Data Encoding Methodology

A guide to the strategic approach for transforming raw survey data into a numerically encoded format suitable for rigorous statistical analysis.

The Three-Phase Approach

Encoding data is not merely a technical step; it is a critical part of the research process that directly impacts the validity of your findings. This tool follows a systematic, three-phase methodology to ensure data is transformed in a transparent, statistically sound, and meaningful way.

This is the most crucial phase. Rushing this step is the most common source of error in data analysis. The goal is to understand the meaning and type of every single variable before any transformation is applied.

1. Data Dictionary Deep Dive (The "What")

We must first classify each variable from your dataset based on its nature:

  • Nominal Categorical: Data with no inherent order (e.g., Department: 'Sales', 'HR', 'Engineering').
  • Ordinal Categorical: Data with a clear, meaningful order (e.g., Experience: '0-3 years', '3-5 years', '5-10 years').
  • Likert Scale: A special type of ordinal data, typically used to measure attitudes or opinions. We must confirm its properties, such as the number of points (5-point, 7-point) and its directionality.
  • Binary: A special case with only two values (e.g., 'Yes'/'No').
2. Define the Encoding Strategy (The "How")

Based on the variable type, we select the optimal encoding method:

For Nominal Data:
One-Hot Encoding is the gold standard. It creates new binary (0/1) columns for each category, preventing the false assumption of an order.
For Ordinal Data:
Integer (Label) Encoding is used. It maps categories to numbers that respect their rank (e.g., `'Low' -> 1`, `'Medium' -> 2`, `'High' -> 3`).
For Likert Scale Data:

We treat this as Ordinal/Interval Data, mapping responses to a numerical scale (e.g., `'Strongly Disagree' -> 1` to `'Strongly Agree' -> 5`).

A critical sub-step is handling Reverse-Coded Questions. For negatively phrased items (e.g., "The system is confusing"), the scale must be inverted to ensure all scores point in the same direction of sentiment. Failure to do so invalidates the analysis.

Once the strategy is defined, we execute the transformation and rigorously validate the output.

  • 1. Configurable Implementation: The encoding logic is implemented to be driven by a configuration file (e.g., `config.toml`). This separates the code from the project-specific encoding rules, enhancing reusability and transparency.
  • 2. Automatic Codebook Generation: After encoding, the system should automatically generate a human-readable "codebook". This file serves as a permanent record, showing exactly how each original value was mapped to its new numerical representation. This is essential for reproducibility and auditing.
  • 3. Sanity Checks: We must verify the transformation. This includes comparing descriptive statistics (like counts) between the original and encoded data and using cross-tabulations to visually confirm the mapping is correct.

The final phase integrates the encoding process into an automated, user-friendly workflow within this application.

The goal is to provide an interface where the user can:

  • Select a source CSV file.
  • View the automatically identified columns and their types.
  • Configure the encoding rules (e.g., specify which columns are nominal, ordinal, and which Likert items need to be reverse-coded).
  • Execute the encoding process with a single click.
  • Receive the encoded data file and its corresponding codebook as outputs, ready for the next stage of analysis (e.g., bootstrapping, modeling).
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