In order to test the hypotheses posed in this study a variety of statistical techniques are used to analyse data. Analysis of scale-based perception and more categorical
demographic quantitative data gathered through interviews with MSE owner-managers includes both univariate and multivariate techniques. Theoretical concepts introduced in chapter three are applied to augment explanations to results of various analyses. The statistical software (SPSS) version 17 is used to perform quantitative analysis.
Univariate analysis entails use of descriptive statistics and cross tabulations in exploring the properties of all key explanatory variables (in raw form) in the study. In most cases results are presented in tables and pair-wise comparisons are made between participant MSE owner-managers in the treatment and control groups. The significance or
otherwise of the differences between participant categories across most independent variables is tested using both parametric (for continuous variables) as well as non- parametric tests (for categorical variables).
Content analysis (using NVivo software version 8) is performed on the qualitative transcripts resulting from interviews with both MFI and BDS agency representatives and the independent researchers. For triangulation purposes, results from content analysis are presented concurrently with the results of univariate analysis of the MSE owner-managers‟ views to provide deeper insights and potential explanations to results. Additionally results of univariate analysis are used to identify variables that need to be controlled for in the subsequent multivariate analysis where the main hypotheses for this study are tested.
Multivariate analysis techniques, logistic and ordinary least squares (OLS) regression, are used to test the main hypotheses of the study as posed in chapter three. For example the logistic regression technique (used for dichotomous dependent variables) is used to test for the odds of continuing to use microcredit and of reporting satisfaction with BDS among participant MSE owner-managers. With regard to the question on the usefulness of microcredit, participant MSE owner-managers‟ intention to continue using
microcredit is the dependent variable whereas satisfaction with currently supplied BDS is the dependent variable used in testing for the perceived usefulness of BDS
(Hypothesis 5) among participant MSE owner-managers. In both cases the explanatory variables used relate to the terms and conditions surrounding access to either service.
108 For example participant MSE owner-managers‟ intention to continue using MFI sourced credit is expected to be explained by type of loan, loan term, the motive for seeking the loan, and loan interest rate among others (Roslan and Karim, 2009, see Godquin, 2004).
With regard to participant MSE owner-managers‟ likelihood of satisfaction with currently supplied BDS, the explanatory variables used include the source and type of BDS, its mode of delivery, perceived competence of BDS agency staff, and the MSE owner-manager‟s level of education among others (Faoite et al., 2004, Kotey and Sheridan, 2004). Justification for and details of measurement for all variables included in the multivariate analyses are presented in the relevant subsequent chapters which report the results of data analysis.
The OLS regression technique is used to test for the extent of covariance between receipt of microcredit and/or BDS, or neither, and MSEs‟ performance (Hypotheses 1 through 4). The dependent variable in these tests is MSE performance, which is a composite measure derived from participant MSE owner-managers‟ self-rated business performance on six financial attributes as indicated in the previous section (4.6). Before computing the MSE performance score, first the reliability of the five indicators used to measure MSE performance is assessed using Cronbach‟s Alpha test. Secondly, raw scores across the five attributes underlying performance are normalised (using Blom‟s formula). Normalisation is a re-scaling processes whereby Likert type ordinal scores are transformed into scores with approximate standard normal distribution properties i.e. resulting scores have a mean close to zero and standard deviation as close to one as possible (Gow, 2010). This means that the resultant MSE performance scores assume the attributes of standard continuous variables and are therefore more amenable to the least squares regression procedure unlike the Likert type ordinal scores.
In testing for MSE performance differentials across the four participant categories (as reflected in Hypotheses 1 to 4), consistent with the literature (see Masakure et al., 2009), several control variables are used; namely age, gender, owner-managers‟
education level and business affiliated family background, type of business activity, and how well established the business is. The extent of a business‟ establishment is a proxy for firm size, a factor that is commonly used (see Jayawarna et al., 2007, Kotey and Sheridan, 2004) in MSE performance studies. Controlling for other extraneous variables is crucial while testing for the impact of use of microcredit and or BDS on MSE
109 performance. This is because the beta coefficient in respect of each independent
variable entered into the OLS model reflects singly the expected influence of the
variable on MSE performance. In such a comparative manner it is possible to single out the most significant contributor to MSE performance while at the same time testing for the extent to which the entire model explains MSE performance.
The one-way analysis of variance (ANOVA) technique is used in testing for Hypothesis 6, assessing the fit between currently supplied BDS and MSE owner-managers‟ BDS needs. In this case the test is whether there is a significant difference in mean
performance scores (the dependent variable) between participant MSE owner-managers who are satisfied with current BDS and those who are not satisfied (the factor).
In addition the sample size of 160 MSE owner-managers participating in this study is considered adequate for correlation-based multivariate analysis. In testing for all the six hypotheses, collinearity is assessed through bivariate Pearson‟s correlation analysis. The decision rule in hypotheses tests in this study follows the conventional 95 per cent threshold.
4.8 Conclusion
This chapter provides a description of the methodology employed in investigating the main research question for this study; that is to assess the association of microcredit and BDS with the owner-managers‟ self-reported performance of their micro and small enterprises. The chapter notes that recent studies assessing the impact of microfinance have used different research designs, which may be classified into three categories; scientific, behavioural, and participatory action learning. Unlike the scientific approach, which is quantitatively oriented, the two other approaches are generally qualitative in nature. This study adopts a mixed method approach, triangulating quantitative data gathered from participant MSE owner-managers with results of qualitative interviews with MFI and BDS representatives and independent researchers working in the area. Specifically the study adopts a quasi-experimental design, with use of a control group and having an ex post facto orientation.
The sample analysed quantitatively consists of 160 haphazardly sampled participant MSE owner-managers operating from within four purposively selected cities in Kenya. That is, the unit of observation and analysis relates to MSEs, not households or
110 individuals as is the case with most MFI impact studies, even though it is the
perceptions of individuals intimately involved with these MSEs that are sought. The participants fall into four categories in respect of their use or non-use of microcredit and/ or BDS. There are approximately forty participants in each of the participant categories. Observations, that is self-reported MSE owner-manager performance ratings, from participant MSE owner-managers who do not use either microcredit or BDS are used for control purposes. The main analysis directed to achieving the purpose of this study is to compare these non-users with observations from their counterparts in each of the other three categories in testing for the impact of use of microcredit and/or BDS on the performance of MSEs, where performance is self-rated. Other analyses are conducted using, for instance, participant profile information.
In the chapter it is noted that quantitative data from MSE owner-managers is gathered through a semi-structured interview protocol while views from MFI and BDS
representatives and independent researchers are collected through open-ended interviews. All data collection instruments are validated by way of peer review with experts. As noted in this chapter, both univariate and multivariate analyses are
performed on the quantitative data with the aid of SPSS and NVivo software is used for the qualitative data. Detailed reporting of the sample profile and results of data analysis relating to the research questions and tests of hypotheses for this study appear in the next three chapters.
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