How to Write and When to Use JAR (Just About Right) Questions

JAR (Just About Right) questions are used to evaluate whether specific product attributes (e.g., sweetness, thickness, flavor intensity) are at the right level from a consumer perspective.

Unlike liking questions, which measure preference, JAR questions help you understand whether an attribute is too weak, too strong, or just right, and how that impacts overall product performance.

When written and used correctly, JAR questions can identify opportunities to optimize product formulation and improve overall liking. JAR questions are most powerful when they move beyond simple description and into diagnosis and action, helping R&D teams understand not just what is off, but what to do about it and why it matters.

At their core, JAR questions are a diagnostic tool: they pinpoint whether specific attributes are too low, too high, or well-optimized, and quantify how those deviations impact overall liking, enabling clear, prioritized product improvements.

What JAR Questions Measure

JAR questions measure the perceived intensity of specific attributes and whether they meet consumer expectations.

They help answer questions like:

Is the product too sweet or not sweet enough?
Is the texture too thick or too thin?
Is the flavor intensity too strong or too weak?

This makes JAR questions especially useful for product optimization and refinement.

When to Use JAR Questions

JAR questions are most useful when you want to understand whether specific product attributes are at the right level and how they can be improved.

Use JAR questions when:

You are optimizing a product formulation (e.g., adjusting sweetness, thickness, scent, or texture)
You want to understand why a product is over- or under-performing
You need to determine whether an attribute should be increased or decreased
You are comparing products and want to understand what is driving differences in performance
You want to identify which attributes are “off” and in what direction (too much vs. too little)

JAR questions are especially valuable when paired with liking data, as they help connect what is wrong with how much it matters.

How to Write Effective JAR Questions

Use Clear, Attribute Specific Language

Each JAR question should focus on one specific attribute. Avoid combining attributes (e.g., “flavor and sweetness”) in a single question.

Example: Thinking about the flavor of this product, would you say it was…

Use a Balanced 5-Point Scale

JAR questions use a 5-point scale with clearly defined anchors on both ends and a neutral midpoint (“just about right”), which represents the ideal level of the attribute. Ensure the scale endpoints represent true opposites of the same attribute (e.g., too weak vs. too strong, too small vs. too large). This helps respondents clearly understand the direction of the attribute and provide more accurate feedback.

Standard JAR Scale Structure:

Much too [low attribute]
Slightly too [low attribute]
Just about right
Slightly too [high attribute]
Much too [high attribute]

You can also tailor wording depending on the attribute. Make sure your scale wording reflects how consumers naturally think about the attribute.

Sweetness: Not sweet enough → Too sweet
Saltiness: Not salty enough → Too salty
Thickness: Too thin → Too thick
Carbonation: Not enough carbonation → Too much carbonation
Size: Too small → Too large

Example JAR Question:

Thinking about the strawberry flavor of this product, would you say it was…?

Much too weak
Slightly too weak
Just about right
Slightly too strong
Much too strong

Key best practices:

Focus on one attribute at a time
Use natural language that matches how consumers think about the attribute
Keep the midpoint neutral: Just about right
Ensure the scale is balanced on both sides

How to Use JAR Questions in Your Study

Pair JAR Questions with Liking Questions

JAR questions should always be used alongside overall liking or attribute liking questions.

JAR tells you what is off. Liking tells you how much it matters. Together, they help you understand what is driving product performance.

You do not need to include a liking question for every individual JAR attribute. As long as you have an overall liking question, you can still assess how different attributes are impacting the overall product performance. However, including attribute level liking (e.g., aroma liking, flavor liking, texture liking) can provide additional context when deeper diagnostics are needed.

Example: Overall Liking + JAR

Overall Texture Liking Question:

How much do you like or dislike the texture of this product?

Dislike extremely
Dislike very much
Dislike moderately
Dislike slightly
Neither like nor dislike
Like slightly
Like moderately
Like very much
Like extremely

Texture JAR Question:

Thinking about the texture of this product, would you say it is…?

Much too thin
Slightly too thin
Just about right
Slightly too thick
Much too thick

Why this pairing matters:

If the texture is too thin and liking is low → increase thickness
If the texture is too thick and liking is low → reduce thickness
If texture is just right and liking is high → keep texture as is

This combination makes results actionable, not just descriptive.

When Not to Use JAR Questions

JAR questions work best for attributes where there is a clear “optimal” level. However, not all attributes fit this framework. Some attributes are better measured using intensity scales rather than JAR questions.

Example of a Poor JAR Question: Bitterness

Bad JAR version: Thinking about the bitterness of this product, would you say it was…Much too weak → Much too strong

Why this is problematic:

Bitterness is not always expected to have a single “just right” level across consumers or product types.
In many categories, bitterness can be polarizing or situational (e.g., desirable in coffee, undesirable in other products).
Respondents may interpret “too bitter” differently depending on personal preference rather than product optimization.

Better approach: Use an intensity scale: How bitter is this product? Not at all bitter → Extremely bitter

Example of a Poor JAR Question: Grittiness

Bad JAR version: Thinking about the grittiness of this product, would you say it was…Not gritty enough → Too gritty

Why this is problematic:

Grittiness is typically a negative attribute, not something consumers want “more” of.
The idea of “not gritty enough” is not meaningful or realistic for most products.
This creates an unnatural scale that can confuse respondents and skew results.

Better approach: Use an intensity or attribute presence scale: How gritty is this product? Not at all gritty → Extremely gritty

Limit the Number of JAR Questions

Focus on attributes that are both important to the experience and actionable for product development.

Too many JAR questions can:

Fatigue participants
Reduce response quality
Add noise to your data
Make it harder to identify the attributes that truly matter

As a general guideline, prioritize 3–6 key attributes rather than trying to evaluate every possible dimension of the product.

When selecting attributes, consider:

What are the core drivers of liking for this product?
What attributes are most likely to vary or need optimization?
What feedback is actionable for product development?

Focusing on the meaningful attributes helps ensure clearer insights and more reliable results.

Common JAR Question Examples

Below are examples of commonly used JAR questions across different attributes. These can be modified based on your product.

Size, Appearance, & Amount

Thinking about the size of this product, would you say it was…?
• Much too small
• Slightly too small
• Just about right
• Slightly too large
• Much too large

Thinking about the color of this product, would you say it was…? Thinking about the size of this product, would you say it was…?
• Much too light
• Slightly too light
• Just about right
• Slightly too dark
• Much too dark

Thinking about the amount of product, would you say it was…?
• Much too little
• Slightly too little
• Just about right
• Slightly too much
• Much too much

Aroma

Thinking about the aroma of this product, would you say it was…?
• Much too weak
• Slightly too weak
• Just about right
• Slightly too strong
• Much too strong

Flavor

Thinking about the overall flavor of this product, would you say it was…?
• Much too weak
• Slightly too weak
• Just about right
• Slightly too strong
• Much too strong

Thinking about the [specific] flavor of this product, would you say it was…?
• Much too weak
• Slightly too weak
• Just about right
• Slightly too strong
• Much too strong

Taste Attributes

Thinking about the sweetness of this product, would you say it was…?
• Not nearly sweet enough
• Somewhat not sweet enough
• Just about right
• Somewhat too sweet
• Much too sweet

Thinking about the saltiness of this product, would you say it was…?
• Not nearly salty enough
• Somewhat not salty enough
• Just about right
• Somewhat too salty
• Much too salty

Thinking about the sourness of this product, would you say it was…?
• Not nearly sour enough
• Somewhat not sour enough
• Just about right
• Somewhat too sour
• Much too sour

Texture & Mouthfeel

Thinking about the overall texture of this product, would you say it was…?
• Much too soft
• Slightly too soft
• Just about right
• Slightly too hard
• Much too hard

Thinking about the thickness of this product, would you say it was…?
• Much too thin
• Slightly too thin
• Just about right
• Slightly too thick
• Much too thick

Thinking about the moistness of this product, would you say it was…?
• Much too dry
• Slightly too dry
• Just about right
• Slightly too moist
• Much too moist

Aftertaste

Thinking about the aftertaste of this product, would you say it was…?
• Much too weak
• Slightly too weak
• Just about right
• Slightly too strong
• Much too strong

Miscellaneous

Thinking about how quickly the product absorbs into your skin, would you say it is…?
• Much too slow
• Slightly too slow
• Just about right
• Slightly too fast
• Much too fast

Thinking about the amount of lather or foam, would you say it is…?
• Much too little
• Slightly too little
• Just about right
• Slightly too much
• Much too much

Thinking about how the weight of the product, would you say it is…?
• Much too light
• Slightly too light
• Just about right
• Slightly too heavy
• Much too heavy

Final Takeaway

JAR questions help you understand whether product attributes are at the right level, but their true value comes from how they are written and used.

When paired with liking data and analyzed through penalty analysis, JAR questions provide clear direction on how to optimize your product and improve overall performance.

To learn more about how to interpret JAR results and use penalty analysis, see our article on Penalty Analysis.