Questionnaire Data Collection Technique
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Table 3.5. The Description of the Validation Questionnaire Result No.
Statements Converted Score x
Mean M Category
Total Score Mean
The results of the questionnaires were calculated using descriptive statistics.
To analyze the data the researcher employed three steps: 1 collecting the raw data, 2 converting and scoring the data for quantitative analysis, 3 categorizing the
scores into a scale of five. The converted scores were adapted from Best 1977 in Maharani 2013 as presented below.
Table 3.6. The Conversion Table of the Raw Scores into the Converted Scores Raw Scores
Meaning of Scores Converted Scores
4 Strongly agree
2 3
Agree 1
2 Disagree
-1 1
Strongly disagree -2
After the converted scores were gathered, they were classified using Criterian Reference Evaluation CRE or known as Penilaian Acuan Patokan
PAP, proposed by Sukarjo 2006. The detailed explanation on how to get the
range of scores can be seen in the table below.
Table 3.7. A Five Scale Using Criterian Reference Evaluation CRE Category
Score Interval
Very High Very Good X X
i
+ 1,80 SD High Good
X
i
+ 0,60 SD X ≤ X
i
+ 1,80 SD Fair
X
i
- 0,60 SD X ≤ X
i
+ 0,60 SD Low Poor
X
i
- 1,80 SD X ≤ X
i
- 0,60 SD Very Low Very Poor
X ≤ X
i
- 1,80 SD
71 Notes: X = actual score
X
i
ideal mean score = maximum ideal score + minimum ideal score
SD standard deviation =
6
maximum ideal score - minimum ideal score The maximum score above refers to the highest converted score i.e. 2, while the
minimum score is the lowest converted score i.e. -2. Furthermore, the final mean score was interpreted to gain the meaning about the overall quality of the product.
According to Table 3.7., if the score is considered very good, it means that it does not need any revision. Then, if the score is classified as good, it means that
the revision is optional. Next, if the score is considered fair, it is necessary to conduct more exploration on the existing part of the design. When the score is
categorized as poor, it is recommended to modify or revise some parts of the application. Moreover, if the category was very poor, revision is highly required.
The qualitative data from the open-ended questions included in the questionnaires were selected as necessary. The information obtained from the
answers of the open-ended questionnaire was used to support the result of the quantitative data from the questionnaire.
Meanwhile, the data obtained from the interviews were interpreted in the form of written paragraphs. The qualitative data which were gathered from the
interviews were selected accordingly. The result was analyzed using the six principles of m-learning proposed by Elias 2011. The qualitative description
served as the complementary data to support the quantitative data from the questionnaire results.
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