Deciphering Links Between Sexual Violence and Castes in India

2021 ◽  
pp. 2455328X2110325
Author(s):  
Yogendra Musahar
Keyword(s):  
Regression Model ◽  
Sexual Violence ◽  
Economic Status ◽  
Family Health ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Wealth Index ◽  
Bivariate Analysis ◽  
Binary Logistic Regression Model ◽  
Explanatory Variables

The recent incident, the gang rape and murder of a 19-year-old woman in Hathras, a small village in Uttar Pradesh of India, once again sparks a debate on links between sexual violence and castes in India. This article aims to examine the links between sexual violence and castes in India. This study utilizes the national representative National Family Health Survey 4 (NFHS-4, 2015–16) data. A bivariate analysis was carried out to analyse the data. A binary logistic regression model was applied to predict the effect of explanatory variables, viz. type of place of residence, years of schooling complete, economic status in terms of wealth index and finally castes on predicted variable, i.e. sexual violence. The binary regression model indicates that there were links between sexual violence and castes. For secured and dignified life of women, caste-based sexual violence must be annihilated.

scholarly journals Diagnosing Multicollinearity of Logistic Regression Model

2019 ◽  
pp. 1-9 ◽  
Author(s):  
N. A. M. R. Senaviratna ◽  
T. M. J. A. Cooray
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Sample Size ◽  
Logistic Regression Model ◽  
Secondary Data ◽  
Binary Logistic Regression ◽  
Condition Index ◽  
Binary Logistic Regression Model ◽  
Correlated Variables ◽  
Explanatory Variables

One of the key problems arises in binary logistic regression model is that explanatory variables being considered for the logistic regression model are highly correlated among themselves. Multicollinearity will cause unstable estimates and inaccurate variances that affects confidence intervals and hypothesis tests. Aim of this was to discuss some diagnostic measurements to detect multicollinearity namely tolerance, Variance Inflation Factor (VIF), condition index and variance proportions. The adapted diagnostics are illustrated with data based on a study of road accidents. Secondary data used from 2014 to 2016 in this study were acquired from the Traffic Police headquarters, Colombo in Sri Lanka. The response variable is accident severity that consists of two levels particularly grievous and non-grievous. Multicolinearity is identified by correlation matrix, tolerance and VIF values and confirmed by condition index and variance proportions. The range of solutions available for logistic regression such as increasing sample size, dropping one of the correlated variables and combining variables into an index. It is safely concluded that without increasing sample size, to omit one of the correlated variables can reduce multicollinearity considerably.

scholarly journals Analisis Pengobatan Pasien Menggunakan Model Regresi Logistik Biner

2020 ◽  
Vol 6 (2) ◽  
pp. 92
Author(s):  
Krishna Krishna Prafidya Romantica
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Explanatory Variable ◽  
Binary Logistic Regression ◽  
Residential Areas ◽  
Binary Logistic Regression Model ◽  
Dummy Variables ◽  
Explanatory Variables ◽  
Patient Class

Researcher used patient data spread across two residential areas, namely sector 1 and sector2. The research data consisted of four explanatory variables, namely: the age of the patient, the class ofpatients found in the hospital, the patient’s area of residence, and the findings of the disease suffered by the patient. Class, sector, and disease variables are variables categorized into categories 0 and 1. The researcher considers the dummy variables discussed in the explanatory variable variables. Category 0 indicates that the sample does not meet the criteria in the category. Choosing, category 1 shows that the sample meets the criteria in the category. Next, the researcher will estimate the explanatory parameter variables and dummy variables, then do the partial test to get the parameter significance and model it using the Binary Logistic Regression Model. With the Logistic Regression Model, researcher will calculate the consideration of the patient’s recovery. This probability is used as

scholarly journals Factors That Can Trigger Depression

2021 ◽  
Vol 7 (2) ◽  
pp. 164-185
Author(s):  
Haydée Maria Correia da Batista ◽  
Andrea Borges Paim ◽  
Brenda Santos Siqueira ◽  
Nelson Francisco Favilla Ebecken ◽  
Ana Claudia Dias
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
National Health Survey ◽  
Machine Learning Techniques ◽  
Binary Logistic Regression Model ◽  
Explanatory Variables ◽  
Learning Techniques

According to data from the last National Health Survey (PNS), conducted in 2013 by the Brazilian Institute of Geography and Statistics (IBGE) in partnership with the Ministry of Health, 7.6% of people aged 18 and over received diagnosis of depression. Therefore, based on this research, the purpose of this study was to identify factors that may be relevant to a possible diagnosis of depression, using machine learning techniques. The binary logistic regression model was chosen as the machine learning technique, with progressive and regressive methods for selecting variables and a model built by the researcher, generating seven different models. The model’s performance evaluation was made by comparing some metrics such as Cox-Snell R2 and Nagelkerke R2, which presented remarkably close results. Based on these models, 37 explanatory variables were selected which were applied to a new logistic regression model. The results showed that some variables significantly increased the chance of a positive diagnosis of depression as well as some variables were indicative of a reduction in the chances of this diagnosis.

Credit constraints and Rural Migration: Evidence from Six Villages in Uttar Pradesh

2018 ◽  
Vol 15 (3) ◽  
pp. 389-398
Author(s):  
Ruchi Singh
Keyword(s):  
Credit Constraints ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Male Migration ◽  
Rural Economies ◽  
And Migration ◽  
The Relationship ◽  
The Empirical Analysis

Rural economies in developing countries are often characterized by credit constraints. Although few attempts have been made to understand the trends and patterns of male out-migration from Uttar Pradesh (UP), there is dearth of literature on the linkage between credit accessibility and male migration in rural Uttar Pradesh. The present study tries to fill this gap. The objective of this study is to assess the role of credit accessibility in determining rural male migration. A primary survey of 370 households was conducted in six villages of Jaunpur district in Uttar Pradesh. Simple statistical tools and a binary logistic regression model were used for analyzing the data. The result of the empirical analysis shows that various sources of credit and accessibility to them play a very important role in male migration in rural Uttar Pradesh. The study also found that the relationship between credit constraints and migration varies across various social groups in UP.

Prevalence and risk factors of anaemia among men: A study based on Empowered Action Group states, India

2021 ◽  
pp. 026010602098234
Author(s):  
Pradeep Kumar ◽  
Himani Sharma ◽  
Kamalesh Kumar Patel
Keyword(s):  
Family Health ◽  
Public Health Problem ◽  
Binary Logistic Regression ◽  
Bivariate Analysis ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Action Group ◽  
Nationally Representative ◽  
The Government ◽  
Eag States

Background: Despite various programmes initiated by the Government of India, the nutritional indicators are not encouraging, as several problems like undernutrition, malnutrition and anaemia – still persist in the country, especially in the Empowered Action Group (EAG) states. Aim: Because of the dearth of studies regarding anaemia among men in India, the present study aimed to determine its prevalence in this population in the EAG states and to analyse its geographical and socio-demographic determinants. Methods: The study utilized nationally representative, cross-sectional survey data from round 4 of the National Family Health Survey conducted in 2015–16. Bivariate analysis along with binary logistic regression were performed to assess the predictors of anaemia among men in the EAG states. Results: Around a quarter of the men in the EAG states suffered from anaemia. A similar high-prevalence pattern was observed across the EAG states. Wherein, Bihar and Jharkhand had the highest prevalence of anaemia while Uttarakhand showed the lowest. Age, place of residence, marital status and caste were positively associated with the likelihood of anaemia among men in the EAG states. Conclusions: Focusing on the EAG states, this study considered the severity of anaemia as a public health problem among men. Strategies to reduce the burden of anaemia among this population are needed. The government should formulate programmes targeting anaemia specifically, and improving the nutritional status among men in general in the EAG states.

scholarly journals Bullying and Cyberbullying among Italian Adolescents: The Influence of Psychosocial Factors on Violent Behaviours

2021 ◽  
Vol 18 (4) ◽  
pp. 1558
Author(s):  
Antonio Tintori ◽  
Giulia Ciancimino ◽  
Giorgio Giovanelli ◽  
Loredana Cerbara
Keyword(s):  
Protective Factors ◽  
Binary Logistic Regression ◽  
Well Being ◽  
Risk And Protective Factors ◽  
Individual Characteristics ◽  
Bivariate Analysis ◽  
Binary Logistic Regression Model ◽  
Social Discomfort ◽  
Individual Variables ◽  
Statistical Sample

Background: The study of adolescents’ behaviours and attitudes is crucial to define interventions for the containment of deviance and social discomfort. New ways of social interaction are crystallising violent behaviours which are moving more than ever on a virtual sphere. Bullying and cyberbullying share a common behavioural matrix that has been outlined through specific environmental and individual characteristics. Methods: A survey carried out in Italy in 2019 on a statistical sample of 3273 students highlighted the influence of several social and individual variables on deviant phenomena. Risk and protective factors in relation to the probability of involvement in bullying and cyberbullying have been shown through a bivariate analysis and a binary logistic regression model. Results: The study shows that presence of stereotypes and social prejudices, tolerance to violence and high levels of self-esteem have resulted as the main risk factors. On the other hand, low levels of tolerance related to the consumption of alcohol and drugs, high levels of trust towards family and friends and being female have been identified as protective factors. Conclusions: This research confirms the validity of several theories on bullying and cyberbullying phenomena. Furthermore, it identifies specific risk and protective factors and their influence on deviant behaviours, with a focus on environmental characteristics which appear as the key field of work to enhance adolescents’ well-being.

scholarly journals Usaha Ternak Domba sebagai Jalur Keluar dari Kemiskinan Buruh Tani di Perdesaan

2020 ◽  
Vol 7 (3) ◽  
pp. 189
Author(s):  
Sondi Kuswaryan ◽  
Cecep Firmansyah ◽  
Muhammad Hasan Hadiana
Keyword(s):  
Regression Model ◽  
Poverty Alleviation ◽  
Binary Logistic Regression ◽  
Farm Labor ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Poor Households ◽  
Simple Regression Model ◽  
Poverty Status ◽  
Household Members

ABSTRAKPenelitian ini bertujuan untuk mengevaluasi kemungkinan usaha ternak domba sebagai aktivitas nafkah untuk pengentasan kemiskinan, serta menentukan jumlah kepemilikan domba yang dapat  membawa rumah tangga buruh tani keluar dari kemiskinan. Survey telah dilakukan di Desa Walangsari Kecamatan Kalapanunggal Kabupaten Sukabumi, melibatkan rumah tangga buruh tani miskin sebanyak 65 orang dan 22 orang tidak miskin. Faktor yang berpengaruh terhadap kemiskinan dianalisis menggunakan model regresi logistik biner, sedangkan jumlah kepemilikan domba yang harus dipelihara untuk keluar dari kemiskinan ditentukan dengan model regresi sederhana. Hasil penelitian menunjukkan bahwa usia kepala keluarga, dan pengalaman beternak tidak mempengaruhi kemiskinan, sedangkan jumlah kepemilikan domba, jumlah anggota rumah tangga, keterlibatan dalam kelembagaan, serta sumber pendapatan dari non pertanian mempengaruhi status kemiskinan rumah tangga buruh tani. Pada rata-rata jumlah anggota rumah tangga sebanyak 4,45 orang,untuk keluar dari kemiskinan buruh tani harus memelihara minimal sebanyak 36,63 ekor domba per rumah tangga. Hasil penelitian ini menegaskan bahwa usaha ternak domba dapat digunakan sebagai sarana untuk pengentasan kemiskinan buruh tani, program pengentasan kemiskinan akan efektif bila melibatkan kelembagaan lokal.Kata Kunci: buruh tani, jumlah kepemilikan domba, kemiskinanABSTRACTThis study aims to determine the possibility of sheep farming as a livelihood activity for poverty alleviation and to determine the amount of sheep ownership that can bring farm laborers households out of poverty. Survey research has been carried out in Walangsari Village, Kalapanunggal District, Sukabumi Regency, involving 65 poor farmer households and 22 non-poor households. Factors affecting poverty were analyzed using a binary logistic regression model, while the number of sheep ownership needed to escape poverty was determined by a simple regression model. The results showed that the age of the head of the family, and experience of sheep farmers did not affect poverty, while the number of sheep ownership, number of household members, involvement in institutions, and sources of income from non-agriculture affected the poverty status of farm laborers' households. In the average number of household members as many as 4.45 people, to get out of poverty must maintain a minimum of 36.63 sheep per household. This research explains that sheep farming can be used as a means to reduce the poverty of farm laborers, and poverty alleviation programs will be effective if they involve local institutions.Keywords: farm labor, number of sheep ownership, poverty

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