A Bayesian multilevel model for women unemployment in South Africa
| dc.contributor.advisor | Bere, A. | |
| dc.contributor.advisor | Sigauke, Caston | |
| dc.contributor.author | Ramarumo, V. P. | |
| dc.date | 2021 | |
| dc.date.accessioned | 2021-12-12T00:55:46Z | |
| dc.date.available | 2021-12-12T00:55:46Z | |
| dc.date.issued | 2021-08 | |
| dc.description | MSc (Statistics) | en_ZA |
| dc.description | Department of Statistics | |
| dc.description.abstract | The study is aimed at investigating and explaining the demographic and socio-economic determinants components a ecting women unemployment in South Africa. The classical and the Bayesian estimation approach were applied to a multilevel logistic regression (MLR) model. Secondary data acquired from the Demographic and Health survey (DHS) held in South Africa in 2016 was used in the study. Information criteria revealed that the random intercept model outperformed the MLR model of the null and random coe cient multilevel models. The Intraclass Correlation Coe cient (ICC) proposes that there is an understandable di erence in women unemployment level over various provinces of South Africa. The results of the classical MLR and the Bayesian MLR indicate in ated commonness for women unemployment and the chance of being without employment for women was established to decrease with an increase of age, wealth index, and educational attainment. | en_ZA |
| dc.description.sponsorship | NRF | en_ZA |
| dc.format.extent | 1 online resource (xi, 82 leaves) | |
| dc.identifier.apacitation | Ramarumo, V. P. (2021). <i>A Bayesian multilevel model for women unemployment in South Africa</i>. (). . Retrieved from http://hdl.handle.net/11602/1814 | en_ZA |
| dc.identifier.chicagocitation | Ramarumo, V. P.. <i>"A Bayesian multilevel model for women unemployment in South Africa."</i> ., , 2021. http://hdl.handle.net/11602/1814 | en_ZA |
| dc.identifier.citation | Ramarumo, V. P. (2021) A Bayesian multilevel model for women unemployment in South Africa. University of Venda, South Africa.<http://hdl.handle.net/11602/1814>. | |
| dc.identifier.ris | TY - Dissertation AU - Ramarumo, V. P. AB - The study is aimed at investigating and explaining the demographic and socio-economic determinants components a ecting women unemployment in South Africa. The classical and the Bayesian estimation approach were applied to a multilevel logistic regression (MLR) model. Secondary data acquired from the Demographic and Health survey (DHS) held in South Africa in 2016 was used in the study. Information criteria revealed that the random intercept model outperformed the MLR model of the null and random coe cient multilevel models. The Intraclass Correlation Coe cient (ICC) proposes that there is an understandable di erence in women unemployment level over various provinces of South Africa. The results of the classical MLR and the Bayesian MLR indicate in ated commonness for women unemployment and the chance of being without employment for women was established to decrease with an increase of age, wealth index, and educational attainment. DA - 2021-08 DB - ResearchSpace DP - Univen KW - A Bayesian inference KW - A multilevel logistic regression KW - Provincial Variations KW - Unemployment LK - https://univendspace.univen.ac.za PY - 2021 T1 - A Bayesian multilevel model for women unemployment in South Africa TI - A Bayesian multilevel model for women unemployment in South Africa UR - http://hdl.handle.net/11602/1814 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11602/1814 | |
| dc.identifier.vancouvercitation | Ramarumo V P. A Bayesian multilevel model for women unemployment in South Africa. []. , 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1814 | en_ZA |
| dc.language.iso | en | en_ZA |
| dc.rights | University of Venda | |
| dc.subject | A Bayesian inference | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.subject | Provincial Variations | en_ZA |
| dc.subject | Unemployment | en_ZA |
| dc.title | A Bayesian multilevel model for women unemployment in South Africa | en_ZA |
| dc.type | Dissertation | en_ZA |