Data analysis is another key stage of primary research. Choosing appropriate data analysis techniques enables the researcher to arrive at valuable results that answer the research objectives (Kumar et al., 2002). Essential steps for the analysis of quantitative and qualitative data are described as follows:
5.10.1 Quantitative Data Analysis
To analyse the quantitative data from the survey, the researcher opted for SPSS software and adopted the steps suggested by Pallant (2004) to include the following steps: (1) preparing codebook, (2) setting up structure for data file, (3) entering data, (4) screening data file for errors, (5) exploring data using descriptive statistics and graphs, (6) modifying variables for further analyses, and (7) conducting statistical analyses to answer research questions.
Step 1: Preparing a codebook
A codebook is ‘a summary of the instructions you will use to convert the information obtained from each subject or case into a format that SPSS can understand’ (Pallant, 2004, p.11). The process involves defining and labelling each of the variables and assigning numbers to each of the possible responses.
Step 2: Setting up structure for data file
This step consists of defining variables and coding instructions as per the codebook. In this research, the researcher defined the variables in full as they appeared in the questionnaire. However, the names of variables were abbreviated. Value labels were also assigned according to the information in the codebook.
Step 3: Ente ring data
After the variables and coding instructions were set in the system, the researcher converted the data, which were keyed into an Excel worksheet earlier during the fieldwork into the
124 SPSS programme. The researcher ensured that the data in the Excel worksheet were coded in accordance with the codebook.
Step 4: Screening data file for errors
In order to ensure the correctness and completeness of the data, the researcher screened the data to identify errors. Descriptive statistics such as frequencies, mean, and standard deviation were employed. Values that fell outside the range of possible values of each variable and missing cases were identified in this process. The data were co rrected by referring to the questionnaire.
Step 5: Exploring data using descriptive statistics and graphs
After the errors in the data file were corrected, the researcher employed descriptive statistics and graphs to explore the characteristics of the data and violations of assumptions underlying the statistical techniques to be used to answer the research questions. At this stage, the researcher was considering two alternative methods of analysis: parametric or non-parametric. Some researchers claim that parametric statistics can still be used for studies employing non-probability sampling as long as the sample is relatively large (Siegel, 1957, Baker et al., 1966, Cohen et al., 1990), like this research. The results showed that the assumption of normality seemed to have been violated. The researcher then opted for non-parametric techniques. Moreover, the data were tested to ensure reliability and validity. In this research, the researcher employed Cronbach’s Alpha reliability test. This test is discussed in detail in Section 5.11 of this chapter.
Step 6: Modifying variables for further a nalyses
Some variables were modified to suit the statistical techniques employed in the analysis. Ratio variables such as age, years in education, annual household income, and total land size were transformed into categorical data, i.e. different groups, to accommodate the Kruskal-Wallis test. In addition, questions in sections 6-8 which were earlier entered as ordinal data (strongly disagree, disagree, neutral, agree, and strongly agree) were considered to be suitable for ordinal regression analysis. The trail tests of the data indicated
125 that the data were not appropriate for ordinal regression analysis. The data were transformed into dichotomous variables which were used for binary logistic regression analysis. The process of transformation is explained in detail in Section 7.2.4. of Chapter 7.
Step 7: Conducting statistical analyses to ans wer the research questions
After necessary modification of variables, the final data set was ready for analysis. The researcher needed to identify appropriate statistical tests which would produce the required results to fulfil the objectives of the study. In this study, the researcher has opted for the following descriptive and empirical techniques:
Descriptive analysis: The researcher employed frequency distributions, mean, and standard deviation. These analyses were used to summarise, organise, and describe the data (Pallant, 2004). The results of these analyses are discussed mainly in the demand analysis chapter (Chapter 6) and in some parts of the impact analysis chapter (Chapter 7).
Inferential Empirical analysis: Based on the discussion in step 5 in which the non- parametric method was found to be appropriate for the analyses, the researcher has identified suitable inferential statistical techniques including cross-tabulation, Mann- Whitney U-test, Kruskal-Wallis test, correlation, and logistic regression. These statistical techniques can be classified into two groups: exploring difference between groups and exploring relationships. The statistical tools for exploring differences between the groups include the following:
Cross-tabulation: Also known as contingency table, this technique organises data into diverse groups and shows the relationship between two variables (de Vaus, 1990, Bryman, 2008). In this research, cross-tabulation was used to compare the services used among the clients of various IsMFIs.
Mann-Whitney U-test: This technique ‘is used comparing two groups, but the basis for comparison is data in ordinal form’(Proctor, 2005, p.289). It is similar to the independent t- test in parametric alternatives. In this research, the test was used to examine whether there are differences in level of demand for and impacts of Islamic microfinancial services
126 among the clients of different gender, religious education, and SME ownership. The mean rank results can reveal the direction of difference (Pallant, 2004).
Kruskal-Walis test: This is similar to the Mann-Whitney U-test, but it is used when more than two independent samples are involved (Pallant, 2004, Proctor, 2005). It is ‘the non- parametric alternative to a one-way between-group analysis of variance’ (Pallant, 2004, p. 226). In this research, the technique was employed to determine the differences in level of demand for and impacts of Islamic microfinancial services among the clients of more than two categories such as income levels, age-groups, marital status, education, and household size. The mean rank results can also identify which group has higher scores.
In addition to the statistical tools used to explore the difference between the groups, the researcher employed some statistical techniques to explore relationships between variables. The techniques consist of the following:
Correlation: This is a statistical tool to measure the relationships between a dependent variable and one or more independent variables. However, bivariate correlation was employed in this study. It describes strength a nd direction of relationship between two variables (Proctor, 2005). Spearman’s rank order correlation is appropriate for the analysis in this research as it can accommodate ordinal data which are obtained from the survey (Pallant, 2004).
Logistic regression analysis: This is a technique to assess the impact of a set of predictors on a dependent variable that is categorical. The independent variables may be continuous, categorical, dichotomous or a mix (Tabachnick and Fidell, 1996, Hosmer and Lemeshow, 2000, Menard, 2002). In this research, binary logistic regression to determine which dependent variable is dichotomous was used to assess demand for and impacts of Islamic microfinancial services. It is used to identify significa nt and strong predictors of the use of the services and their impacts on the lives of the clients and their households. The details of the analysis are discussed extensively in Chapter 6 and Chapter 7.
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5.10.2 Qualitative Data Analysis
The qualitative analysis is basically intended to complement the quantitative analysis by providing a deeper understanding of demand for and impacts of Islamic microfinancial services on the clients’ households. It particularly attempts to uncover the pathways in which these services affect the lives of the clients, and other underlying operational issues of the IsMFIs from the clients’ perspectives. The information from the interviews is important as it can validate the findings from the survey in which direct and indirect impacts cannot be identified. This information can be obtained only by directly approaching the clients and requesting them to explain their views in more detail. By triangulating quantitative analysis, qualitative analysis and the literature review, the aim and objectives of this study can be met.
In this study, the qualitative analysis framework is based on coding method. In particular, this research employed thematic analysis in which emphasis is placed on what is said rather than on how it is said (Riessman, 1993). As semi-structured interviews were used in this study, the interview questions were designed to stimulate discussion so that the required information was collected.
The researcher recorded the interviews and also, made notes during the interview sessions. The records were then transcribed in Thai and translated into English by the researcher. The researcher then transferred the transcribed file into the NVivo programme for coding. The analysis of the qualitative data was performed using a coding technique. Firstly, the main themes were created to provide basic meanings and categories of words and phrases used by the interviewees and the main interview questions. The focused codes were created for each sub-theme to provide interpretive meanings of those words and phrases. Eventually, final- level codes were assigned to give explanatory power to each sub-theme. For each sub-theme, a brief analysis was provided to interpret and connect each sub-theme to the main theme.
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5.10.3 Secondary Data Analysis
Secondary data analysis is a descriptive process in which outreach and sustainability indicators were calculated from financial data. Outreach indicators include the number of clients, financing outstanding and growth, and total balance of deposits. Sustainability indicators consist of financial spread and operational self-sufficiency. Some other indicators such as efficiency ratios, profitability ratios, and Islamic-related indicators are also included in Chapter 9. Descriptive comparisons are made across the IsMFIs.