Joppe (2000, p.1) pointed that ‘ … an accurate representation of the total population under study is referred to as reliable if the results of a study can be reproduced under a similar methodology…’. Reliability, by definition, refers to the extent to which studies can be replicated. In order to satisfy the criterion of reliability in a piece of research – no matter it is quantitative or qualitative – it is important for the researcher to document the research procedure explicitly (Kirk and Miller, 1986). This is what Franklin and Ballan
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(2001) called the ‘audit trail’, which is important to provide a basis for checking the researcher’s dependability.
Therefore, the study documents:
• the employed research methods and the overall research design (including diagram presentation to show the explicit flow);
• the dependent and independent variable measures;
• the procedure for sample setting and the source of data used in the quantitative analysis;
• the data analysis and hypothesis testing procedures;
• the assumptions in the model and variable setting procedures;
The study also relies on publicly available secondary data sources which are audited or else published by responsible government offices. Before running the data in the model, the data character is observed through descriptive statistics and graphical observations. In addition, the required tests such as panel unit root test are employed to test for stationarity of the panel and time series variables.
5.6.2. Validity
Generally, there are three key types of validity in a quantitative study:
A. External Validity- refers to the extent to which the findings of a particular study can be generalized across populations, contexts and time (Dellinger and Leech, 2007). The quantitative study of this thesis appears to have less threat to external validity. This is because of low problem in data availability, sample size (census is used) and the quality of data (which is audited). More importantly, the study is a piece of mixed methods research in which the combination of qualitative and quantitative studies has the potential to achieve triangulation which is one of the important ways to enhance external validity (Bryman, 1988). This study examines the relationships among concentration and bank performance using both quantitative statistical technique and qualitative
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interpretation and description. By doing so, it is possible to achieve consistency in some findings, and thus increases the external validity of the overall research. In addition, the use of census in the quantitative research (which is a dominant research approach) enhances the generalization of result.
B. Internal Validity- is conceptualized as the degree to which the researcher is confident about the conclusion/inferences of the causal relationship between variables/events (Tashakkori and Teddlie, 1998). In a hypothesis testing study, internal validity is normally pursued through complex statistical procedures that enable control over extraneous variables (Johnson et. al., 2007). In this study, the assumed relationship between dependent variable and independent variables is based on theoretical foundation and the findings of empirical work. Several control variables that impacts the dependent variable are also introduced into the models following empirical works, regulatory standards, interview experiences and the business pattern of Ethiopian banks. Moreover, several statistical instruments are used to test the robustness of the estimated results and the assumptions in the regression model based on (Guajarati, 2003):
1. Normality of the residuals or errors
2. Linear relationship between the independent and dependent variable(s) 3. Homoscedasticity- equality of variance of the errors.
4. No autocorrelation between the disturbances 5. There is no perfect multicollinearity
Therefore, the model is tested for the above stated assumptions.
i. Model Diagnosis a. Tests for Normality
The hypotheses used in testing data normality are based on the data distribution that tests for:
Ho: The distribution of the data is normal Ha: The distribution of the data is not normal
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If a test does not reject normality, this suggests that a parametric procedure that assumes normality (e.g. a t-test) can be safely used. In addition to the formal tests for normality, data is also graphically examined.
b.Tests for Linearity
The ANOVA table contains tests for the linear, nonlinear, and combined relationship between variables. The hypotheses used in testing data normality are:
Ho: There is no linear relationship between variables, Ha: There is linear relationship between variables.
If the test for linearity has a significance value smaller than 0.05, this indicates that there is a linear relationship. Alternatively, a graphical approach is used to observe plots for linearity. Linearity is displayed by the data points being arranged in the shape of an oval.
c. Test for Multicollinearity
This is carried out using the analysis of the Variable Inflation Factor (VIF) statistics. Small inter-correlations among the independent variables is expressed with VIF ≈ 1. However, VIF>10 depicts collinearity is a problem.
VIF= 1/ tolerance, where tolerance= 1-R2 , R2 is the coefficient of determination.
In addition, correlation analysis is conducted to examine for multicollinarity problem.
d. Autocolleration
To test for the existence of autocolleration, the Durbin Watson test is employed. This module tests correlations between errors and assumes that the error terms are stationery and normally distributed with mean zero. The test statistic can vary between 0 and 4 with a value of 2 indicating that the residuals are uncorrelated. A value greater than 2 indicates a negative correlation and a value less than 2 depict a positive correlation.
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The Hypothesis to be tested is then: H0= ps= (s>0)
H1= ps=ps for some non zero p with/p/<1
e. Heteroskedasticity
The test of the presence of heteroskedasticity, the Breusch-Pagan/ Cook-Weisberg tests is employed. This test involves testing the null hypothesis that the error variances are all equal versus the alternative that the error variances are a multiplicative function of one or more variables.
H0 = Var(u/x1, x2….xn)=E(u)= 𝜎2
H1= Var(u/x1, x2….xn)=E(u)≠ 𝜎2
The null hypothesis is true when the model is homoscedastic. If the alternative hypothesis is true, the model is heteroskedastic.
C. Construct/content Validity – Construct validity threat arises when investigators use
inadequate definitions and measure variables based on those inadequate definitions (Modell, 2005). In this study, the treats to construct validity is limited as it forwards explicit definition for each variable via setting a conceptual framework as well as before running the model. Moreover, the use of multiple methods is likely to reduce the threats to the construct validity. The indicators used in the quantitative analysis are further are examined in the qualitative interviews so as to check the accuracy of the definition of indicators.