5 Organizing and Preparing or Analyzing of Data in Research

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5 Organizing and Preparing or Analyzing of Data in Research

5 Organizing and Preparing or Analyzing of Data in Research

Data should be processed using specific techniques to draw the conclusion. The processing technique is used to make data valid, simple, and reliable. Data are, first of all, classified and grouped on the basis of its nature, quality and trends. To see a relationship between such data, data processing is essential. Data processing procedures are given below:

1. Editing

The process that detects errors in the raw data and rectifies the errors and simplifies the act of coding is known as editing. Especially, the data obtained from interviews, observations, and questionnaires should be edited. It ensures the quality of the data. Sometimes respondents want to hide the real information or complete the questionnaire without understanding them such data requires editing. For example, in a questionnaire, a respondent may respond as unmarried in one question, and in the next responses, he/she said having 2 children. Then it is understood that the respondent is married and needs to edit the data. Editing of collecting data is made in the field and in the office. They are known as field editing and central editing respectively.
Field editing means the editing made by a surveyor in the field itself on the basis of his/her experience and observation. Surveyor edits the data after the completion of collecting data from interviews and other methods. Surveyor uses codes and edits the data after the completion of the data collection process.
Data are edited before the analysis of such data in the office by the editor alone or jointly is known as central editing. It removes the wrong and incomplete data and makes data ready for analysis. 
The main objectives of editing are to ensure the following things:
  • Accuracy of data.
  • Consistent with the intent of the questions and other information in the survey. 
  • Data is uniformly entered.
  • It is complete.
  • Arrange to simplify coding and tabulation.


The editor should follow the following rules while editing data:

a. Be familiar with instructions given to the interviewer and interviewees.

b. Do not destroy and erase the original entry.

c. Make all edited entries on an instrument in some distinctive color and in a standard form.

d. Put initial to signalize in all changed or amended answers.

e. Place initial signature and date of editing on each instrument completed.

2. Coding

The act of assigning numbers or other symbols to the responses of respondents so that the responses can be grouped into a limited number of categories is known as coding. Single code should be provided to similar information or data.
Data collected from observation techniques will not be similar and such unsystematic and different types of information should be systematized. Researchers cannot develop his/her concepts until the data is systemized. Thus, coding is essential for the analysis of data. Nowadays, a computer is used for coding data. Instead of entering the word male or female in response to a question that asks for the identification of one’s gender, we would use numeric codes (For example, 0 for male and 1 for female). Such a process of providing 0 and 1 to males and females is known as coding.

Following rules are to be followed while coding:

a. Coding should avoid unclarity and duality so that codes can be used consistently.

b. All the codes used are to be defined.

c. Coding éystem should be developed while developing data collection design.

d. Codes are to be recorded in a codebook that provides meaning and information about the codes.

e. Codes should be appropriate to the research problem and purpose.

f. It should be exhaustive.

g. It should be mutually exclusive.

h. It should be derived from one of the classification principles.

3. Classification

Classification means separating items according to similar characteristics and grouping them into various classes. First of all, data should be collected as per the research objective. Such data are not ready for comparison and analysis until and unless they are systematically arranged and even they cannot be understood. Thus, dividing the data into different classes based on their characteristics is known as classification. The classification of data into limited categories specifies some data detail but is necessary for efficient analysis.
For example, the classification of employees with the purposes of providing training on the basis of age is given below:

Age Group Number
20 to 30 years 30
31 to 40 years 20
41 to 50 years 10
Total 60

Classification of data can be made on the following bases:

a. Geographical classification: 


When the data is classified on the basis of an area or places like a village, distinct, zone, development region, etc. is known as geographical classification. Employees of an organization can be classified as follows on the basis of places of their permanent address:

Place No. of Employees
Far western 20
Western 50
Eastern 30
Total 100

b. Chronological classification: 


Classification of data based on time frame is known as chronological classification. Generally, time is written in increasing order. For example, classification of data based on the position of employment can be presented as follows:

Year Employed Population
2067 150000
2068 153000
2069 160000
Total 463000

c. Qualitative classification: 


If the data are classified on the basis of their characteristics or qualities then such classification is known as qualitative classification. For example, the classification of employees of an organization on the basis of gender is given below:

Gender No. of Employees
Male 105
Female 33
Total 138

d. Quantitative classification: 


Classification of data on the basis of the class interval is known as quantitative classification of data, facts/ information like production, value, marks obtained, weight, etc. For example, the classification of employees on the basis of their salary is given below:

Salary (Rs.) No. of Employees
5,000 – 10,000 50
10,001 – 15,000 20
15,001 – 20,000 13
Total 83


5. Summarizing of Data.

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