Penalty Plus: Attribute-related survey data analysis

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InsightsNow PenaltyPlus Analysis

Penalty Plus: Attribute-related survey data analysis

Using Penalty Analysis Effectively

When we want to gain a deep understanding of what product attributes resonate deeply with consumers, it is useful to consider penalty analysis. Our InsightsNow Penalty Plus approach gives insights into attributes that affect purchase interest, liking or other identified product-related measures. Product developers can then use these insights to hone, innovate or optimize products for the maximum impact in the marketplace.

Product attributes used in penalty analysis are measured with “Just-About-Right” (JAR) scales. These are categorical scales where some points represent “too little” of a particular attribute, some points represent “too much,” and one point represents “Just-About-Right.” Penalty analysis measures the change in product liking due to that product having “too much” or “too little” of the attribute of interest. When it is implemented correctly, a penalty analysis research approach is a functional method that all researchers can use. 

Our sophisticated, model-based approaches help you perform significance testing, and set up parameters to identify product testing study participants who fall outside of the “Just-About-Right” range.

Penalty Plus eBook

In our eBook, we explore two kinds of penalty testing: Grand Mean or JAR Mean Penalties, and discuss the pros and cons of each kind of testing. We also look at significance testing, and examine several methods that may be used to apply statistical testing to penalties. And it’s important to keep in mind that all these tests must have real-world significance—what are the safeguards you can employ to make sure your data is the best it can be? Read on to learn more about best practices and recommendations regarding penalty analysis, so you can understand how to apply it to your next project.