> ## Documentation Index
> Fetch the complete documentation index at: https://docs.redem.io/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. GQS supports grids with 5 or more items, while pattern checks are meaningful only from 7 items onward.
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:
