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

Simon Monyai, 'Maseka Lesaoana, Timotheus Darikwa and Philimon Nyamugure

Journal of Applied Statistics, 2016, vol. 43, issue 1, 128-139

Abstract: This paper combines factor analysis and multinomial logistic regression (MLR) in understanding the relationship between extracted factors of quality of life pertaining to education and variables of five key areas of the levels of development in the context of the South African 2009 General Household Survey. MLR was used to analyse the identified educational factors from factor analysis. It was also used to determine the extent to which these factors impact on educational level outcomes across South Africa. The overall classification accuracy rate displayed was 73.0% which is greater than the proportion by chance accuracy criteria of 57.0%. This means that the model improves on the proportion by chance accuracy rate of 25.0% or more so that the criterion for classification accuracy is satisfied and the model is adequate. Evidence is that being historically disadvantaged, absence of parental care, violence in schools and the perception that fees were too high generally have a negative influence on educational attainment. The results of this paper compare well with other household surveys conducted by other researchers.

Date: 2016
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2015.1077941 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:43:y:2016:i:1:p:128-139

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2015.1077941

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().