Data Analysis Methods

5.6.2.1 Statistical Analysis for the Questionnaire Survey

After the process of data collection, the next step is to analyse and interpret the data. The researcher has used IBM SPSS Statistics version 20 to analyse the data for this study. This section presents the methods of analysis used and the details of the particular tests used are explained below:

1. Descriptive analysis

Descriptive analysis or descriptive statistics describe the phenomena of interest; for instance how frequently certain phenomena occur (frequencies), the mean or average score of a set of data collected, and the extent of variability in the set (central tendency and dispersion) of the dependent and independent variables (Sekaran, 2003). For this study, the researcher used frequency distribution and measures of central tendency (Mean) to describe the data in the first part of analysis.

2. Factor Analysis

Factor analysis helps in reducing a vast number of variables to a meaningful, interpretable, and manageable set of factors (Sekaran, 2003). It is employed in relation to multiple-indicator measures to determine whether groups of indicators tend to bunch together to form different clusters, referred to as factors (Bryman and Bell, 2011). In this study, factor analysis is used to reduce the number of variables related to bank selection criteria to produce a manageable set of factors.

95 3. Kruskal-Wallis Test

Kruskal-Wallis Test is the non-parametric alternative to a one-way between groups analysis of variance. This test allows researcher to compare the scores on some continuous variable for three or more groups which means that scores are converted to ranks and the mean rank for each group is compared (Pallant, 2001). This test is used to test the statistical differences between the means for each of the selection factors among ethnic groups in Malaysia.

4. Mann-Whitney Test

The Mann-Whitney test is a nonparametric test to examine significant differences between two different groups and when the dependant variable is measured on an ordinal scale and the independent variable on a nominal scale (Sekaran, 2003). This test allows the researcher to compare scores for two different groups such as between male and female customers.

5. Regression Analysis

Regression analysis is a statistical tool to investigate the relationships between variables and it is used when the researcher seeks to ascertain the causal effect of one variable upon another (Sykes, 1993). In this research, regression analysis is used to identify the relationship between variables (service quality, satisfaction, loyalty) as well as to identify the significant independent variables that constituted strong predictors to customer satisfaction. In particular, regression analysis is used to determine which dimension of service quality that has a relationship and predicts customer satisfaction, indicating a prediction of one variable is made from another. Since this investigation involves more than two independent variables, multiple regression analysis is conducted to assess the effect of the six service quality dimensions on customer satisfaction. Multiple regression analysis shows how much variance in the dependant variable can be explained by the independent variables, which indicate the relative contribution of each independent variable (Pallant, 2001).

96 Regression Equations

Regression Equation 1

Taking into account the use of six service quality dimensions as independent variables to explain the dependent variable which is customer satisfaction, the following regression equation is formulated:

CS = ɑ + b1 TG + b2RS + b3EP + b4AS + b5RL + b6CE + e Where:

CS: Satisfaction; TG: Tangibles; RS: Responsiveness; EP: Empathy; AS: Assurance; RL: Reliability; CE: Compliance-Ethical; ɑ: constant; e: error

Based on the regression equation above, customer satisfaction in this study is measured or predicted by the explanation of the relationship between satisfaction and service quality dimensions. There are different types of multiple regression analysis (Pallant, 2001). The often used method is stepwise as it produces output which is simpler to interpret (Foster, 1998), it considers variables to include in the model and chooses the best predictors of the dependent variable (Hair et al., 1998). Similarly, Tabachnick and Fidell (2001) also shared the same view and suggested that the method skims the unnecessary variables and accepts the mostly meaningful and significant variables to the model.

Regression Equation 2

With the inclusion of independent and dependent variables, the following regression equation is formulated:

CL = ɑ + b1 TG + b2RS + b3EP + b4AS + b5RL + b6CE + e Where:

CL: Loyalty; TG: Tangibles; RS: Responsiveness; EP: Empathy; AS: Assurance; RL: Reliability; CE: Compliance-Ethical; ɑ: constant; e: error

97 Regression Equation 3

CL = ɑ + b1 CS + e Where:

CL: Loyalty; CS: Satisfaction; ɑ: constant; e: error

Control variables

Control in scientific research refers to control of variance; for example in setting up an experiment, there is an experimental group and a so-called control group as a form of control (Pedhazur, 1997). However, in non-experimental research, the predictors are beyond the manipulative control of the researcher suggesting the use of statistical control is important because the researcher has to be able to control the effects of some variables while investigating the effects of other variables (Pedhazur, 1997). In this study, demographic variables such as gender, age, ethnicity, income, education, and duration of banking relationship are considered as control variables.

5.6.2.2 Validity and Reliability

When using a questionnaire to obtain primary data, it is crucial to ensure the validity and reliability of the data collected. The questionnaire used in this study has gone through several stages before it was distributed. Firstly, the draft of the questionnaire was reviewed by experts from the university to judge how well the scale items measured all of the constructs and variables in order to ensure content validity. Next, the draft was piloted to several academics in Malaysia as well as the actual customers to determine the appropriateness of the questions listed in the questionnaire. Then, a reliability test was conducted to examine the consistency of the instrument.

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One of the most commonly used techniques in measuring the reliability of the questionnaire items is Cronbach‟s alpha. Cronbach‟s alpha is based on the average correlation of items within a test if the items are standardised. If the items are not standardised, it is based on the average covariance among the items. Cronbach‟s alpha ranges in value from 0 to 1. The closer the Cronbach‟s Alpha is to 1, the higher the internal consistency reliability. Pallant (2001) suggests that for a set of items that is to be accepted as having satisfactory internal consistency and reliability, the Cronbach‟s Alpha should be greater than 0.7. In this study, the value for Cronbach‟s Alpha in the questionnaire is 0.959 which indicates that all items are considered reliable with high consistency for further investigation. Table 4.2 below shows the Cronbach‟s Alpha value derived from the SPSS software.

Table 5.2 Reliability Statistics Cronbach's Alpha Cronbach's Alpha

Based on Standardized

Items

N of Items

.959 .960 56

5.6.2.3 Thematic Analysis for the Semi-structured Interview

There are various approaches to the analysis of qualitative data. Thematic analysis is considered as one of the most common ways in analysing qualitative data (Bryman and Bell, 2011). This is due to the flexibility of this type of analysis which could potentially provide good data (Braun and Clarke, 2006). Furthermore, thematic analysis is useful in organising and describing the data set in order to identify, analyse and report patterns or themes within data (Braun and Clarke, 2006). In this type of analysis, the investigator looks for themes which are present in the interviews and subsequently provides a framework to compare the answers from different interviewees (Gomm, 2008). In this study, thematic analysis is utilised in order to understand and making comparisons between all set of answers from the interview participants.

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Under thematic analysis, there are common strategies in analysing the interview data. The investigator needs to decide what are the themes, what will count as evidence of a theme, coding a transcript as an indication that this statement refers to this theme, and then starts the analysis in terms of which interviewees said that, and what particular theme it relates to (Gomm, 2008). Those are the strategies being considered by the researcher for this study. Firstly, the recorded interviews were manually transcribed into written texts by the researcher in order to analyse the data thoroughly. All of the interview conversations were typed in a separate file according to each interviewee. Secondly, the researcher read through all of the transcribed data to obtain general overview of the information provided by the interviewees and reflects on the meaning of the answers before any data classification was made. Next, the written texts were then separated based on each question so that responses were collectively gathered for that particular question. Each response from the interviewees was then coded and categorised to identify the themes that the data belong to. Then, the researcher sorted out the responses into categories or themes based on the questions. Finally, the themes that dominate the responses are tied together into a descriptive explanation by the researcher.