Handling Behavioral Scores with Mixed Scales: Best Practices for Encoding and Ordering Ranks

Problem Description:

I’m working on a data processing pipeline that involves handling behavioral survey data from multiple scales (e.g., Likert scales, frequency scores, and categorical data). My goal is to encode these mixed scales properly while maintaining the correct rank/order (for instance, ensuring that higher Likert scores indicate stronger agreement).

However, I’ve run into several issues with encoding and rank preservation.

Question:

What are some robust methods or best practices for:

• Encoding behavioral scales with mixed types (e.g., Likert, categorical, frequency scores) while maintaining the order and rank.
• Handling inconsistent answer sets across different surveys (e.g., 5-point vs. 7-point scales).
• Dynamically encoding ordinal and categorical variables in a way that respects the natural order.
• Dealing with missing values and inconsistent responses within the encoding process.

Tools I’m Using:

Python (Pandas, Scikit-learn)
Streamlit (for visualization and reporting)

Any suggestions for tools, workflows, or algorithms to dynamically and effectively encode behavioral data will be greatly appreciated. I’d also love to know if anyone has encountered similar challenges and found solutions that work across varied datasets. I’m relatively new to this data pipeline stuff. Thank you in advance!

Below are the approaches I’ve tried so far, but none have provided a robust, generalizable solution:

Hard-coding mappings for categories and ordinal features, like:

{‘Never’: 0, ‘Rarely’: 1, ‘Sometimes’: 2, ‘Often’: 3, ‘Always’: 4}
This became unmanageable across multiple datasets with slightly different answer sets (e.g., some surveys use 5-point scales, others use 7-point).

LightGBM encoding: I used LightGBM to encode categorical features dynamically. While it works well for feature importance, it didn’t seem to capture or maintain the ordinal nature of all scales.

Clustering methods to find patterns within responses – but this approach failed to respect the natural ordering of some ordinal scales.

One-hot encoding: This lost the rank structure entirely, making it unsuitable for certain analyses.

Ordinal encoding: I also tried OrdinalEncoder from sklearn, but it didn’t encode the columns properly (in some cases, the results didn’t align with the expected order or meaning).

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