How to analyze questionnaire data? It must pass through various stages, ranging from data entry into the computer processing through SPSS or Ms. Excel, testing the validity and reliability, descriptive analysis and hypothesis testing. Here is the stages:

1. Validity and Reliability

What distinguishing between questionnaire data processing method with secondary data are validity. When we conducted the study with a questionnaire, so we need to test the validity and reliability of the questionnaire. Why need to do? because the questionnaire was arranged by researcher, meanwhile answering the questionnaire is respondent. The purpose is to minimize interpretation gap between researcher and respondent.

Moreover, Good questionnaire should be well understand by respondents as good as the questionnaire maker. A Questionnaire should has high level of consistency over times.

Otherwise, in secondary data, we do not need to test the validity and reliability.

2. Entry Data

Furthermore, After the questionnaires collected, it needs to input the data into a computer. The most common software for data entry is excel. Surely, spread sheet Excel are familiar among us. How to arrange the data in spread sheet. Stacking down in the spread sheet is the respondents. Meanwhile, the column fill by the item number or questionnaire answer. Likewise, Input data into SPSS is similar with spreadsheet Excel. The data arranges on row as respondent and column as question.

For closed questions, we can give score for each answer option in your question. For example, the answer: strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1. Only the score input into spread sheet.

In certain conditions, negative questions are possible? In such conditions, reversely the score of 5 changes to 1, 4 to 2 and so on.

 3. Descriptive analysis

To present the questionnaire results, researcher need to process the data using descriptive analysis. What type of graph is suitable for secondary data? Frequency distribution format is a common to present in descriptive. The display is presented how the number of respondents who answered agree, how that answer did not agree and so on.
In descriptive statistics, common measurement need to provide such as: mean, median, mode and standard deviation. However, when we provide ordinal data as mean and standard deviation, in fact we’re treating these data into numeric data.

4. Hypothesis testing to analyze questionnaire data

Is a questionnaire research able to test a hypothesis? The answer is sure. Actually, Likert scale questionnaire data is ordinal data. It is most appropriate statistical technique is non-parametric techniques. However, due to limitations of statistical tools in non-parametric analysis, somehow data transformation is applied to transform ordinal data into a numerical scale. Even though, transformation method is not a must, as long as the data distribution is normal, then statistical parametric methods can apply.

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