Describe coding as used in data analysis

Coding involves assigning numbers or other symbols to answers so the responses can be grouped into a limited number of classes or categories. The classifying of data into limited categories sacrifices some data detail but is necessary for efficient analysis. Coding helps the researcher to reduce several thousand replies to a few categories containing the critical information needed for analysis. In coding, categories are the partitioning of a set and categorization is the process of using rules to partition a body of data.
Coding rules
The categories should be:
• Appropriate to the research problem and purpose: Categories must provide the best partitioning of data for testing hypotheses and showing relationships.
• Exhaustive
• Mutually exclusive
• Derived from one classification principle
Coding closed questions
The responses to closed questions include scaled items and others for which answers can be anticipated. When codes are established early in the research process, it is possible to pre-code the questionnaire. Pre-coding is particularly helpful for data entry because it makes the intermediate step of completing a coding sheet unnecessary. The data are accessible directly from the questionnaire. A respondent, interviewer, field supervisor or researcher is able to assign an appropriate numerical response on the instrument by checking, circling or printing it in the proper coding location.

Coding open-ended questions
Open-ended questions are always used where insufficient information or lack of a hypothesis prohibits preparing response categories in advance, need to measure sensitive or disapproved behaviour, discover salience or encouraging natural modes of expressions. Content analysis is always used to analyse open-ended questions. Converse and Presser (1986) define content analysis as a research technique for the objective, systematic and quantitative description of the manifest content of a communication.

Content analysis follows a systematic process i.e.
• Selection of a unitization scheme. The units may be syntactical, referential, prepositional or thematic
• Selection of a sampling plan
• Development of recording and coding instructions
• Data reduction
• Inferences about the context
• Statistical analysis

Content analysis guards against selective perception of the content, provides for the rigorous application of reliability and validity criteria and is amenable to computerization.
“Don’t know” replies
“Don’t know” replies are evaluated in light of the questions nature and the respondent. While many don’t know are legitimate, some result from questions that are ambiguous or from an interviewing situation that is not motivating. It is better to report don’t knows as a separate category unless there are compelling reasons to treat them otherwise

Data entry
Data entry converts information gathered by secondary or primary methods to a medium for viewing and manipulation. Data entry is accomplished by keyboard entry from pre-coded instruments, optical scanning, real time keyboarding, telephone pad data entry, bar codes, voice recognition, optical mark recognition (OMR) and data transfers from electronic notebooks and laptop computers. Database programs, spreadsheets and editors in statistical software programs e.g. SPSS and SAS offer flexibility for entering, manipulating and transferring data for analysis, warehousing and mining.

Data description
The objective of descriptive statistical analysis is to develop sufficient knowledge to describe a body of data. This is accomplished by understanding the data levels for the measurements we choose, their distributions and characteristics of location, spread and shape. The discovery of miscoded values, missing data and other problems in the data set is enhanced with descriptive statistics
There are three general areas that make up the field of statistics: descriptive statistics, relational statistics, and inferential statistics:

---

Titany answered the question on October 21, 2021 at 13:36