> ## Documentation Index
> Fetch the complete documentation index at: https://docs.repdata.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Grid-Question Score
> Learn how the Grid-Question Score identifies low-quality response patterns in grid questions.
## What is the Grid-Question Score?
The GQS helps assess data quality in grid questions (also known as matrix questions). It uses machine learning and a defined set of pattern-detection rules to evaluate how respondents interact with grid items.
## High-level checks performed
* Whether the sequence of responses across the grid items shows minimal variance, e.g., always choosing the same option across many rows (straight-lining).
* Whether the ordering of responses is highly repetitive or patterned (e.g., always lowest → highest → lowest → highest) when that would not make sense for the item content.
* Whether at least a minimum number of items are present in the grid (public recommendation: 7 items or more) so that pattern checks are meaningful.
To detect response patterns beyond straightlining, grid question data must be submitted to the API in the original display order shown to the respondent.
## What Patterns Does the GQS Detect?
Here are some examples of behavioral patterns in grid responses that are analyzed by ReDem:
