This chapter mainly to discuss

the methodologies applied in gathering data and information needed in order to

carry out a successful research study and to contribute to the development of a

valid and critical thesis. In particular, this chapter describes how this study

is carried out in terms of settings, sampling size and types, data types and

collection technique, descriptive analysis and types of test that will carried

out.

The data uses in the study will

consist of both the independent variable and also the dependent variable. There

are two independent variables which are the total financing and also the total deposits

of the Islamic banking in Malaysia. The data will be in form of secondary data

where all of the information will be taken from other sources and not from

primary sources like questionnaires. In this study, there are two variables

which is the Islamic Bank’s total financing and total deposits which consist of

the 16 listed Islamic Banks in Bank Negara Malaysia. This study will include

both local and foreign Islamic bank that is a fully fledged Islamic bank and

banks that uses the Islamic banking window.

The overall population of banks

in Malaysia consists of several types of banking financial institutions which

include commercial banks for both local and foreign banks, banks with marketing

and representative offices in Malaysia, offshore banks in Labuan, investment banks,

merchant banks and also Islamic banks. All of the banks are regulated under the

supervision of the Malaysian central bank which is the Bank Negara Malaysia.

For this study, we had chosen a total number of 16 Islamic banks located in

Malaysia and they can be from either local or foreign Islamic banks. As

mentioned above, there are three types of Islamic banks in Malaysia namely the

full-fledge Islamic banks which are the local Islamic banks, the foreign

Islamic banks and also the conventional banks which have subsidiary that are

using Islamic banking products, called Islamic bank windows.

Purposive Sampling – It is a non-probability

sampling method which selected the variables based on the characteristics and the

objective of the study (ThoughCo.com, 2017). As for this study, we use the

total population sampling where we selected all of the Islamic banks and Islamic

banks windows in Malaysia.

The data type use for the study

is Secondary Data. Secondary data is the data that have already been collected

and are readily available from other sources. It also means that the material

created by other researchers can be reuse by the general research community

(Hox and Boeije, 2015).

The data gathered for this study were obtained as a

secondary data from various sources such as the World Bank archive and also

annual reports of IB.

Data obtained from Annual reports

starting in the year 2007. For total

deposits, it takes into account the savings deposit and demand deposits (Wadiah

and Tawarruq), Term deposit which consist of Mudarabah, Bai’Bithamman Ajil,

Wadiah and Tawarruq. For total financing it includes Bai’ Bithaman Ajil for house financing and other term financing,

Ijarah, Ijarah Muntahla Bittamlik, Murahabah, and other principles. Both of the

data collected were in the unit of MYR currency.

The Shapiro-Wilk test is a

hypothesis test procedure for determining if samples of data are from the same

distribution or in other words, to check whether the distribution of normal or

not. The test is non-parametric and entirely agnostic to what this distribution

actually is. The fact that we never have to know the distribution the samples

come from is incredibly useful, especially in software and operations where the

distributions are hard to express and difficult to calculate with. The reason

why we chose to use the Shapiro-Wilk rather than the other normality test is

because of its good power properties (Mendes and Pala, 2003).

To observe the basic nature of

the data, this study used descriptive statistics. Observing the TD, TF and GDP

descriptive statistics helps summarizing the data at hand to represent any

patterns or variations; this includes inspecting their minimum, maximum and

means (Rashwan, M. H , & Ehab, H. 2016). Standard deviation will also be

observed indicates the variation in the data set and to be checked whether it

is close to the mean value.

Trend analysis is

used in this study in order to find the aspects of the technical analysis of

the industry that will be evaluated. The movements of the indicators used for

this study have been analyzed to predict and assume the directions of the

indicators which in this case are the total financing of the Islamic banks, the

total deposits of the Islamic banks and also the economic growth of the country

which is being conveyed by using the GDP of the country. By doing this, it can

help us to understand more about the variables before using the data obtained

to find the objectives stated.

To inspect the stationary

properties of the series using the Augmented-Dickey-Fuller (ADF) test

procedure. The ADF test is used to determine the order of integration of each

series in the model. The order of integration is established by determining

whether the series is stationary or non-stationary. If the series is however

found to be non-stationary, then the series is differenced, and the resultant

differenced series is then tested to determine whether it is stationary or

non-stationary (Dickey

and Fuller, 1979).

The hypothesis testing of this

study consists of the Spearman Correlation and also the Multiple Linear

Regression analysis to find the results which can accept or reject the

hypotheses stated. In order to continue with the testing, it is imperative to

consider the fact that the hypotheses stated that there will be two condition

of testing which are the periods of the data used. The first one is the

immediate effect of both of the independent variables towards the dependent variable.

It took the consideration of looking on how does the independent variables

could affect the GDP of Malaysia in a 1-year period, hence it was called

immediate. While the other one is to find the relationship of the variables in

a short term period which in this case is a 5-year period. The formulas to

differentiate the two time frames are as follows:

As shown

above, the immediate affect can be evaluated by adding 1 year to a GDP for each

independent variable that we used. For example, to find the immediate effect of

the total financing of the year 2014 towards the GDP, we need to add up 1 year

to the GDP which will be 2015. Meaning that we can now see the effect brought

by the independent variable towards the GDP in the 1 year after. The reason why

we chose to do both the immediate effect and the short-term effect was to see

the comparison between the two time frame and how the independent variables can

affect the GDP. By doing this, we can find whether or not the Islamic banks in

Malaysia can affect the economic growth and if they do, how fast can they

affect it. Furthermore, this is the first study to use the

immediate effect on the effects

of the total deposits and total financing of Islamic banks towards the economic

growth of Malaysia.

Spearman Rank Correlation is a

non-parametric that is going to be used in this study. It measures the degree

of association between variables. he

Spearman correlation coefficient is usually adopted when

the assumption of

the bivariate normal distribution is not tenable (R.

Artusi, P. Verderio, E. Marubi, 2012). Using sample denoted by rs and is by design will be

compelled as follows

The spearman correlation observes

the p-value to determine whether it is significant or not. If the p-value is

less than 0.05, it indicates that it is significant and vice versa. The

Spearman Correlation can also be interpreted by examining how the sample is

close to ±1 the higher the strength of both

variables towards one another.

Correlation is

an effect that can be describe the strength of the correlation using the

description given below for absolute value rs:

The regression analysis will be used in order to

estimate the relationship among the variables whether they have positive or

negative relationship with each other. It is to investigate the linear

relationship between IV and DV (Yan, Xin 2009). One variable is considered to

be an explanatory variable and the other is considered to be a dependent

variable model (Stat. 2016). Multiple linear regressions (MLR) however, are a

method that uses several explanatory variables to predict the outcome of a

response variable. The multiple linear regression equation

for this study can derive as follows:

In order to know the significant

between all variables, we will observe the F statistics and probability. If the

probability is <0.05, it shows significance between variable and will be
insignificant if >0.05. F statistic is a value derives from running an

Analysis of Variance (ANOVA) in the regression analysis to find out if the

means between variables are significantly different. F test will show if a

group of variables is statistically significant.

Overall, the sources of secondary

data, the sampling size, data collection technique, trend and descriptive

analysis was stated in this chapter. This study will use Shapiro-Wilk for

normality test with Spearman Correlation and Multiple Linear Regression used in

determining the significance between the independent variable and the dependent

variable.. Besides that, Statistical Package for the Social Sciences (SPSS) and

E-views had been chosen as the instruments to progress and run the data. The

generated result will be interpreted and discussed in the following chapter.