Research scope Multiple Regression Analysis Coefficient of Determination Test R

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CHAPTER III RESEARCH METODOLOGY

A. Research scope

The scope of this research is the field of marketing that focuses on how much influence brand awareness X1, perceived brand quality X2, brand association X3, and brand loyalty X4 as the independent variable on purchase decisions Y. This research conducted in Soekarno-Hatta International Airport on September 2014 until March 2015.

B. Sampling Method

1. Population

Population is the generalization region consisting of objects or subjects that have certain qualities and characteristics defined by the researchers to learn and then drawn conclusions Sugiyono, 2007: 115 in addition to the population could also be defined as a sum total that includes all members of the study Istijanto, 2009: 113. The population in this study is that consumers Garuda Indonesia in Jabodetabek

2. Sample

The sample is part of the number and characteristics possessed by the population. When a large population and researchers may not learn all that there is in the population, for example, because of limited funds, manpower, time, the researcher can use the sample drawn from that population Sugiyono, 2007: 116. According Istijanto 2009: 113 as a part of the sample is drawn from the population. As a result, the sample is 48 always a smaller part of the population. The technique of sampling using the nonprobability sampling technique. This technique does not use the selection procedure but rather an opportunity to rely on personal judgment of researchers Malhotra, 2009: 371. The sampling technique used by the author is the sampling method judgment sampling that samples taken in accordance with the desired characteristics of the population, yet respondents were concerned, anytime and anywhere be found, used as elements of the study sample Hamid, 2007: 32. Sampling was based on the consideration that the respondents ever buy or use the services of Garuda Indonesia. Samples will be selected by the author as a source of data required for this study is the consumer Garuda Indonesia in Soekarno-Hatta International Airport who lived in Jabodetabek. In this study, sampling adapted to the theory of Roscoe in Sugiyono 2009: 129-130, that the sample size is appropriate when the research will perform a multivariate analysis, the number of members of the sample of at least 10 times the number of variables studied. The number of variables in the study was 5, so that the number of samples required by 50 10 x 5 sample of respondents on Garuda Indonesian consumers Jabodetabek.

C. Data Collection Method

The information generated by the marketing research is the end result of processing during the research process. Information is basically starting from raw materials called the data so it is often also referred to as 49 raw data Istijanto, 2009: 35. In obtaining the data, the author uses primary data and secondary data

1. Primary Data

Primary data is data obtained directly from the source. Meanwhile, according Istijanto 2009: 44 The original primary data collected by researchers to answer the research question specifically. Primary data in this study were obtained in full from the respondent through a list of questions. Primary data collected includes identifying data of respondents, respondents opinions about decisions concerning the purchase of brand awareness, perceived brand quality, brand association, brand loyalty on Garuda Indonesia. Primary data collection method used in this research is a method of distributing a questionnaire on consumer Garuda Indonesia. Respondents are consumers who never use or purchase services in the Greater Jakarta Garuda Indonesia. Data collection will be conducted through a questionnaire submitted to each of the selected respondents. With a personal questionnaire, researchers can deal directly with the respondent and can provide the necessary explanations. The data in this study obtained directly from the questionnaires questionnaire by the respondent. The questionnaire in this study consisted of questions derived from each study variable indicators. The questions on the questionnaire prepared by the Likert scale. Likert scale is a widely used scale that asked the respondent indicates the 50 degree of approval or disapproval of each of a series of statements mengenaik stimulus object. Generally measurements using five response categories ranging from strongly agree and strongly disagree Malhotra, 2009: 298. Scale 1-5 to obtain data that is numerical and given a score or value. For the category of questions with answers strongly disagree or strongly agree. Table 3.1 Likert Scale No Answer Score 1 Strongly Agree 5 2 Agree 4 3 Doubtful 3 4 Disagree 2 5 Strongly Disagree 1 Source: Maholtra, 2009 Figures 1 one shows that the respondents giving negative responses strongly disagree to the questions posed, while the number 5 five showed positive responses strongly agree.

2. Secondary Data

According Istijanto 2009: 38 secondary data secondary data means both or not directly from the source instead of the other party. Secondary data is a type that is not cultivated itself was collected by 51 researchers. This data can be obtained from the literature, previous research journals, magazines, and data necessary documents in this study.

D. Data Analysis Method

1. Data Quality Test

This stage is the stage that is very important and decisive. At this stage the data is processed such that it successfully concluded the truth that can be used to address issues raised in the study. Data analysis methods used in this study using multiple linear regression analysis.

a. Validity Test

Validity is the level of research instruments to express the data in accordance with the matter to be disclosed. In other words, the validity indicates the extent to which a measuring instrument that can be used to measure what should be measured. Validity test is used to determine the feasibility of the items in a list of questions to define a variable. The list of questions generally support a group of specific variables. A questionnaire as valid if there are similarities between the data collected by the data actually happened on the object under study. Sugiyono, 2004: 172. In determining whether or not an item that will be used, usually to test the significance of the correlation coefficient in the minimum limit of correlation of 0.30, meaning that an item is considered valid if the total score is greater than 0.30 Duwi Priyatno, 2010: 90. 52

b. Relialibility Test

Reliability test is a measure of the stability and consistency of the respondents in answering the issues related to the constructs question is a variable dimension and are arranged in the form of a questionnaire. Said to be reliable or reliable instrument if someone answers to questions are consistent or stable from time to time. Reliability test is used to measure the variables used that are completely free of errors so as to produce a constant result though was tested several times. Reliability calculations performed using Cronbach Alpha statistical test. A construct or variable said to be reliable if the Cronbach Alpha value 0.60 Nunnally in Ghozali, 2005

E. Classical Assumption Test

1. Normality Test

Normality test aims to test whether the regression model, or residual confounding variable has a normal distribution Ghozali, 2005: 110. Good data and fit for use in research is one that has a normal distribution. Normality of data can be viewed in several ways, including by looking at the normal curve p-plot. A variable is said to be normal if the distribution of the image data points are spread around the diagonal line, and the spread of the data points in the direction to follow a diagonal line. 2. Multicollinearity Test According Ghozali 2005: 91, multicollinearity test aims to test whether in the regression model found a correlation between the 53 independent variables independent. Good regression model should not happen correlation between independent variables. If the independent variables are correlated, then these variables are not orthogonal. Orthogonal variable is the independent variable that the correlation between the members of the independent variables equal to zero. To detect the presence or absence of multicollinearity in the regression model are as follows: a. R2 value generated by an empirical regression model estimation is very high, but individually many independent variables are not significantly affect the dependent variable. b. Analyzing the correlation matrix of independent variables. If there is a correlation between variables were quite high generally above 0.90, then this is an indication of the presence of multicollinearity. Not a high correlation between the independent variable does not mean free of multicollinearity. Multicollinearity may be due to the effect of the combination of two or more independent variables. c. Multicolinearity can also be seen from 1 the value of tolerance and his opponent 2 variance inflation factor VIF. Both of these measurements indicate each independent variable Which is explained by the other independent variables. In simple terms each independent variable becomes dependent variable dependent and diregres against other independent variables. Tolerance measures the variability of independent variables selected when described by other independent 54 variables. So a low tolerance value equal to the value of VIF high because VIF = 1 tolerance. Cut-off value which is commonly used to indicate the presence of multicollinearity is a tolerance value 0.10 or equal to the value of VIF 10. Each investigator should determine the level of collinearity that can be tolerated. For example, the value of tolerance = 0:10 collinearity level equal to 0.95. Although multicollinearity can be detected with the value of tolerance and VIF, but we still do not know where the independent variables are the mutually correlated. 3. Heteroscedasticity Test According Ghozali 2005: 105, heteroscedasticity test aims to test whether in the regression model occurs inequality variance of residuals of the observations to other observations. If the variance of the residuals of the observations to other observations remain, it is called and if different homokedastisitas called heterocedastity. Good regression model is that homokedastisitas or not happen heterocedastity. Most data crossection containing heterocedastisity situation because this data to collect data that represent various sizes small, medium, large. One way to detect the presence or absence of heteroscedasticity is by looking at the graph plots the predicted value of the dependent variable dependent is ZPRED with residual SRESID. Detection of the presence or absence heterocedastisity can be done by looking whether there is a specific pattern on the scatterplot graph between SRESID and ZPRED 55 where Y is the Y axis that has been predicted, and the X axis is the residual prediction Y - Y in fact who has in-studentized. With the analysis if there is a specific pattern, such as the existing dots forming a pattern of regular wavy, widened and then narrowed, it indicates there has been a heterocedastisity and if there is no clear pattern, as well as points spread above and below the 0 on the Y axis, it does not happen heterocedastity.

F. Multiple Regression Analysis

Multiple regression analysis is a tool to predict the value of the effect of two or more independent variables on the dependent variable. Which aims to prove the existence of a functional relationship or a causal relationship between two or more independent variables Nugroho, 2005: 43. Multiple linear regression formula: Bhuono Agung Nugroho, 2005: 43. Where: Y = Purchase Decision a = Constant Y if X = 0 b 1 = Coefficient Regression brand awareness X 1 = Brand Awareness b 2 = Coefficient Regression perceived brand quality X 2 = Perceived Brand Quality b 3 = Coefficient Regression brand association Y = a + b 1 x 1 + b 2 x 2 + b 3 x 3 + b 4 x 4 + e 56 X 3 = Brand Association b 4 = Coefficient Regression brand loyalty X 4 = Brand loyalty e = Standard Error

G. Coefficient of Determination Test R

2 The coefficient of determination R2 aims to find out how much the ability of independent variables explaining the dependent variable. The fundamental flaw in the use of the coefficient of determination is the number of independent variables was entered into the model. Every additional one independent variable, then surely increase R2 does not matter whether these variables significantly influence the dependent variable. Therefore, many researchers recommend using Adjusted R2 values upon which to evaluate the best regression model. Unlike the value of R2, adjusted R2 value can go up or down when the independent variable is added to the model Ghozali, 2005: 83. In fact adjusted R2 value can be negative, although the desired must be positive. According to Gujarati in Ghozali, 2005: 83, if the obtained empirical test indigo negative adjusted R2, then the value is considered to be zero. According Nachrowi and Usman 2006: 108, said that to see the influence of the independent variable on the dependent variable, one of the indicators that can be used is the value of beta Standardized Coefficient contained in the 57 table coefficient, this figure may indicate the influence of variables independent of the dependent variable.

H. Theoretical Hypotheses