Logistic Regression Modelling for Complex Survey Data with an Application for Bed Net Use in Mozambique

Cassy, Sheyla Rodrigues; Natário, Isabel; Martins, Maria do Rosário Oliveira

doi:10.4236/ojs.2016.65074

Cited by 16 publications

(19 citation statements)

References 12 publications

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“…From Cassy, Natário, and Martins [ 40 ], when analyzing survey data where the interest is to predict a binary outcome from an explanatory variable and a set of covariates, the use of the logistic regression model is common. The regression model is in the class of limited dependent variable models for studying the relationship between a binary response variable Y , representing success ( Y = 1) or failure ( Y = 0) and a set of covariates x = ( x 1 , x 2 , ⋯, x p )′.…”

Section: Methodsmentioning

confidence: 99%

Household ICT Utilization and Food Security Nexus in Nigeria

Ejemeyovwi

Osabohien

Adeleye

et al. 2021

International Journal of Food Science

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The study examines the nexus between household ICT utilization and food security in Nigeria, which supports goal 2 of the 2030 Sustainable Development Goals (SDGs) that aims to “end hunger, achieve food security, improved nutrition, and promote sustainable agriculture.” The study employs the logit regression to wave 4 of Living Standard Measurement Integrated Survey on Agriculture (LSMS) data for the empirical analysis. Based on the analysis, the study finds that for male household, ICT utilization has a statistically significant and positive nexus with food security. In contrast, for the female households, an insignificant and, however, negative nexus is observed with food security in Nigeria. Furthermore, the findings show that for male household users, a 1 percent increase in male household ICT utilization spurs about 0.68 percent increase in food security in Nigeria. The findings imply that among the male and female household ICT users, the male household ICT utilization is significantly contributes to food security in Nigeria. The study recommends that relevant stakeholders take strategic measures to ensure that the potentials of household ICTs be fully maximized to contribute to food security in the nearest future as confirmed by studies in other regions.

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Section: Methodsmentioning

confidence: 99%

Household ICT Utilization and Food Security Nexus in Nigeria

Ejemeyovwi

Osabohien

Adeleye

et al. 2021

International Journal of Food Science

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“…The standard errors of the estimated regression parameters and odds ratios are computed using either Taylor series (linearization) or replication (resampling) method based on the complex sample designs (Sarndal, Swensson, & Wretman, ), and hence, the SLR model is assumed to provide valid standard errors of the estimated regression coefficients for a complex survey data. For detail on SLR, please see Cassy et al ().…”

Section: Methodsmentioning

confidence: 99%

“…In order to make statistically valid inferences for the population, properties of sampling design need to be incorporated in the logistic analysis. Survey logistic regression (SLR) model provides valid inference by incorporating the properties of sample designs in the analysis and consistent estimates of regression coefficients (Cassy, Natário, & Martins, ).…”

Section: Introductionmentioning

confidence: 99%

On selection of an appropriate logistic model to determine the risk factors of childhood stunting in Bangladesh

Bhowmik

Das

2018

Maternal & Child Nutrition

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Stunting is the core measure of child health inequalities as it reveals multiple dimensions of child health and development status. The main focus of this study is to show the procedure of selecting the most appropriate logistic regression model for stunting by developing and comparing several plausible models, which ultimately helps to identify the predictors of childhood stunting in Bangladesh. This study utilizes child anthropometric data collected in the 2014 Bangladesh Demographic and Health Survey. Valid height-for-age anthropometric indices were available for a total of 6,931 children aged 0-59 months, of which about 36% were stunted. Ordinary logistic, survey logistic, marginal logistic, and random intercept logistic regression models were developed assuming independence, sampling design, cluster effect, and hierarchy of the data. Based on a number of model selection criteria, random intercept logistic model is found the most appropriate for the studied children. A number of child, mother, household, regional, and community-level variables were included in the model specification. The factors that increased the odds of stunting are children older than 11 months, short birth interval, recent morbidity of children, lower maternal education, young maternity, lower maternal body mass index, poor household wealth, urban residential place, and living in Sylhet division. Findings of this study recommend to utilize an appropriate logistic model considering the issues relevant to the data, particularly sampling design and clustering for determining the risk factors of childhood stunting in Bangladesh.

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“…When estimating the regression coefficients and variance of the coefficient, logistic regression analysis of a complex sample considers design effects such as stratification, clustering, and individual weights [ 19 , 20 , 21 ]. First, the population was divided into K strata, and each stratum was divided into

clusters.…”

Section: Materials and Methodsmentioning

confidence: 99%

Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases

Shin

Lee

2022

Healthcare

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Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created a nomogram related to metabolic syndrome in this paper for the first time. We analyzed data from the Korea National Health and Nutrition Examination Survey VII. Total 17,584 participants were included in this study, and the weighted sample population was 39,991,680, which was 98.1% of the actual Korean population in 2018. We identified 14 risk factors affecting metabolic syndrome using the Rao-Scott chi-squared test. Next, logistic regression analysis was performed to build a model for metabolic syndrome and 11 risk factors were finally obtained, including BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history and age* sex. A nomogram was constructed to predict the occurrence of metabolic syndrome using these risk factors. Finally, the nomogram was verified using a receiver operating characteristic curve (ROC) and a calibration plot.

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Logistic Regression Modelling for Complex Survey Data with an Application for Bed Net Use in Mozambique

Cited by 16 publications

References 12 publications

Household ICT Utilization and Food Security Nexus in Nigeria

Household ICT Utilization and Food Security Nexus in Nigeria

On selection of an appropriate logistic model to determine the risk factors of childhood stunting in Bangladesh

Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases

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