Data collected was first pre-processed where the questionnaires were examined and cleaned to ensure they were completed and consistently filled. Thereafter, the response questions were numerically coded and responses stored in a database template under assigned variable names for analysis using Statistical Package for Social Sciences (SPSS) computer software version 19.
Data entered was subjected to Data View option to display in order to cross check the accuracy of data entered. This exercise led immediately to checking of errors and omissions, cleaning of errors and controlling data quality. This process was vital towards solid foundation for analytical processing of data both qualitatively and quantitatively. Thus, it is the bases for success in the final analysis. The qualitative data collected using the open ended questionnaire and interview guide, was taken in form of notes and used to compliment the quantitative data thematically.
27 3.8.1 Descriptive Statistics Analysis
Analysis of the socio-economic characteristics of the household heads, level of adoption and the socio-economic profiles of adopters and non-adopters was done using descriptive statistics such as frequencies, means, standard deviation, percentages. This analysis was performed using SPSS version 19 and Microsoft Excel was used to enable graphical representation. The study adopted Garson (2012) guideline that before starting with any advanced analysis, it is always good to start with some descriptive statistics and simple graphics.
3.8.2 Non-parametric Test
To examine the nature of the relationship between independent and dependent variables used in this study was conducted through a non-parametric tool, the chi- square. Chi-square test is a nonparametric statistical test to determine if the two or more classifications of the samples are independent or not (Zibran, 2012).
Thus the chi-square formula is:
---equation (2)
Where k = # of categories, Oi = observed number of cases in each category, Ei = expected number of cases in each category. In this study Chi square was was used to test whether the explanatory variables were related among the adopters and non- adopters. The results were then tested for significance at 0.05 (95 percent confidence level).
k Ei Ei Oi 2 2 ( )
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Independent sample t-test was used to assess whether or not the mean satisfaction scores of electrified and un-electrified public facilities are statistically different. This test provides mean difference, t-values, degrees of freedom and their significance. According to Park, (2009) independent sample t-test is a statistical analysis tool used to compare mean scores on the continuous variable for two different independent groups. In this study t-test was used to detect difference in households‘ perception on quality service provision in electrified and non-electrified public facilities. Thus the t- test formula is:
t= 12 2 1
, df = (n1-1) + (n2-1) ---equation (3)
Where12 is the difference in sample results and s12 is the difference in standard error (Park, 2009). The results were then tested for significance at 0.05 (95 percent confidence level).
In order to assess interaction effects between the independent variables and investigate the socio economic determinants of electricity adoption, independent variables were screened for multicollinearity using Colinearity Diagnostics function in SPSS (Leech, Barrett, & Morgan, 2005). Consequently, binary logistic regression analysis was performed to examine the influence of various household socio- economic characteristic on electricity adoption. This regression method was chosen because binary logistic regression is primarily used when the dependent variable is a dummy categorical variable (usually dichotomous) and has two outcomes such as 0 and 1. More so, logistic regression is often chosen when the predictor variables are a mix of continuous and categorical variables. Logistic regression makes no assumptions about the distributions of the predictor variables (Peng, 2002). In this
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study, the dependent variable (adoption of electricity by households) is a dichotomous variable. Hence a value of 0 was assigned if the household was of a non- adopter and 1 if the household was an adopter, giving a regression of a non-linear form. This was done following a guide line provided by Hilbe (2009).
Thus, probability of adoption is explained as follows:
ln (p/1-p) =a + b1x1+b2x2+…+bnxn---equation (4)
The logit transformation of the probability of adoption is represented as follows:
Logit (p) = a + b1x1+b2x2+…+bnxn---equation (5)
Where p: is the probability of a case belonging to category 1, p/1-p: the odds of electricity adoption, a: constant, n: number of predictors and b1-bn represents regression coefficients.
The spatial distribution patterns of electricity accessibility points, adopters and non- adopters households mapping was done using GPS coordinates collected during fieldwork. The GPS coordinates were reorganized into a GIS compatible file and imported into ArCGIS 10.2 to generate maps showing location of adopters, non- adopters and electricity accessibility points in the study area. The summary of the various objectives, data types used, method adapted for the analysis with the corresponding statistical tools presented in (Table 3.1).
30 Table 3.1: Variables for Data Collection
Objectives Type of data collected Data collection Tools Data analysis Dependent variable Independent variable 1. Examine how household social- economic characteristics influence electricity adoption in the study area.
Adoption Household characteristics;
Age, Gender, marital status, education level, occupation of the household head, sources of income, monthly income, and main type of dwelling. Questionnaire Descriptive statistics; Percentages, Mean, standard deviations, Frequencies, cross tabulations. Chi- square. Regression (Binary logistic regression model), 2. Assess the socio-
economic benefits and challenges of electricity adoption among households in the study area.
Electrification Electricity use benefits of various activities
Questionnaire Descriptive statistics; Percentages, mean, standard deviations, frequencies. Chi- square. 3. Evaluate the effect of rural electrification on development of public facilities
Electrification Quality of service delivery Questionnaire Interview guide Descriptive statistics; Percentages, mean, standard deviations, frequencies and cross tabulation t-test 4. Examine the spatial distribution of electricity adoption, non adoption and accessibility in the study area.
Adoption Accessibility GPS sets
Questionnaire
Using ArCGIS software version 10.2 layers created and generated into maps.
Source: Author, 2013