Data Analysis

Data gathered is analyzed using quantitative methods. The data collected is coded in Excel for easy analysis and descriptive statistics drawn from the responses to analyze the sample characteristics. The Financial Services Confidence Index is then calculated from the first four questions of the questionnaire to determine the level of consumer confidence.

3.4.1 The Financial Service Confidence Index The confidence index is calculated using the following steps:

I. Calculate the percentage of positive and negative responses for each of the questions.

II. The balance (this is the difference between the positive and negative response percentages) of each question is calculated and summed. An average of the summed balances is obtained by dividing the number of questions, 100 points are then added to obtain the confidence index.

Mathematically, Confidence index = (sum (% of positive responses (i) - % of negative response (i)) /n) + 100, where i = Question number and n = total number of questions.

The index is interpreted to show whether the public is optimistic, pessimistic, or neutral about the outlook of microfinance institutions. An index equal to 100 points means a stable outlook of the financial services sector, a less than 100 points mean a pessimistic outlook and a greater than 100 points means an optimistic outlook.

3.4.2 Binary Logistic Regression

To determine the relationship between demand for microfinance services and confidence in microfinance services, a binary multiple logistic regression is used. Logistic regression is most appropriate for testing hypotheses about the relationship between a categorical dependent variable and one or more categorical or continuous independent variables (Peng, Lee & Ingersoll, 2002; Agresti, 2002). A logistic regression is preferred because the assumptions of normal distribution of the data and linearity are not required (Lee, 2005; Peng, Lee, & Ingersoll, 2002). It only assumes that the

observations are independent which is true about the data gathered since the demand for microfinance services by one respondent is independent of the other. Multiple logistic regression is used to be able to control other factors that may have an influence on the dependent variable (demand). The general multiple logistic regression model is given as; logit [p(Y = 1)] = α + 𝛽₁ 𝑋_𝑖 + 𝛽₂ 𝑋_𝑖 + …….+ 𝛽_𝑛 𝑋_𝑛, where n is the number of predictor (independent) variables, p(y = 1) is the probability of the dependent variable occurring, α

is the intercept, 𝑋_𝑖 is the independent variables (i = 1, 2, 3,…..n) and 𝛽 is the slope coefficient of the independent variables.

3.4.2.1 Specified Logistic Regression Model

For the purposes of this research, the specific logistic regression model for this study is represented in the below equation.

Logit [p(Demand = 1)] = α + 𝛽₁ 𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝐼𝑛𝑑𝑒𝑥_𝑖 + 𝛽₂ 𝐺𝑒𝑛𝑑𝑒𝑟_𝑖 + 𝛽₃ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝐿𝑒𝑣𝑒𝑙_𝑖,

The conceptual framework in Chapter 1 of this report is used in determining the dependent and independent variables. The decision of patronizing microfinance services representing demand is the dependent variable with binary categorical responses as Yes and No coded in dummy variables. The independent variables are the confidence index of individual respondents, gender and the level of education of respondents. Reasons for all independent variables included in the model are explained in the conceptual framework of the research in Chapter 1 subsection 1.6 of this report.

Gender and the level of education are both categorical independent variables coded in dummy variables as male = 0, female = 1 and senior high school and below = 1, tertiary = 0 for gender and education levels respectively. The confidence level of respondents is the only continuous independent variable in the model. The regression is performed using the SPSS data analysis software.

The hypothesis tested using the logistic regression is give as;

H0: There is no significant relationship between the level of confidence in microfinance institutions and demand for microfinance services.

H0 is the null hypothesis and the alternate is the opposite of it. The null hypothesis is rejected at a 5% significance level if the p-value of the coefficient of confidence (𝛽1 ) is less than 0.05. A p-value greater than 0.05 however, indicates a failure to reject the null hypothesis.

3.4.3 T-test of Difference in Means

In addition to using the logistic regression, the independent t-test is performed to test for differences in means of various categories or groups within the sample studied using the SPSS statistical tool. The dependent variable is the level of confidence in microfinance institutions while the categorical independent variables are gender, usage of microfinance services and the satisfaction from the use of microfinance services (this is done for only respondents who have ever used/demanded microfinance services). The categories of gender are male and female, usage takes the categories of yes and no while satisfaction is also categorized as yes and no. Thus, the mean confidence levels of the various categories of the independent variables are compared. Three separate t-test hypotheses are tested.

H0:

μ

1 =

μ

2

μ

1 is the mean of males and

μ

2 is the mean of females. This is the first t-test conducted and rejecting the null hypothesis would mean that there is a significant difference in the means of male and female respondents in terms of their level of confidence in microfinance institutions.

In the second hypothesis testing,

μ

1 is the mean confidence of respondents who have ever used microfinance services and

μ

2 is the means confidence of respondents who have never used microfinance services. The null hypothesis here means that the mean confidence of respondents who have ever used microfinance services is the same as those who have never used microfinance services. This implies a respondent’s level of

confidence in microfinance institutions is not influenced by usage of microfinance services.

The last t-test hypothesis evaluates the null hypothesis that the mean confidence of respondents satisfied with the microfinance services that they used (

μ

1) equals the mean confidence of respondents who were not satisfied with the microfinance services that they used.

The null hypothesis is rejected for each t-test when the p-value is greater than 0.05 significance level and it fails to be rejected when the p-value is less than 0.05.

In document Public confidence in microfinance institutions and demand for microfinance services in Ghana (Page 31-36)