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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

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.

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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 (, 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.

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