2. NAME OF SUPERVISOR: Prof. G.N. Amahia

3. YEAR OF COMPLETION: 30/09/2014

4. TITLE OF Ph.D THESIS: Modelling Response Propensities in Household Surveys in Ibadan Metropolis, Oyo State


Response propensity is a respondent’s tendency to willingly answer survey questions. Propensity models have been used to study low response as a result of non response, and to reduce non response bias. These propensity models only considered the effects of main characteristics on response propensities, ignoring their interaction effects. Therefore, this study was designed to adapt and validate response propensity models with main and interaction effects in household surveys. A two stage stratified sampling scheme was used to select 400 households in Ibadan metropolis using the master sample list of the National Integrated Survey of Households prepared by the National Bureau of Statistics as the sampling frame. Households in both urban and rural areas were interviewed in five waves within a period of fifteen months (January 2011 – March 2012). An interviewer-administered questionnaire was used to collect data on household characteristics and response propensities: age, sex, household size, educational level, employment status, nature of employment, location, marital status, religions, literacy level and household income and expenditure. Household characteristics were analysed using summary statistics. Multi-way contingency tables were constructed to investigate the presence of relationships and dependence structures among the characteristic under consideration. The characteristics that exhibited significant relationship and dependence structure were used in constructing a propensity model with main and interaction effects. The household characteristics were used to construct a Response Surface Polynomial Model (RSPM). The RSPM was subjected to canonical analysis to characteristics its turning point of the RSPM was used to determined saddle point, minimum point and maximum point respectively. The average household’s size in rural and urban were 6.2+0.8 and 5.8+0.4 5respectively. The proportion of households headed by women (measure of parity was 0.28 in rural and 0.21 in urban. The disparities in the response rates in urban wave’s insignificant (p > 0.06) while they were significant (p < 0.03) in the rural waves. Household size x1, tertiary and zero levels of education x2, x3 respectively, and nature of employment x4 showed significant dependent structure (p < 0.03). The propensity model that incorporated these significant characteristics and their interactions had adjusted R2 = 0.8, and was significant (p < 0.04) The RSPM has adjusted R2 = 0.7, and was significant (p < 0.04). Canonical analysis of the RSPM gave eigenvalues λ1 = -0.01 and λ2 = -0.03. The negative values of the RSPM indicated maximum turning point. The propensity to respond was optimum when the individual interviewed was employed and had a household size of not more than six. Household size, employment status and their interactions were found to play significant roles in obtaining optimum response in household surveys in Ibadan metropolis. A policy that promotes employment as well as encourages six persons per household is recommended as this would greatly enhance response propensities in household surveys. Keywords: Response rate, Non-response bias, Propensity model, Interaction effects, Canonical analysis. Words Count 453.