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

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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.


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 < 0.468, the statements will not be considered valid. Variables will be valid if the total correlation is higher than r value. 


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