Application of factor analysis to the 2009 general household survey in South Africa

Abstract

Introduction: The high number of variables from the 2009 General Household Survey is prohibitive to do holistic analysis of data due to high correlations that exist among many variables, making it virtually impractical to apply traditional methods such as multinomial logistic regression. The purpose of this study to identify observed variables that can be explained by a few unobservable quantities called factors, using factor analysis. Methods: Factor analysis is used to describe covariance relationships among 162 variables of interest in the 2009 General Household Survey (GHS) and 2009 Quarterly Labour Force Survey of South Africa (QLFS). Data for the respondents aged 15 years and above was analysed by first applying factor analysis to the 162 variables to produce factor scores and develop models for five core areas: education, health, housing, labour force and social development. Multinomial logistic regression was then used to model educational levels and service satisfaction using identified factor sores. Results: The variability among the 162 variables of interest was described by only 29 factors identified using factor analysis, even though these factors are not measured directly. Multinomial logistic regression (MLR) analysis showed negative and significant impact of education factors (fees too high, violence and absence of parental care) on levels of educational attainment. “Historically advantaged” factor is the only factor significant and positively affects educational levels. Housing and social development factors were regressed against service satisfaction. Housing factors such as the home owners, age of a house and male household heads were found to be significant. Social development factors such as “no problem with health”, sufficient water, high income, household size and telephone access were found to be significant. Labour force factors such as employment, industrial business and occupation, employment history and long-term unemployment have positive and significant impact on levels of education. Conclusion: It can be concluded that factor analysis as a data reduction technique has managed to describe the variability among the 162 variables in terms of just 29 unobservable variables. Using MLR in subsequent analysis, this study has managed to identify factors positively or negatively associated with educational levels and service satisfaction. The study suggests that educational, housing, social development and labour force facilities should be improved and education should be used to improve life circumstances. Keywords: factor analysis, factors, multinomial logistic regression, logits, educational levels of attainment, service satisfaction, quality of service delivery.