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What is the Open-Ended Score?

ReDem enables precise analysis of open-ended responses by classifying them into eight distinct categories, each with a corresponding score. This process is powered by a GPT based AI model, delivering reliable and comprehensive results you can trust.

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How is the Open-Ended Score calculated?

Each response is first classified into one of our quality categories. Each category is then assigned a score from 0 to 100, reflecting the response’s quality. These question scores are aggregated to calculate an overall Open-Ended Score (OES) for each respondent.

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The quality categories are:

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Wrong Topic

Identifies responses that deviate from the question or topic by evaluating their context against relevant keywords and the question itself. The context check is always enabled. Keywords are only necessary when the question lacks sufficient context. When adding them, ensure they broadly reflect the intended context to avoid false positives caused by overly narrow interpretations. Both the question and keywords must be in a supported language. Responses failing to align with the expected context are classified as “Wrong Topic” and assigned an OES (Overall Evaluation Score) of 30.

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AI Generated Answer

The model analyzes open-ended responses to assess whether they were artificially generated, evaluating patterns across a wide range of variables including grammar, structure, phrasing, syntax, word choice, sentence length, complexity, and predictability. Responses identified as AI-generated are assigned a score of 0.

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Nonsense

Enabling nonsense detection identifies gibberish, random numbers, and meaningless statements. Such responses are classified as “Nonsense” and assigned a score of 10.

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Wrong Language

Responses provided in an unexpected language are flagged as “Wrong Language” and assigned a score of 20. You can specify the expected language(s); if none are defined, the language check remains inactive. Open-Ended Score supports over 100 languages, including English, German, French, Spanish, Chinese, Japanese, Swedish, and more. The check is performed per respondent, and it is recommended to use the questionnaire language as the reference.
Questions lacking linguistic content (e.g., brand awareness questions) are not suitable for the language check.

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Duplicate Respondent

The optional duplicate check detects potentially fraudulent responses, identifying both exact duplicates and partial matches.
  • This check detects repeated responses to the same question. Short or common responses are not penalized, but substantial duplications may lower the score. Scoring is continuous, so minor overlap results in moderate impact, while extensive duplication leads to stronger penalties.
  • Duplicate Respondents Across Multiple Questions: When repetition occurs across several questions, the penalty increases. In severe cases, such as near-complete repetition across multiple responses, a score of 0 may be assigned.
  • Full Response Set Matches: If a respondent’s full set of answers matches another’s, all responses are marked as duplicates and scored 0.
  • Whitelists exclude standard or commonly accepted responses from being flagged.

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Duplicate Answer

We check whether a respondent provides the same or similar answers across multiple questions. If duplication is limited to one question, a score of 50 is assigned; if duplicates are found in more than one question, the score is 0.
If a response qualifies as both “Duplicate Respondent” and “Duplicate Answer,” the “Duplicate Respondent” category takes precedence.

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Bad Language

Responses containing swear words or offensive language are classified as “Bad Language” and are assigned a score of 10.

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Generic Answers

Responses containing vague or generic phrases like “good,” “ok,” “anything,” or “yes” are classified as Generic Answers and assigned a score of 50.

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No Information

Responses that lack meaningful content—such as “no idea,” “nothing,” “no comment,” or “I don’t know”—are labeled as No Information and given a score of 60.

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Valid Answer

Valid responses are those that do not fall into any other quality category. Each response is also evaluated for its level of detail. “Valid Answers” are assigned a score between 70 and 100, based on their detail quality.

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Use of GPT for Open-Ended Score

ReDem uses the most advanced GPT large language models (LLMs) from OpenAI, to analyze and categorize open-ended responses. This enables precise and reliable scoring by leveraging cutting-edge language understanding. To ensure data privacy and compliance, GPT is integrated into the ReDem OES with strict safeguards:

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Individual Responses & Anonymity

Each response is sent to OpenAI individually, using a fully anonymized ID. Only the single response is transmitted per API request—complete survey datasets are never shared.

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Exclusive Use by ReDem

Only ReDem communicates with OpenAI. The platform does not share any details about the origin or source of the responses.

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Data Storage & Retention

OpenAI retains data for up to 30 days, after which it is permanently deleted. The data is never used to train AI models.

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GDPR-Compliant Data Transfers

ReDem and OpenAI operate under a Data Processing Agreement based on EU Standard Contractual Clauses (SCCs). This ensures all data transfers—including those involving personal data—fully comply with GDPR This setup gives you both the advanced capabilities of GPT and the data protection required for responsible AI use in survey research.