Application of multinomial logistic regression to educational factors of the 2009 General Household Survey in South Africa

Monyai, Simon; Lesaoana, Maseka; Darikwa, Timotheus; Nyamugure, Philimon

doi:10.1080/02664763.2015.1077941

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…The standard errors of the coefficient estimates (B) are used to check for numerical errors or multicollinearity in the solution of the MLR. A standard error that is greater than 2.0 indicates that multicollinearity exists among the independent variables [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

“…MLR is an extension of the binary (or dichotomous) logistic regression and it is used when the outcome variable has more than two outcomes. For the MLR, one category or level of the dependent variable is specified as the reference category and regression coefficients are estimated for each independent variable [ 30 ]. The independent variables of MLR can be of a nominal, ordinal, or continuous type [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

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Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

2020

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This study focused on urban corporate people and applied multinomial logistic regression (MLR) to identify the impact of anthropometric, biochemical, socio-demographic and dietary habit factors on health status. Health status is categorized into four levels: healthy, caution, affected, and emergent. A cross-sectional study, based on convenience sampling method, was conducted to select 271 employees from 18 institutions under the Grameen Bank Complex, Dhaka, Bangladesh. Biochemical measurements such as blood uric acid are highly significant variables in the MLR model. When holding other factors as constants, with a one-unit increase in blood uric acid, a person is 11.02 times more likely to be “emergent” compared to “caution”. The odds are also higher, at 1.82, for the blood uric acid to be “affected” compared “caution”. The results of this study can help to prevent a large proportion of non-communicable diseases (NCDs) by reducing the most significant risk factor: blood uric acid. This study can contribute to the establishment of combined actions to improve disease management.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

2020

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“…According to Monyail et al (2016), the standard that shows the usefulness of MLR model should be 25% improvement over the rate of accuracy achievable by chance. Thus, the classification accuracy rate should be at least 69.9% for the MLR model to be adequate.…”

Section: Evaluation Of the Usefulness Of The Mlr Modelmentioning

confidence: 99%

Determinants of Chronic Illness Among Aged Population in Ghana: A Multinomial Logit Approach

Nyarko¹,

Agyarko²,

Nyarko³

et al. 2021

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Generally, the constant change in demographic trends among the aged depict gradual increase in the size of the aged population globally. The aged population is often capitalised with degenerative conditions such as chronic illness which affect their ability to function effectively and often require special support. Despite the increase in the size of the aged population and their associated degenerative conditions, very few descriptive studies on the determinants of chronic illness among the aged has been researched especially in developing countries such as Ghana and there is no compelling evidence on the association of chronic illness and its determinants. Thus, this study seeks to analyse and predict the impact of age, gender, education, marital status, Quality of Life (QoL), social cohesion, settlement and depression on chronic illness among the aged population in Ghana. From the studies a Multinomial Logit Regression (MLR) was employed to analyse the data obtained from the Ghana Statistical Service (GSS) under, Ageing and Adult Health (SAGE), 2012. It was revealed that out of the 1384 sampled, 71% had no chronic illness whilst 22% and 7% had one and two or more chronic illness respectively. Again, it was observed that, Depression State, Gender, Marital Status and Settlement Type (rural or urban) play significant roles in determining the likelihood of the aged getting chronic illness while Physical Function, Social Cohesion, QoL, Age and Education Level were not statistically significant determinants of chronic illness. Our findings demonstrate that chronic illness among the aged is constantly increasing in Ghana especially in the urban communities and need to be addressed urgently through governmental policies and programs in the quest to help salvage the deteriorating conditions of the aged. Keywords: Aged Population, Multinomial Logit Model, Chronic Illness

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“…It can be observed that mean and median are 4.38 and 4.00 respectively. Quantile measure is considered as the cut-off for classifying the staysvery short (1-2), short (3-4), medium (5-8), long (9)(10)(11)(12)(13)(14). The results of the classifications are also confined with initial investigation carried out with the hospital administrators and management.…”

Section: Figure 1 Histogram Of Diabetes Length Of Stay In Hospitalmentioning

confidence: 99%

Modelling Length of Stay in Hospitals using Multinomial Regression

Harini¹,

Subbiah²,

Srinivasan³

2019

IJCTS

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Hospital management is generally focused on studying the length of stay of patients since the measure has an impact on hospital resources. It is a challenging task for the hospital management to model the length of stay as they are asymmetric and heterogeneous in nature. Diabetes is a major health problem prevalent worldwide which leads to hospitalization over a time period. The present study deals with stay of diabetes patients classified as very short, short, medium and long duration of stay based on quantile classification rather than arbitrary approach. In this study, we have attempted to include an important covariate known as medical record since it assist in reducing the stay of a patient and can thereby accommodate more patients deserving treatment as inpatients. Based on the multiple levels of the response variable, we have considered fitting multinomial regression model for length of stay on diabetes. Further, this study has considered the validation of variable selection procedure for model fitting using subsampling approach. In conclusion, it has been identified that medical records is one of the important factor affecting the stay of patients and subsampling approach has been helpful in building the final model.

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Application of multinomial logistic regression to educational factors of the 2009 General Household Survey in South Africa

Cited by 11 publications

References 13 publications

Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

Determinants of Chronic Illness Among Aged Population in Ghana: A Multinomial Logit Approach

Modelling Length of Stay in Hospitals using Multinomial Regression

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