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CHAPTER III RESEARCH METHODOLOGY
A. Scope of Research
This research was conducted to analyze the influence of differentiation strategy product differentiation, image differentiation and word of mouth to
purchase decision on Maichi Chip product. The research will be conducted at Sharif Hidayatullah State Islamic
University Jakarta, its implementation in March 2012 until the completion writing, determination of the target is consumers who have purchased product
Maichi Chip. The case studies of Maichi Consumer Sharif Hidayatullah State
Islamic University in Jakarta because Maichi product very popular in young people and usually Maichi sold among students like in State Islamic University in
Jakarta. This research used a method quantitative causal research that aims to clarify
the effect of independent variable Product Differentiation X1, Image Differentiation X2 and Word Of Mouth X3 to dependent variable Purchasing
Decision Y.
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B. Sampling Method
According to Sugiyono 2009: 81 sample is a member of the selected population using a specific procedure that is expected to represent its
population. Meanwhile based on Malhotra 2009:370 sample is a subgroup of the elements of the population selected for participation in the study.
According to Malhotra 2009:377 convenience sampling is a non probability sampling technique that attempts to obtain a sample of convenient
element. The selection of sampling units is left primarily to the interviewer. In the development of this study researcher used a 100 questionnaire
respondents. The use of these 100 questionnaires with technical reasons is greater speed of data collection and lower cost. Cooper and
Schindler,2006:403 The use of these 100 questionnaires with technical reasons because the
proper sample for this research between 30 to 500 people. Sugiono,2010: 74
C. Data Collection Method
1. Primary Data
Primary data is data the researcher collects to address the specific problem of the research question at hand. Cooper and Schindler,2006:89
Primary data obtained from direct consumer review how the object of
research and the techniques used Questionnaire.
According to Malhotra 2009:330 questioner is a formalized set of question for obtaining respondent. In this study the author ask the question in
writing.
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2. Secondary Data
According to Cooper and Schindler 2006: 89 secondary data is the result of studies done by others and for different purpose than the one for
which the data are being reviewed. Secondary data is data obtained indirectly or through another party, or
a historical report prepared in the archives, published or not. Secondary data use: book, magazines and the internet. Malhotra ,2009:124
D. Analysis Method
1. Validity and Reliability Test
a. Validity According to Malhotra 2009: 316 validity is the extent to which
observed scale scores reflect true differences among objects on the characteristic being measured, rather than systematic or random
errors. According to Duwi Priyatno 2010:90 in determining the worth
absence of an item to be used, usually performed significance test of correlation coefficient at 0.30 limitations minimal correlation, meaning
that an item is considered valid if the total score is greater than 0.30. Validity
and reliability
tests conducted
by distributing
questionnaires to 100 respondents around Uin Syarif Hidayatullah Jakarta, in which questionnaire contains 15 questions statements that
must be answered by the respondents and then the data using software Product Statistics SPSS 20 for Windows.
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b. Reliability Reliability refers to the extent to which a scale produces
consistent results if repeated measurements are made. Therefore, reliability can be defined as the extent, to which measures are free
from random error Maholtra, 2009:315 According to Ghazali 2006:46 reliability measurements can be
done in 2 ways: 1 Measure or measurements repeated: here someone will be given
the same questions at different times, and then see if he remains consistent with the answers.
2 One shot or one-time measurement: here measurement only once and then the results were compared with another question or
measure the correlation between answers to questions. This research will use one time measurement that using Cronbach
alpha test α. A variable is said to provide reliable if the Cronbach
alpha values 0.60. Ghozali, 2006:46
2. Classical Assumptions Test
a. Multicollinearity Test According to Imam Ghozali 2006:95 multicollinearity test aimed to test
whether regression model is founded correlation among independent variables. To detect the presence or least multicollinearity in the regression
model is as follows:
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1 The value of R
2
is generated by an empirical regression estimates are very high, but individually variable, independent variables are many
that do not affect the dependent variable. 2 Analyzing the correlation matrix of variables-the independent
variable. If there is a correlation between independent variables is quite high usually above 0.90, then this is an indication of
multicollinearity. If below 0.90, the absence of multicollinearity. 3 Multicollinearity also can be seen from the value of tolerance and
Variance Inflation Factor VIF. Both these measures indicate each independent variable which is explained by other independent
variables. Tolerance measures the independent variables were selected that are not explained by other independent variables. Low
tolerance value equal to a high VIF value because VIF = 1Tolerance. Value commonly used to indicate the presence
multicollinearity is tolerance value 0.10 and the value of VIF 10. Each investigator must determine the level of colinearity which it still
can be tolerated. b. Heteroscedasticity Test
According to Imam Ghozali 2006:125 heteroscedasticity test aimed to test the regression model. In the regression model, there are differences
on variants from one observation to others. If variants from residual constant, so it called heterokesdastisity. A good regression model if there
is no heterokesdastisity.
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According to Duwi Priyatno 2012:158 glejser test is done by regressing between the independent variable with residual absolute value.
If the value of significance between independent variable with absolute residuals more than 0.05, so there is no problems heteroscedasticity. Step
analyses in SPSS are as follows: 1. Find the value of unstandardized residuals: click Analyze
Regression Linie 2. Classify the dependent and independent table
3. Click Save unstandardized Ok 4. Search for absolute residual values: click Transform Compute
Variable 5. ABS_RES click on the Target Variable and enter the Numeric
Expression unstandardized residuals at the start with the words ABS 6. Regressing the independent variable with the absolute value of
residuals: click Analyze Regression Linear 7. Enter ABS_RES of Dependent table and enter the variable X1, X2,
X3 in the Independent table OK c. Normality Test
According to Imam Ghozali 2006:147 normality test is a test of the normality of data distribution. Normality test is a test of the most widely
performed by parametric statistical analysis. The use of normality tests because there is a parametric statistical analysis, the assumptions that
must be owned by data is that the data are normally distributed. The
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purpose normally distributed data is that data will follow a normal distribution form.
There are two ways to detect whether or not residual normal distribution, those are with graph analysis and statistical tests. One of the
easiest ways to see the normality of residuals is to look at a histogram graph comparing observational data with the distribution of near-normal
distribution. Normal distribution will create a straight line diagonal and plotting residual data will be compared with the diagonal line. If the
residual data distribution is normal, then the line that describes the actual data will follow the diagonal line.
E. Multiple Linier Regression
Multiple linear regression analysis is to measure the magnitude of the effect between two or more independent variables and to predict dependent
variable using the independent variable. Duwi Priyatno, 2012:127
Multiple Linear Regression Equation:
Y = a + ß1X1 + ß2X2 + ß3X3
Y =Dependent Variable Purchasing Decision
X1, X2, X3 =Independent Variable Product differentiation X1, Image
differentiation X2 and Word of mouth X3 ß1, ß2
= Regression Coefficient a
= Constanta Number
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From the counting with SPSS 20 gain the information and explanation on the coefficient determination, F test, and T test to answer the
formulation of the problems. These are the following explanation that is connecting to the problem above, that is:
a. The Coefficient of determination Test R
2
According to Imam Ghozali 2006:202 the coefficient of determination R
2
essentially measure how far the ability of models to explain variation in the dependent variable. The value determination of
coefficient is between zero and one. The R
2
is small means that the ability of independent variables in explaining variations in the dependent variable
is very limited. Value close to one berate the independent variables provide almost all the information needed to predict the variation of the
dependent variable. Basic weaknesses use the coefficient of determination is biased
towards the number of independent variables entered into the model. Each additional one independent variable, then R
2
would increase, no matter whether these variables affect the dependent variable or not. Therefore, in
this research used is the R Square that have been adapted or Adjusted R
2
as adjusted for the variables used in this research.Adjusted R
2
value can rise or fall if an independent variable added into the model.
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b. Simultaneous Test of F test F test essentially indicates whether all the independent variables or
independent variables included in the model have jointly influence on the dependent variable. The probability is smaller than 0.05, then the result
means that there are significant effects of independent variables collectively against the dependent variable. Step to examine the hypothesis
with F test as follow Ghazali,2006, 203 : 1 Determine Hypothesis:
Ho: β1, β2, β3=0, there is no significant influence simultaneously among variable of product differentiation,
image differentiation and word of mouth to purchase decision Ha: β1, β2, β3≠0, there is significant influence simultaneously
among variable of product differentiation, image differentiation and word of mouth to purchase decision
2 Determining level of significance Criteria for testing the significance level is amount 5 or
α =0.05.
3 Determining the criteria acceptance and reject of Ho If F test F table, so Ho rejected and Ha accepted, means
independent variable simultaneously have significant influence to dependent variable.
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If F test F table, so Ho accepted and Ha rejected, means independent variable simultaneously don’t have significant
influence to dependent variable. c. Partial test of T-test
This test method is used to partial test or t test aims to determine how big the influence of each independent variable X individually partially
toward dependent variable Y Ghozali, 2006:88. T test method is as follows two tail test:
1 Determine Hypothesis Product differentiation hypothesis:
Ho: β1=0, there is no significant influence partially between Product differentiation and purchase decision.
Ha: β1≠0, there is significant influence partially between product differentiation and purchase decision.
Image differentiation hypothesis: Ho: β2=0, there is no significant influence partially between
image differentiation and purchase decision. Ha: β2≠0, there is significant influence partially between product
differentiation and purchase decision Word of mouth hypothesis:
Ho: β3=0, there is no significant influence partially between word of mouth and purchase decision.
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Ha: β3≠0, there is significant influence partially between trust in word of mouth and purchase decision
2 Determining level of significance Lind et al.,2010:338 Criteria for testing the significance level is amount 5 or
α = 0.05
, because use two tailed so α value divided 2 so 0.025 3 Determining t table Lind et al.,2010:338
Distribution of t table searched on α = 5 with degree of
freedom df = n-k-1 number of questionnaire-number of independent variable-1
4 Comparing value t test with value t table Suharyadi,2009:90 If -t test - t table it means Ho rejected and Ha accepted, so
independent variable partially has significant influence toward dependent variable.
If t test + t table it means Ho rejected and Ha accepted, so independent variable partially has significant influence toward
dependent variable.
Measurement Scale of Variable
A measurement of each variable in this research uses linkert scale to measure attitudes, opinion and perceptions of individuals or groups social phenomena.
Sugiyono, 2009:94 By using a Likert scale, the measurement variable is an indicator variable that
will be
outlined. Using a Likert scale of
five 5 levels to express the
attitude or the respondents answer as follows:
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Table 3.1 Likert scale of five 5 levels
Sugiyono, 2009:94 No
Range Weight
1 Strongly agree SA
5 2
Agree A 4
3 Neutral N
3 5
Disagree D 2
6 Strongly disagree SD
1
F. Operational Variable
Kategori