Overview

The "Evaluate my taxonomy" tool is designed to help you assess and enhance your taxonomy or code frames using an advanced Language Learning Model (LLM). This tool is particularly beneficial for market researchers and data analysts who need to ensure their coding frameworks are optimized for accuracy and efficiency. By leveraging the power of LLM, you can refine your taxonomies to better categorize and interpret data, making your research more reliable and insightful.

Who this tool is for

Market Researchers: As a market researcher, you often deal with large volumes of qualitative data that need to be categorized accurately. This tool allows you to evaluate your existing taxonomy lists, ensuring that each code is distinct and relevant. You can also check specific responses against your codes to see if they fit well, making your data analysis more precise and actionable.

Data Analysts: If you are a data analyst, you know the importance of having a well-structured coding framework. This tool helps you refine your code frames by suggesting improvements and ensuring that your codes are not too vague or overly detailed. This ensures that your data categorization is both efficient and effective, leading to more accurate insights.

Survey Designers: As a survey designer, you need to ensure that the open-ended responses you collect can be easily categorized and analyzed. This tool helps you evaluate your coding criteria and taxonomy, making sure that your codes are concise and non-redundant. This streamlines the process of analyzing survey responses, saving you time and effort.

How the tool works

The "Evaluate my taxonomy" tool operates through a series of steps designed to assess and improve your taxonomy or code frames. Here’s a detailed step-by-step guide on how it works:

1. Input Selection: You start by selecting the type of evaluation you need. You can choose between "Checking my taxonomy list" or "Checking a response and a code." This selection determines the subsequent steps and inputs required.

2. Provide Taxonomy or Code: Depending on your selection, you will either enter your full taxonomy list (one code per line) or a specific code to be assessed for a particular response. This input is crucial as it forms the basis of the evaluation.

3. Input Text (if applicable): If you selected "Checking a response and a code," you will need to provide the text to be coded, such as an open-ended response or a review. This helps the tool understand the context in which the code is being used.

4. Set Criteria: You can specify the coding criteria in bullet points, such as the maximum number of words per code, the need for unique identifiers, and avoiding duplicate contents among codes. These criteria guide the LLM in evaluating and suggesting improvements.

5. LLM Evaluation: The tool uses an advanced LLM to analyze your input based on the provided criteria and constraints. For taxonomy lists, it checks for near-synonyms, context separation, and rewording suggestions. For specific codes, it assesses whether the code matches the response content and suggests improvements if necessary.

6. Output Generation: The tool generates a JSON output with the improved taxonomy or revised code along with a description of the changes. If the taxonomy or code is already well-defined, it notes that no updates are needed.

7. Review and Implement: You can review the suggested improvements and implement them in your coding framework. This ensures that your taxonomy or code frames are optimized for better data categorization and analysis.

Benefits

• Enhances the accuracy and efficiency of your coding frameworks.
• Ensures distinct and relevant codes, avoiding redundancy.
• Provides actionable insights for better data categorization.
• Saves time by automating the evaluation and improvement process.
• Improves the reliability of your research findings.

Additional use-cases

• Refining survey response categories to ensure clear and concise coding.
• Evaluating and improving customer feedback categorization for better sentiment analysis.
• Enhancing product review coding frameworks to identify key themes and trends.
• Optimizing social media comment categorization for more accurate sentiment tracking.
• Improving interview transcript coding for qualitative research analysis.