cat2cat

Author

Maciej Nasinski

cat2cat is a data harmonization procedure designed to unify inconsistently coded categorical variables in longitudinal or panel datasets. When categorical coding schemes evolve over time—due to new regulations, updated classifications, or shifting study requirements—these changes can hamper meaningful data analysis. cat2cat addresses this challenge by using a mapping (transition) table between two distinct time points.

If a single category could match multiple new categories, cat2cat replicates the observations and uses a variety of methods—ranging from simple frequency counts to advanced machine learning—to approximate the probability of assignment to each candidate category. This flexible, probability-driven approach ensures that final, harmonized data retain critical information without discarding ambiguous observations.

The algorithm and concepts behind cat2cat originate from two key publications:
- (Nasinski, Majchrowska, and Broniatowska, 2020) where the core procedure and replication rules were introduced.
- (Nasinski, Gajowniczek, 2023) which provides in-depth explanations of applying cat2cat in real-world longitudinal datasets.

Key Features:
1. Mapping Table: Central to cat2cat is a transition table that links categories from an older time point to those in a newer time point (or vice versa), allowing both forward and backward mappings.
2. Replication of Ambiguous Observations: When more than one category is possible, cat2cat creates multiple copies of the observation, each assigned to a different candidate category.
3. Probability Estimation: Users can choose simpler frequency-based methods or integrate more sophisticated machine learning techniques (e.g., k-Nearest Neighbors, Random Forest) for assigning probabilities to each replicated observation.
4. Multiple Language Implementations:
- R: Available on CRAN, offering easy integration with R’s statistical and data manipulation capabilities.
- Python: Distributed through PyPI, aligning with Python’s extensive data science libraries.
5. Rich Documentation & Examples: Both R and Python packages include comprehensive vignettes/notebooks, guiding users through set-up, data preparation, and best practices for effectively harmonizing their datasets.

Use Cases:
- Surveys Conducted Over Multiple Years: Where occupational or demographic codes may change between survey waves.
- Administrative Datasets: Which may update classifications or coding schemas over time, causing inconsistencies.
- Research Studies: Involving evolving or newly introduced categorization structures requiring backward or forward compatibility.

Benefits:
- Retain Maximum Information: By replicating rather than discarding ambiguous observations, users preserve the dataset’s statistical richness.
- Flexibility in Approach: Ranging from simpler frequency-based methods to advanced ML.
- Peer-Reviewed & Validated: The methodology is backed by published research

For more details, tutorials, and examples, visit the official repositories:

By providing a clear transition plan for categorical codes and robust probability-based assignment, cat2cat proves invaluable for researchers, data scientists, and analysts managing longitudinal datasets with inconsistently coded categorical variables.