46
D. Analysis Method
To answer all of the research questions, the writer intend to do the analysis through Descriptive Analysis and Multiple Linier Regression
Analysis Method. Here are the explainations of methodology used: 1. Descriptive Analysis
In this study, tests were performed by using descriptive to determine whether there is awareness of the effect of tax reform on individual tax
payers by using multiple linier regression methods and to simplify the calculation, so researchers will use the tools of SPSS Statistical Product
and Service Solution v17. Descriptive analysis is aimed to make the formulation or picture actual systematically and real actual factm
behaviour and also the relation between phenomenon that is looking for. Sugiono 1999: 112 in Astrid 2011: 27.
2. Multiple Linier Regression This research uses multiple linear regression models as follow:
Y =
αί + βί
1
X
1
+ βί
2
X
2
+ ℮ί OR
Y = αί + βί
1
Rat + βί
2
Ei + ℮ί
Where,
Y = Awareness of Individual Taxpayers
α = Intercept
β = Regression coefficient
Rat = Reform administration tax
Ei = Emotional intelligence
47
Regression analysis will be used to test hypotheses formulated
for this study. Two variables, namely reform administration tax and
emotional intelligence were entered. Multiple regressions will determine the significant relationship between dependent and
independent variables, the direction of the relationship, the degree of
the relationship and strength of the relationship. Sekaran 2006.
Multiple regression are most sophisticated extension of correlation and are used to explore the predict ability of a set of
independent variables on dependent variable. Pallant 2001. Two hypotheses generated. From the hypothesis it gives direction to assess
the statistical relationship between the dependent and independent variables. The convention of P value has been set as of 5 i.e. 0.05
used as evidence of a statistical association between the dependent and independent variables.
To gather the best model of research, researcher must perform other pre-tests. The test are: normality test, assumption test
heterodixity test, auto-correlation test, multi-colinearity test, and hypothesis test.
3. Data Quality Test To ensure the quality of data, the writer needs to conduct validity and
reliability test, and also classic assumption test.
48 a. Validity test
A tool is said valid if can answer as clear about variable that is counted. A questioner is said valid if statement in questioner can be
able to disclosure something that will be counted by its questioner. This validities test use Pearson correlation by counting correlation
between each score question with total score has significant level under 0, 05 so that its question is said valid and opposite. Ghozali
2009. This validity test use The Pearson Product Moment Correlation Method, which computing correlation of each indicator
from each variable with its total root. b. Reliability test
Instrument is said reliable if exists similar to data in different time. A questioner is said reliable if the answer someone toward question is
consistencies stable from one time to other. Ghozali 2009. Reliabilities test is used to count that variable is using really free from
mistake even though produce consistent the outcome although is tested much time. Testing the reliability is shown by using the
Cronbach’s Alpha coefficient. The standardized formula of Cronbachs Alpha to calculate reliability coefficient instrument can be
defined as: r =
1
−
∑
49 Where : r = reliability coefficient instrument
K = number of question
∑
= total variance particle = total variance
However, more flexibly, Sugiono 2005 suggests that the score of reliability differentiates on every variable. To interpret the range of
reliability for an instrument, a base directive is used as shown below:
Table 3.1 Reliability Instrument Scale
Source : Sugiono 2005 c. Classic Assumption Test
The Gaussian, standard, or classical linear regression model CLRM, which is the cornerstone of most econometric theory, makes 10
assumptions underlying of Ordinary Least Square method. Gujarati, 2003, in Ghozali 2006: 86. This research will focus on its 6 basic assumption in
context of the two-variable regression model.
Assumption 1 : Linear Regression Model. The regression model is linear
in the parameters
Interval Coefficient Level of Reliability 0.200
Very low 0.200 - 0.399
Low 0.400 - 0.599
Sufficient 0.600 - 0.799
High 0.800 - 1.00
Very High
50
Assumption 2 : X values The independent variable are fixed in
repeated sampling. Values taken by the regressor X are
considered fixed in repeated samples. More technically, X is assumed to be nonstochastic.
Assumption3 : Zero mean value of disturbance u
i
. Given the value of
X, the mean, or expected, value of the random disturbance term u
i
is zero. Technically, the conditional mean value of u
i
is zero. Symbolically, we have
Assumption 4 : Homoscedasticity or equal variance of u
i .
Given the value of X, the variance of u
i
is the same for all observations. That is, the conditional variances of u
i
are identical. Symbolically, we have
Assumption 5 : No autocorrelation between the disturbances. Given
any two X values, X
i
and X
j
i ≠ j, the correlation between
any two u
i
and u
j
i ≠j is zero. Symbolically, we have
Assumption 6 : Zero covariance between u
i
and X
i
, or Eu
i
X
i
= 0. By Assumption,
As noted earlier, given the assumptions of the classical linear regression model, the least-squares estimates possess some ideal or
Kategori