Data Analysis: Editing, Coding, Tabular Representation of Data
Data analysis is a multi-step process of inspecting, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. This process is integral in various domains such as business, science, and social sciences to ensure effective operations and informed decisions.
Editing
Editing involves checking and adjusting responses in completed questionnaires to correct omissions, improve legibility, and ensure consistency. The goal is to ready the data for coding and storage.
Purpose of Editing:
- Consistency: Ensure responses are consistent both within and among different questionnaires.
- Completeness: Fill in missing responses to reduce item non-response.
- Order: Make use of questions answered out of order.
- Facilitation: Simplify the coding process.
Basic Principles of Editing:
- Number of Schedules/Questionnaires: Verify the total number of completed questionnaires.
- Completeness: Ensure all questions are fully answered.
- Legibility: Check handwriting for clarity.
- Inconsistencies: Identify and correct inconsistent answers.
- Uniformity: Maintain a consistent format.
- Irrelevant Responses: Remove answers that do not pertain to the questions.
Types of Editing:
- Field Editing: Preliminary editing done by a field supervisor immediately after data collection to catch technical errors and clarify responses.
- Office Editing: More thorough editing performed by central office staff to ensure data quality.
Coding
Coding assigns numerical scores or symbols to responses, facilitating computer processing. This step is crucial for organizing data into a structured format.
Key Components:
- Code: A numerical score or symbol assigned to responses for classification.
- Data Matrix: An electronic rectangular arrangement of data into rows (cases) and columns (variables).
Structure of Data Matrix:
- Field: A collection of characters representing a single data type.
- Record: A collection of related fields for a single case or respondent.
- File: A collection of related records for a sample.
Tabular Representation of Data
Tabular Representation organizes data into rows and columns, making it easy to read, interpret, and analyze.
Components of Data Tables:
- Table Number: A specific identifier for the table.
- Title: Describes the data, study period, location, and classification nature.
- Headnotes: Additional information about the table, often including data units.
- Stubs: Titles of the rows.
- Caption: Titles of the columns.
- Body or Field: The main content of the table, with each cell containing data.
- Footnotes: Supplementary information to the table title, used sparingly.
- Source: The origin of the data, mentioned below the footnote if secondary data is used.
Construction of Data Tables:
- Title Alignment: Ensure the title aligns with the study’s objective.
- Comparison: Place comparable rows or columns next to each other.
- Stub Placement: If rows are lengthy, place stubs on the right side.
- Singular Headings: Use singular forms for headings (e.g., “good” instead of “goods”).
- Footnotes: Use only if necessary.
- Column Size: Maintain uniform and symmetrical column sizes.
- No Abbreviations: Avoid abbreviations in headings and sub-headings.
- Units Specification: Clearly specify units above columns.
Advantages of Tabular Representation:
- Ease of Representation: Efficiently confines large amounts of data.
- Ease of Analysis: Facilitates statistical analysis such as central tendency and dispersion.
- Comparison: Simplifies comparison by placing relevant data adjacent.
- Economical: Easy to construct and visually appealing, saving time and space.
Summary
Data analysis involves critical steps such as editing, coding, and tabular representation. Editing ensures data completeness and consistency, coding organizes data into a structured format for processing, and tabular representation presents data in an easily interpretable and analyzable format. These steps collectively enhance data accuracy, reliability, and usability in various applications.