scholarly journals Pemodelan Determinan Pernikahan Dini di Daerah Pedesaan dengan Pendekatan Regresi Logistik Biner

2021 ◽  
Vol 4 (2) ◽  
pp. 76
Author(s):  
Aloysius Bela Boro ◽  
Siskarossa Ika Oktora
Keyword(s):  
Logistic Regression ◽  
Rural Areas ◽  
Goodness Of Fit ◽  
Binary Logistic Regression ◽  
Wealth Index ◽  
Early Marriage ◽  
Negative Effects ◽  
Response Variable ◽  
Study Results ◽  
The Government

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> The behavior of early marriage in Indonesia is still high and most prevalent in rural areas. In addition to violating the law, a marriage performed before reaching 19 years also has many negative effects. One of them is the death of the mother and the baby. Using data from the Demographic and Health Survey 2017, this study aims to analyze the determinants of early marriages in rural areas in Indonesia. The response variable used is binary categorical data, namely the status of early marriage and not early marriage, so we use a binary logistic regression. The steps performed on this model include estimates of parameters, parameter testing either simultaneously or partially, and a test of the goodness of fit. The results show that the variables of education level, internet access, and wealth index significantly affected early marriage status in rural areas in Indonesia in 2017. Based on the goodness of fit result, this model is proper for modeling early marriage behavior in Indonesia. The study results can be used as a reference for the government in formulating policies to overcome the problem of early marriage in rural areas in Indonesia.</p><p> <strong>Keywords</strong><strong>: </strong>early marriage, rural area, categorical response variable, binary logistic regression</p>

scholarly journals Bivariate binary analysis on composite index of anthropometric failure of under-five children and household wealth-index

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Demeke Lakew Workie ◽  
Lijalem Melie Tesfaw
Keyword(s):  
Logistic Regression ◽  
Composite Index ◽  
Binary Logistic Regression ◽  
Wealth Index ◽  
Household Wealth ◽  
Education Level ◽  
Multiple Birth ◽  
Under Five ◽  
Under Five Children ◽  
Study Results

Abstract Background Malnutrition is the most common cause of mortality and morbidity of children in low and middle income countries including Ethiopia and household wealth index shares the highest contribution. Thus, in this study it is aimed to conduct bivariate binary logistic regression analysis by accounting the possible dependency of child composite index anthropometric failure and household wealth index. Methods In this study the data from Ethiopian Demographic and Health Survey (EDHS) 2016 involved 9411 under five children was considered. Child Composite Index Anthropometric Failure (CIAF) measures the aggregate child undernourished derived from the conventional anthropometric indices (stunting, underweight and wasting). The correlation between CIAF and wealth index was checked and significant correlation found. To address the dependency between the two outcome variables bivariate binary logistic regression was used to analyze the determinants of child CAIF and household wealth index jointly. Results Study results show that region, place of residence, religion, education level of women and husband/partner, sex of child, source of drinking water, household size and number of under five children in the household, mothers body mass index, multiple birth and anemia level of child had significant association with child CIAF. Female children were 0.82 times less likely to be CIAF compared to male and multiple birth children were more likely to be CIAF compared to single birth. Children from Oromia, Somalie, Gambela, SNNPR, Harari and Addis Ababa region were 0.6, 0.56, 0.67, 0.52, 0.6 and 0.44 times less likely to be CIAF compared to Tigray. A household from rural area were 15.49 times more likely poor compared to a household. The estimated odds of children whose mothers attended primary, and secondary and higher education was 0.82, and 0.52 times respectively the estimated odds of children from mothers who had never attended formal education. Conclusion The prevalence of children with composite index anthropometric failure was high and closely tied with the household wealth index. Among the determinants, region, religion, family education level, and anemia level of child were statistically significant determinants of both CIAF and household wealth index. Thus, the authors recommend to concerned bodies and policymakers work on household wealth index to reduce the prevalence of child composite anthropometric failure.

scholarly journals Binary logistic regression to estimate household income efficiency. (south Darfur rural areas-Sudan)

2016 ◽  
Vol 4 (1) ◽  
pp. 31
Author(s):  
Sofian A. A. Saad ◽  
Amin Adam ◽  
Afra H. Abdelateef
Keyword(s):  
Logistic Regression ◽  
Rural Areas ◽  
Household Income ◽  
Household Head ◽  
Binary Logistic Regression ◽  
Statistical Technique ◽  
Household Expenditure ◽  
Response Variable ◽  
Explanatory Variables ◽  
Main Factors

<p>The main objective behind this study is to find out the main factors that affects the efficiency of household income in Darfur rejoin. The statistical technique of the binary logistic regression has been used to test if there is a significant effect of fife binary explanatory variables against the response variable (income efficiency); sample of size 136 household head is gathered from the relevant population. The outcomes of the study showed that; there is a significant effect of the level of household expenditure on the efficiency of income, beside the size of household also has significant effect on the response variable, the remaining explanatory variables showed no significant effects, those are (household head education level, size of household head own agricultural and numbers of students at school).</p>

scholarly journals Predictors of iron consumption for at least 90 days during pregnancy: Findings from National Demographic Health Survey, Pakistan (2017–2018)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sumera Aziz Ali ◽  
Savera Aziz Ali ◽  
Shama Razzaq ◽  
Nayab Khowaja ◽  
Sarah Gutkind ◽  
...  
Keyword(s):  
Health Survey ◽  
Rural Areas ◽  
Iron Supplementation ◽  
Wealth Index ◽  
Prevalence Ratio ◽  
Deficiency Anemia ◽  
Multivariable Logistic Regression Analysis ◽  
Demographic Health Survey ◽  
Sampling Weights ◽  
The Government

Abstract Background Iron supplementation is considered an imperative strategy for anemia prevention and control during pregnancy in Pakistan. Although there is some evidence on the predictors of iron deficiency anemia among Pakistani women, there is a very limited understanding of factors associated with iron consumption among Pakistani pregnant women. Thus, this study aimed to investigate the predictors of iron consumption for at least ≥90 days during pregnancy in Pakistan. Methods We analyzed dataset from the nationally representative Pakistan Demographic Health Survey 2017–2018. The primary outcome of the current study was the consumption of iron supplementation for ≥90 days during the pregnancy of the last birth. Women who had last childbirth 5 years before the survey and who responded to the question of iron intake were included in the final analysis (n = 6370). We analyzed the data that accounted for complex sampling design by including clusters, strata, and sampling weights. Results Around 30% of the women reported consumed iron tablets for ≥90 days during their last pregnancy. In the multivariable logistic regression analysis, we found that factors such as women’s age (≥ 25 years) (adjusted prevalence ratio (aPR) = 1.52; 95% CI: 1.42–1.62)], wealth index (rich/richest) (aPR = 1.25; [95% CI: 1.18–1.33]), primary education (aPR = 1.33; [95% CI: 1.24–1.43), secondary education (aPR = 1.34; [95% CI: 1.26–1.43), higher education (aPR = 2.13; [95% CI: 1.97–2.30), women’s say in choosing husband (aPR = 1.68; [95% CI: 1.57–1.80]), ≥ five antenatal care visits (aPR =2.65; [95% CI (2.43–2.89]), history of the last Caesarian-section (aPR = 1.29; [95% CI: 1.23–1.36]) were significantly associated with iron consumption for ≥90 days. Conclusion These findings demonstrate complex predictors of iron consumption during pregnancy in Pakistan. There is a need to increase the number of ANC visits and the government should take necessary steps to improve access to iron supplements by targeting disadvantaged and vulnerable women who are younger, less educated, poor, and living in rural areas.

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.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

scholarly journals Application of Connectionist Approach in Classification of Nutritional Status Among Arsenic Affected people in Rural Areas in Bangladesh

2014 ◽  
Vol 5 (2) ◽  
pp. 23-29
Author(s):  
A Rahman ◽  
M Akter ◽  
AK Majumder
Keyword(s):  
Neural Network ◽  
Body Mass Index ◽  
Logistic Regression ◽  
Nutritional Status ◽  
Body Mass ◽  
Clinical Data ◽  
Predictive Models ◽  
Rural Areas ◽  
Binary Logistic Regression ◽  
Connectionist Approach

Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to explore and compare the process of constructing common predictive models. Models based on an artificial neural network (the connectionist approach) and binary logistic regressions were compared in their ability to classifying malnourished subjects and those with over-weighted participants in rural areas of Bangladesh. Subjects were classified according to the indicator of nutritional status measured by body mass index (BMI). This study also investigated the effects of different factors on the BMI level of a sample population of 460 adults of six villages in Bangladesh. Demographic, enthropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460(140 male and 320 females) participants 186(40.44%) were identified as malnourished (BMI<18.5 gm), and the remainder 274(59.56%) were found as over-weighted (BMI>18.5 gm). Among other factors, arsenic exposures were found as significant risk factors for low body mass index (BMI) with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 72.85% of cases with malnourished in the training datasets, 76.08% in the testing datasets and 75.26% of all subjects. The sensitivities of the neural network architecture for the training and testing datasets and for all subjects were 84.28%, 84.78% and 81.72% respectively, indicate better performance than binary logistic regression model. DOI: http://dx.doi.org/10.3329/akmmcj.v5i2.21128 Anwer Khan Modern Medical College Journal Vol. 5, No. 2: July 2014, Pages 23-29

scholarly journals Modelling of Risk Factors of Diabetes in United Arab Emirates (UAE)

2015 ◽  
Vol 1 (2) ◽  
pp. 28
Author(s):  
Suheer Haroun
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Blood Pressure ◽  
Logistic Regression ◽  
Blood Glucose ◽  
United Arab Emirates ◽  
Binary Logistic Regression ◽  
Sample Survey ◽  
Fresh Vegetables ◽  
Study Results

Objectives: The aim of this study was to model and determine factors influencing the risk of diabetes mellitus (DM) in the United Arab Emirates and to analyze data related to the topic. Methods: The study was carried out in UAE, using a questionnaire to out-patients in a medical clinic that contained socio-demographic characteristics and risk factors were used for data collection. Sample survey data analyzed using descriptive techniques, correlations, and binary logistic regression models. Binary logistic regression were performed to find the crude and adjusted odds ratio (OR) and 95% confidence interval (CI) was calculated to find the significance of the observed OR. A p-value ≤ 0.05 was considered statistically significant All Analysis was performed using SPSS and Microsoft excels. Results: study results showed that six main factors influence the risk of diabetes in UAE, which are, blood glucose, blood pressure, physical activity, waist size, gender and family history of diabetes. Marital status, smoking, and intake of fresh vegetables and fruits did not show any statistically significant association with risk of diabetes in UAE. Blood glucose is observed as the most statistically significant factor (for every one unit increase in blood glucose, the study expect a 5.422 increase in the risk of developing diabetes), at the meantime gender observed as the lowest statistically significant factor (if the respondent is male the probability of being diabetic is 0.809 percent) holding all other independent variables constant. Conclusion: Results of the present study will be one of use in planning primordial, primary and secondary measures of prevention at the community. Encouraging physical activity, controlling blood pressure and blood glucose may significantly decrease the risk of diabetes mortality; effective health education programs promoting regular exercise and effective advices may needed to reduce the burden of diabetes in UAE.

scholarly journals Levels and Determinants of Complementary Feeding Pattern Exclusive of Minimum Meal Frequency and Dietary Diversity among Children of 6 to 23 Months in Bangladesh

2020 ◽  
Vol 9 (3) ◽  
pp. 28-44
Author(s):  
Naznin Pervin ◽  
Darryl Macer ◽  
Shamima P. Lasker
Keyword(s):  
Logistic Regression ◽  
Rural Areas ◽  
Complementary Feeding ◽  
Dietary Diversity ◽  
Secondary Data ◽  
Feeding Pattern ◽  
Multivariable Logistic Regression Analysis ◽  
Multivariable Logistic Regression ◽  
Effective Interventions ◽  
The Government

Objective: To estimate the level of complementary feeding pattern (CFP) among children aged between 6 to 23 months and to identify the determinants in individual, household and community level in Bangladesh. Methods: From secondary data of Bangladesh Demographic Health Survey (BDHS) 2011 was used in this study. A total of 2,373 children aged between 6 to 23 months were selected. To estimate the level of CFP dimension index and the “score of the index” was used as dependent variables. Statistical analyses and tests were guided by the nature of the variables. Multivariable logistic regression analyses were performed to identify the significant determinants of CFP. Results: The overall level of CFP among children aged between 6 to 23 months was low. More than 95% of the children experienced inadequate (92.7%) CFP level. The mean levels of CFP as well as percentages of no or inadequate (94.1%) CFP were significantly lower among children of the youngest age group (06 months), uneducated parents, unemployed/laborer fathers, socio-economically poor families, food insecure families and rural areas. However, only few variables remained significant for adequate CFP in the multivariable logistic regression analysis. Adequate CFP was significantly lower among the children aged between 6 to 23 months (OR: 0.22, 95% CI: 0.10-0.47), children of illiterate fathers (OR: 0.32, 95% CI: 0.11-0.95) and socio-economically middle-class families (OR: 0.28, 95% CI: 0.09-0.86) as compared to their reference categories. Conclusion: Inappropriate and inadequate CFP may cause serious health hazards among children of 6 to 23 months in Bangladesh. It is ethical to take effective interventions and strategies by the government and other concerned stakeholders to improve the overall situation of CFP in Bangladesh.

scholarly journals ANALISIS FAKTOR YANG MEMPENGARUHI STATUS PENERIMAAN BERAS KELUARGA MISKIN MENGGUNAKAN REGRESI LOGISTIK BINER DI KECAMATAN LANGSA BARAT

2020 ◽  
Vol 14 (2) ◽  
pp. 181-192
Author(s):  
Amelia Amelia ◽  
Fitra Mulyani ◽  
Ulya Nabilla
Keyword(s):  
Logistic Regression ◽  
Economic Status ◽  
Secondary Data ◽  
Binary Logistic Regression ◽  
Primary Data ◽  
Binary Logistic Regression Analysis ◽  
Binary Logistic Regression Model ◽  
The Status ◽  
Rice Consumption ◽  
The Government

Poverty is an inability to meet basic needs measured by expenditure, including rice consumption. Based on data from the Central Statistics Agency (BPS), as much as 95% of Indonesia's population consumes rice as the main food, with an average rice consumption of 102 kg/person/ year (BPS, 2013). Furthermore, BPS stated that almost 1/4 of them or around 25.95 million people were included in the category of the poor population as of March 2018. So the government made a policy to tackle the problem through the program of giving poor family rice (Raskin), namely subsidized rice assistance to households poor. However, in the implementation of the Raskin program, there was a deviation of around 40% of Indonesia's population with a middle-upper social-economic status receiving Raskin and 12.5% ​​of the population with a socio-economic status upon receiving Raskin. Therefore this study aims to analyze the significant factors that affect the status of rice in poor families using binary logistic regression analysis. The location of the study was conducted in the District of West Langsa because the district was one of the districts receiving the most Raskin in the City of Langsa. The data used in this study are primary data and secondary data. The results of the analysis show that the factors that influence Raskin's acceptance status are the level of education, type of floor, type of fuel, expenditure for food, and frequency of purchasing new clothes. The binary logistic regression model obtained is

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