Displaying the top-2 and bottom-2 frequency responses while filtering out the neutral responses to bipolar Likert 5-point questions can provide more insightful interpretations for several reasons:
- Clarity of Opinion: By focusing on the most extreme responses to a bipolar scale, the charts capture the strongest opinions, which are often the most informative. These responses highlight the most passionate and definitive views of the respondents, providing a clearer picture of the overall sentiment.
- Reduction of Ambiguity: Neutral responses can sometimes muddy the waters, as they may represent indecision, lack of knowledge, or true neutrality. By filtering these out, you reduce ambiguity and focus on the more decisive opinions, making it easier to interpret the data.
- Identification of Polarization: Highlighting the top-2 and bottom-2 responses helps identify polarization within the data. This can be crucial for understanding the extent to which opinions are divided, which is often more actionable than an average sentiment rating.
- Enhanced Actionability: Extreme responses often indicate areas that require immediate attention or intervention. By focusing on these, you can prioritize actions based on the most critical feedback, whether it's addressing major pain points or leveraging strong positive feedback.
- Improved Communication: When presenting findings to stakeholders, displaying the most extreme responses can make the data more compelling and easier to understand. It provides a clear narrative of what is working well and what needs improvement, without the noise of neutral responses.
There is no, one best chart for every situation
Data visualization is all about effectively communicating information, and the best way to do that can vary greatly depending on the context. Different types of data, different audiences, and different goals all require different approaches. For example, a bar chart might be perfect for comparing quantities across categories, while a line chart is better suited for showing trends over time.
The diversity of data types, the specific goals of the visualization, and the audience's preferences and familiarity all contribute to the fact that there is no one-size-fits-all solution in data visualization. Each situation demands a tailored approach to ensure the information is communicated as clearly and effectively as possible.
This is a wonderful metaphor in describing how much work it can take to present just one, really good finding
“You have to shuck a lot of oysters to find a single pearl. Don’t show all the shells you shucked; just show the pearl.”
We strive to build visualizations that clearly and concisely impart the data set's biggest finding.