Processing of Data

Meaning

• Data processing refers to the systematic organization, transformation, and preparation of raw data into a meaningful and analyzable form.
• It is the intermediate stage between data collection and data analysis.
• Objective: To ensure accuracy, reliability, and readiness of data for statistical analysis.

Steps in Data Processing

(i) Editing

• Careful checking of raw data to detect and correct errors, inconsistencies, or omissions.
• Ensures accuracy, completeness, and uniformity.
• Types:
  • Field editing: Done by enumerators immediately after data collection.
  • Central editing: Done by supervisors/researchers at central office.

(ii) Coding

• Assigning symbols or numbers (codes) to responses so that they can be classified into categories.
• Example: Gender → Male = 1, Female = 2.
• Types:
  • Pre-coding: Codes are decided in advance (used in structured questionnaires).
  • Post-coding: Codes developed after data collection (used in open-ended responses).

(iii) Classification

• Grouping data into categories based on common characteristics.
• Types:
  • Chronological classification → based on time (e.g., adoption over years).
  • Geographical classification → based on place (e.g., states, districts).
  • Qualitative classification → based on attributes (e.g., literacy, caste, training).
  • Quantitative classification → based on numerical values (e.g., age, income).

(iv) Tabulation

• Arranging classified data into rows and columns (tables) for easy analysis.
• Types:
  • Simple tabulation: One characteristic (e.g., age distribution of farmers).
  • Complex tabulation: Multiple characteristics at a time (e.g., age × education × adoption).
• Provides summary and comparison at a glance.

Importance of Data Processing

• Converts raw data → meaningful form.
• Ensures accuracy, reliability, and consistency.
• Reduces errors and biases.
• Facilitates statistical analysis.
• Provides basis for interpretation, conclusions, and report writing.