Multilevel Modeling of Determinants of Fertility Status of Married Women in Ethiopia

Eyasu, Anteneh Mulugeta

doi:10.11648/j.ajtas.20150401.14

Cited by 5 publications

(4 citation statements)

References 7 publications

(8 reference statements)

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“…The rate of birth among women with no and primary Scale (e ij ) 0.05255 education was 1.52 and 1.2 respectively times the rate of birth with secondary education, moreover, no statistically significant difference was observed in the rate of birth by women with higher and secondary education levels. The result is conformable with [11,25,26,28] where increasing the educational level of women reduced the rate of birth. A one-year increase in the age at first birth of women results in a 0.0017 decrement in the log rate of birth, the result is consistent with the finding of [11,25,26,29]) where the older the age at first birth of women the lower number of children ever born per woman will be.…”

Section: Discussionsupporting

confidence: 70%

“…The result is conformable with [11,25,26,28] where increasing the educational level of women reduced the rate of birth. A one-year increase in the age at first birth of women results in a 0.0017 decrement in the log rate of birth, the result is consistent with the finding of [11,25,26,29]) where the older the age at first birth of women the lower number of children ever born per woman will be. Additionally, a one-month extension of a woman's preceding birth interval caused a 0.01209 reduction in the log rate of birth, which was consistent with the findings of [30,31], where increasing proceeding birth interval resulted in the reduced number of children ever born.…”

Section: Discussionsupporting

confidence: 70%

“…Within the setting of the above circumstances, this study attempted to spot out the factors related to the total number of children ever born among married women in Ethiopia. Many studies investigated determinants of fertility in Ethiopia with some sets of variable and statistical methods like logistic regression, survival analysis, and linear regression models [8][9][10][11]. Since the total number of children ever born is a count data, the Poisson regression model (PRM) and Negative binomial regression models (NBRM) are shown to be statistically more acceptable.…”

Section: Introductionmentioning

confidence: 99%

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Application of two level count regression modeling on the determinants of fertility among married women in Ethiopia

Hussen

2022

BMC Women's Health

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Background Fertility is the element of population dynamics that has a vital contribution toward changing population size and structure over time. The global population showed a major increment from time to time due to fertility. This increment was higher in south Asia and sub-Saharan Africa including Ethiopia. So this study targeted the factors affecting fertility among married women in Ethiopia through the framework of multilevel count regression analysis using the 2016 Ethiopian Demographic and Health Survey data. Methods Secondary data set on the birth records were obtained from the 2016 Ethiopia Demographic and Health Survey. The survey was a population-based cross-sectional study with a two-stage stratified cluster sampling design, where stratification was achieved by separating every region into urban and rural areas except the Addis Ababa region because it is entirely urban. A two-level negative binomial regression model was fitted to spot out the determinants of fertility among married women in Ethiopia. Results Among the random sample of 6141 women in the country, 27,150 births were recorded based on the 2016 Ethiopian Demographic and Health Survey report. The histograms showed that the data has a positively skewed distribution not extremely picked at the beginning. Findings from the study revealed that the contraception method used, residence, educational level of women, women’s age at first birth, and proceeding birth interval were the major predictors of fertility among married women in Ethiopia. Moreover, the estimates from the random effect result revealed that there is more fertility variation between the enumeration areas than within the enumeration areas. Conclusion Unobserved enumeration area fertility differences that cannot be addressed by a single-level approach were determined using a two-level negative binomial regression modeling approach. So, the application of standard models by ignoring this variation ought to embrace spurious results, then for such hierarchical data, multilevel modeling is recommended.

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Section: Discussionsupporting

confidence: 70%

Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

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Application of two level count regression modeling on the determinants of fertility among married women in Ethiopia

Hussen

2022

BMC Women's Health

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“…Several studies considered the different concepts of fertility and population growth using successive census data and the development of reproductive and fertility measures in urban and rural areas (12,10). Other studies examined the relationship between fertility and different socioeconomic and demographic characteristics and their impact on population growth (13)(14)(15)(16). Many studies also suggested different models to explain the effect of direct and indirect factors on fertility (17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

Structural equations modelling to quantify the effect of direct and intermediate factors on fertility changes in Egypt during 2000–2014

Abdelghany

Abbbasy

Shabrawy

2020

East Mediterr Health J.

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Background: Egypt's population increased by 10 million between 2011and 2014 and the total fertility rate increased to 3.5 births per woman. Aims: This paper aimed to evaluate the direct and intermediate factors that could explain this increase in the total fertility rate. Methods: Data from the 2000 and 2014 Egypt Demographic and Health Surveys were used. The average number of births per woman in the 3 years before the survey was used as a proxy for total fertility rate. A structural equations model was used to measure the total effect of direct and intermediate variables on number of births per woman. Results: The effect of the intermediate variables on births per woman was stronger than the direct factors in both health surveys. In the 2000 and 2014 surveys, the intermediate factors with the greatest effect were: unmet need for family planning methods, discontinuation of using family planning methods in the 5 years before the survey, number of discontinuations of using family planning methods in the 3 years before the survey and child death under 5 years. In the 2000 survey, the direct factors affecting a decline in births per woman were: exposure to family planning messages, woman's work status, wealth index and woman's age 3 years before the survey. In the 2014 health survey, the effect of these variables on fertility declined. This decrease contributed to increasing the total fertility rate to 3.5 births per woman. Conclusion: Population policies should reintensify efforts to reduce the fertility rate based on the factors that influence fertility, such as family planning messages.

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The Impact of Spatial-Demographic Disparities on Fertility Variations in Serbian Municipalities—An Example of Hidden Content Research

Obradović

Vojkovic

2021

Key Challenges in Geography

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Multilevel Modeling of Determinants of Fertility Status of Married Women in Ethiopia

Cited by 5 publications

References 7 publications

Application of two level count regression modeling on the determinants of fertility among married women in Ethiopia

Application of two level count regression modeling on the determinants of fertility among married women in Ethiopia

Structural equations modelling to quantify the effect of direct and intermediate factors on fertility changes in Egypt during 2000–2014

The Impact of Spatial-Demographic Disparities on Fertility Variations in Serbian Municipalities—An Example of Hidden Content Research

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