Question Type: grid_rating_question

When and Why to Use

Use this to capture numeric or labeled ratings across multiple items in a structured format. It's useful for:

Performance, satisfaction, or quality ratings
Comparing several attributes on a common scale
Collecting fine-grained opinion data

Supports sliders, buttons, and star ratings with configurable point labels.

Portrait Experience

Each row is presented individually with a rating interface (slider, star, or button)
User can scroll vertically through rows
Labels and anchors are shown depending on the labels config

Landscape Experience

Grid layout with rows as items and ratings as columns
Ratings are input inline per row
Star and slider styles are fully supported

Configuration Options

Option	Type	Required	Default	Description
`question`	`string`	yes	-	The prompt shown to the user
`rows`	`List[str	MediaItem]`	yes	-
`row_name`	`string`	yes	-	Used in reporting structure
`image`	`MediaItem`	no	-	Top-level image shown above the question
`number_of_points`	`int`	no	5	Number of rating points
`first_point`	`int`	no	1 or first label	First point on the scale
`labels`	`Dict[int, str]`	no	-	Custom labels for scale points
`style`	`string`	no	`slider`	One of "slider", "button", or "star"
`randomize`	`bool`	no	`False`	Whether to randomize row order
`recodes`	`Dict[str, str]`	no	-	Optional recoding of rating values
`dont_know_option`	`str`	no	""	Optional "Don't know" fallback
`skip_empty`	`bool`	no	`False`	Skip question if no rows provided
`default`	`Dict[str, int]`	no	random	Test data defaults per row
`custom_validator`	`Callable[[Dict[str, int]], str	None]`	no	-
`image_label_field`	`str`	no	-	Field used for row image labels
`show_image_label`	`bool`	no	`True`	Show/hide row image labels
`image_size`	`Tuple[int, int]`	no	-	Size to render row images
`**topics`	`dict`	no	-	For reporting and text substitution

Example Code

Basic usage:

s.grid_rating_question( "Please rate the following aspects of the product", rows=["Quality", "Price", "Service", "Delivery"], row_name="Product Aspect", number_of_points=5 )

With custom labels and recodes:

s.grid_rating_question( "How would you rate these aspects?", rows=["Speed", "Design"], row_name="Attribute", number_of_points=5, labels={ 1: "Very Poor", 3: "Neutral", 5: "Excellent" }, recodes={ "1": "Low", "2": "Low", "3": "Mid", "4": "High", "5": "High" } )

With star style and default values:

s.grid_rating_question( "Please rate your satisfaction with each brand", rows=["Brand A", "Brand B"], row_name="Brand", style="star", number_of_points=5, default={"Brand A": 4, "Brand B": 5} )

Notes

style="slider" shows an interactive scale; use button for tap options or star for visual flair
labels can help orient respondents, especially on non-obvious scales
custom_validator can enforce variation or prevent straight-lining

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