3 Stages of Good and Correct Data Processing Techniques
In research, there are several stages that must be passed before presenting information from your research results. First, you have to collect data first, then process it and then analyze it so that the results are more detailed.
Before processing the data, make sure you have finished carrying out the first task, namely collecting data. If you have collected data, let’s simplify it first by doing data processing techniques. At this stage, there are several advantages that can be taken:
- The data that you have collected can be simplified again by processing it
- Organize data in a neater and easier to understand arrangement
- Facilitate the data analysis process later
Data processing is very important in research, so that it is not complicated when analyzed later, so we need to make the arrangement of the data that has been collected more tidy by processing the data.
There are 2 types of data in the research, namely qualitative and quantitative data.
Qualitative: data covered is not numeric data (numbers). What is covered has to do with facts, or words.
Quantitative: the data covered almost entirely is numeric (numbers). What is inputted is more on statistics.
The steps that must be passed in data processing are basically the same.
Data Processing Techniques
The following are 3 steps that you must go through in processing data.
3 Steps of Data Processing Techniques in Qualitative and Quantitative Research
Editing
In this step, you have to re-check all the data you got from the ‘data collection’ stage.
Some of the things you should do here are:
Checking the entire list of questions answered by respondents
Adjusting the respondent’s answer whether it is in accordance with the question asked
Are the questions answered completely?
Is the respondent’s answer the same or does it change?
Coding (Coding)
The purpose of coding is to simplify the answers of the respondents. You can do coding by adding symbols such as numbers so that they can be further categorized.
Example:
You coded 1, for the answer ‘agree’. Code 2, for ‘disagree’ answers. Or add code 0, for a blank answer (respondent did not answer the question).
Tabulating (Tabulating)
Tabulation is the activity of calculating and compiling the coding results which will later be simplified again in tabular form. The table created can be in the form of a correlation table, a frequency table, or a cross table.
There are 2 types of tabulations:
- Manual Tabulation: all tabulation activities are done by hand. From counting to serving, everything is done by hand.
- Mechanical Tabulation: this tabulation is assisted by a computer or certain equipment.
After going through these 3 steps, your data has been successfully processed. But your research is not finished yet, you still have to move on to the analysis stage.
Qualitative and Quantitative Data Analysis Techniques
Data analysis is the final stage of summarizing the results of the research you have done. The purpose of this data analysis is to simplify the arrangement of the data that you have processed.
Apparently, the data that has been processed can be further detailed and made as concise as possible. There are two types of ways of doing analysis. This is important you know because this method relates to the type of data you are doing (Qualitative or Quantitative).
- Nonstatistical Analysis
This type of analysis is very suitable if the data you are processing is qualitative data (data not numbers). This analysis is not carried out by calculating statistics and the way to do this analysis is by reading the data.
- Statistical Analysis
Suitable for processing quantitative data (data with numbers). This analysis is done by ‘counting’ the existing data. Unlike non-statistical analysis, it is enough to just read.
Statistical analysis is still further divided into two types:
- Descriptive Statistical Analysis: presenting facts and descriptions of the research without giving an assessment.
- Inferential Statistical Analysis: provides an assessment of the object being studied.
That is the technique of data processing as well as its analysis in qualitative and quantitative data research.
