance of opening partnership influences the outcome of Twenty20 matches along with other variables. The data used for the exercise is from Twenty20 international cricket matches played within the period January 2012 to June 2018. The study finds that opening partnership while chasing is a significant factor in deciding the match outcome during the run chase for the said dataset. Also, the best opening batting partners have been identified.

scholarly journals Sociodemographic Determinants of Marital Satisfaction Among an Iranian Population

2021 ◽  
Vol 11 (3) ◽  
pp. 183-190
Author(s):  
Farahnaz Bahrami ◽  
◽  
Akram Kharazmi ◽  
Shahab Rezaeian ◽  
Ali Alami ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Marital Satisfaction ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Family Income ◽  
Economic Status ◽  
Simple Random Sampling ◽  
Cross Sectional Study ◽  
Satisfaction Scale ◽  
Cross Sectional

Background: There is a lack of Iranian studies on marital satisfaction, which is one of the important factors in the stability of marriage. Therefore, the present study aimed to evaluate the effects of sociodemographic variables on marital satisfaction. Methods: This analytical and cross-sectional study was conducted on 770 married people from Gonabad City, Northeast of Iran. The simple random sampling method was used. Also, the ENRICH marital satisfaction scale was used to measure marital satisfaction as a dependent variable. A cut-off score of 100 was considered for the scale, with higher scores indicating satisfaction. Moreover, the logistic regression model was used to examine the effects of the variables on marital satisfaction. Results: The Mean±SD age of the participants was 39.2±10.6 years. Most of the participants (75%) were categorized as satisfied, with a Mean±SD score of 113.9±26.8. Besides, both univariate and multiple regression analyses showed a strong significant relationship between economic status and marital satisfaction, ie, a better economic status increased the odds of marital satisfaction. After controlling other investigated variables, the logistic regression model showed that men are about two times more likely than women to be satisfied with their marriage (adjusted odds ratio=1.82). Conclusion: The present findings showed that family income and gender positively influence marital satisfaction. Accordingly, marital satisfaction and family solidarity could be enhanced by the provision of legal and social rules to ensure the equal right of men and women, as well as attempts to improve the economic status of the families.

scholarly journals The Determinant Factors of Muzakki Toward Paying Zakat, Infaq, Sedekah (ZIS) in Zakat Institutions

2020 ◽  
Vol 3 (2) ◽  
pp. 143
Author(s):  
Hening Pratika Nila Hapsari ◽  
Unggul Priyadi
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Logistic Regression Analysis ◽  
Logistic Regression Model ◽  
Primary Data ◽  
Role Of Government ◽  
Financial Accountability ◽  
Objective Study

Introductions to The Problem: Zakat is one of worship which is often mentioned in the Al Quran. It's just that the potential for Zakat, Infaq, Alms (ZIS) is not comparable to the actual actual figures. Many factors influence muzakki in paying ZIS.Purpose/Objective Study: This study aims to analyze the factors that influence muzakki to pay ZIS in zakat institutions, namely Yatim MandiriDesign/ Methodology/ Approach: The sample in this study amounted to 200 respondents. LAZ Yatim Mandiri was chosen because it is an Amil Zakat Institution that is consistent in collecting ZIS funds from the smallest amount to the large amount. This study uses logistic regression analysis and the data used are primary data. Based on the analysis that has been done, it is found that 61% results can be predicted correctly in the logistic regression model in this study.Findings: The consistency of muzakki in paying ZIS at the Yatim Mandiri Amil Zakat Institution is influenced by the variables of religiosity, income, trust, shariah compliance, knowledge, justice, data publication, financial accountability, motivation, the role of ulama, the role of government. And the consistency of muzakki in paying ZIS at the Yatim Mandiri Amil Zakat Institution is not influenced by the variables of shariah compliance and financial accountability.

scholarly journals Determinants that Associated with Traveling History of COVID-19 Patients in Ethiopia During Stay at Home State of Emergency

2021 ◽  
Author(s):  
Kindu Kebede Gebre ◽  
Million Wesenu Demissie
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Demographic Factors ◽  
Age Group ◽  
State Of Emergency ◽  
Corona Virus ◽  
Gender And Age ◽  
History Of ◽  
Socio Demographic Factors

Abstract Background: The recent outbreak of Novel Coronavirus (SARS-CoV-2) Disease (COVID-19) has put the world on alert and impacting societies around the world in an unprecedented manner. The main aims of this study was to investigate the association among the socio-demographic factors with traveling history of COVID-19 Patients in Ethiopia during stay at home state of emergency. Methods: A total of 162 respondents with COVID-19 during March 13, 2020 to May 6, 2020 in Ethiopia were used. Two sided chi-square test was used to test the association between the socio demographic factors among COVID-19 Patients. A log-complement logistic regression model was used to compute the health ratios (HR) and 95% confidence interval (CI) to measure the effect of those factors. Results: The data was analyzed using 162 patients of severe acute respiratory syndrome corona virus-2. An association was found between traveling history of COVID-19 infected patients and Gender (male vs female) [B =5.410, p<0.020] and Age group [a=13.082, p<0.004]. Log-complement logistic regression model showed that Gender and Age were significant factors associated to traveling history of COVID-19 Patients. Health ratio showed that increasing risk of traveling history for COVID-19 patients associated with higher number of males [ HR=0.5895, 95%CI: 0.4007-0.8672, P<0.0073] and Age group 18-39 years [HR=0.4139, 95%CI: 0.2385-0.7184, P<0.0017] on patients of COVID-19. Akaike information criteria with minimum value [AIC=1.2158] indicated that Log complement logistic regression model was fitted the data well for the similar dataset of patients’ with novel corona virus. Conclusions: Male Gender and Age group 18-39 years are significant socio-demographic factors associated to traveling history of patients with corona virus disease. Further socio-demographic investigations are required to better understand the extent of association with Gender and Age for effective intervention and fight this pandemic to preserve lives.

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