Problems in processing

Data Processing: Editing, Coding, Tabulating

Data Processing

Data processing involves transforming raw data into meaningful statements. This process includes data analysis, interpretation, and presentation.

Editing Data

Editing ensures the collected data is accurate, consistent, and complete.

• Example Problems: Missing answers, incorrectly marked responses, implausible answers.
• Solutions: Standardize responses (e.g., converting income to a consistent time frame), correct obvious errors (e.g., excessive chili use).

Coding Data

Coding translates responses into numerical values for analysis.

• Pre-coding: Assigning codes during questionnaire design.
• Post-coding: Categorizing and coding open-ended responses after data collection.

Data Classification/Distribution

Classifying data into meaningful categories helps in analysis.

• Types:
  • Frequency Distribution: Shows the number of occurrences.
    • Ungrouped: Individual scores (e.g., specific ages).
    • Grouped: Collapsed scores (e.g., age ranges).
  • Percentage Distribution: Represents frequencies as percentages.
  • Cumulative Distribution: Shows frequencies up to a certain point.
  • Statistical Distributions: Uses measures like mean, median, mode.

Tabulation of Data

Tabulation organizes data into tables for analysis.

• Manual vs. Computerized: Manual tabulation for small datasets; computerized for larger, complex datasets.
• Benefits: Simplifies findings, identifies trends, and shows relationships.

Problems in Data Processing

“Don’t Know” (DK) Responses

DK responses can indicate either genuine uncertainty or flaws in the question.

• Solutions: Improve question design, interviewer rapport, and categorize DK responses appropriately during analysis.

Use of Percentages

Percentages simplify data but can be misleading if not used correctly.

• Rules: Average percentages correctly, avoid large percentages, ensure the base is understood, calculate percentage decreases correctly, and use causal factors in tables.

Data Processing Activities

Input

Converting collected data into a computer-readable format.

• Collection: Gathering raw data.
• Encoding: Converting data for computer processing.
• Transmission: Sending data to processors.
• Communication: Sharing data between systems.

Process

Transforming raw data into information through classification, storage, and calculation.

Output

Presenting processed data for decision-making.

Challenges in Data Processing

Collection of Data

Accurate data collection is critical for reliable results.

• Techniques: Observation, questionnaires, interviews, focus groups.

Duplicacy of Data

Duplicate data entries can lead to inaccuracies.

• Solution: Data deduplication to remove redundant data.

Inconsistency of Data

Incomplete or conflicting data can hinder analysis.

• Solution: Validate data for completeness and consistency.

Variety of Data

Handling different data formats (text, images, videos) can be challenging.

• Solutions: Indexing, data profiling, metadata management, format conversion (e.g., XML).

Data Integration

Combining data from diverse sources into a unified view.

• Techniques: Consolidation, federation, propagation.

Volume and Storage of Data

Managing large volumes of data efficiently.

• Solutions: Object storage, scale-out NAS, distributed nodes.

Poor Description and Metadata

Lack of proper documentation complicates data extraction.

• Solutions: Use de-normalization, stored procedures, and NoSQL databases.

Modification of Network Data

Changing data structure in complex networks is difficult.

• Solution: Use schema comparison utilities.

Security

Protecting data from breaches is crucial.

• Solutions: Encryption, limited access, secure storage practices.

Cost

Managing the cost of data processing.

• Solutions: Plan expenses, use data compression, optimize resources.

Summary

Data processing transforms raw data into meaningful information through structured activities. Challenges include handling DK responses, ensuring data accuracy, managing different data formats, integrating data, ensuring security, and controlling costs. Effective techniques and solutions are essential for reliable and efficient data processing.

By understanding and addressing these detailed aspects, one can enhance the data processing workflow, ensuring accurate and meaningful insights from the data collected.