3.7 Research Instrument

Research Instrument is the tool that is

utilized in order to come up with the answer of the research questions of this

study, which also used to gather, examine, investigate an issue or collecting,

process, analyze and present the data in a systematic and objective to solve

the problem or to test a hypothesis. The researcher intends to collect relevant

information as many as possible from a variety of sources.

3.7.1 Data Collection

Procedure

Primary

Data

To collect the data in this research, the researcher using primary

data collection through the questionnaire. According to Sekaran and Bougie (2010),

questionnaire is a written set of questions given to the respondent to answer.

Respondents can give an answer with a mark on one or several answers that have

been provided, or write down the answer. The researcher using this questioner

for the primary tools to collect the data and the question based on

predetermined variables. The questionnaires were distributed during December

1th 2017 – January 20th, 2018.

3.7.2

Validity Test

Validity test assesses whether a scale measures what it is

supposed to measure (Hair et al., 2010). According to Malhotra (2010), the

validity of a scale can be explained by seeing how far can the differences in

observed scale scores reflect true differences in what is being measured, as

opposed to organised or unorganised error. The researcher asserts that the

purpose of validity test is to filter the construct and eliminate incorrect

statements from the construct of the present study. Validity test is used to

determine whether the questionnaire is valid or not.

The Pearson product-moment correlation coefficient, or PPMCC

for short, is a measure of the aptitude of a linear interrelation (dependence) between

two variables and is noted by r. Basically, a PPMCC attempts to draw a

straight line of best fit through the data of two variables, and the Pearson

correlation coefficient, r, indicates how far the distance of all these

data points are to this line (Statistical.laerd.com, 2013). The valid data represents

the proper statements of the construct that can be distributed to the sample respondents.

In a Pearson Correlation, the results are obviously somewhere

between -1 and 1. A perfect negative correlation between values is indicated

with the exact result of -1, while the closer the result get to 1 can be the

indication of a perfect positive correlation among the variables. On the other

hand, there is no linear relationship between the two variables if the result

is 0.

In many cases, it is very infrequent to obtain a

correlation of 0, -1 or 1. Obviously the correlation would be somewhere in

between -1 to 1. When the value of r gets closer to zero, the variation

around the line of best fit would be greater. To do the validity test, researcher

distributed 16 questionnaires to 16 sample respondents. Using SPSS version

20.0. To see the r to conclude the either the statement is valid or

invalid. The degree of freedom is equal to the number of sample size or df= n

(Triola, 2006). If df = 16 and alpha = 0.05, the r value should be more

than 0.468 to be considered valid. If r