New Multi-Reference Significant Difference feature in Harmoni

Our new Multi-Reference Significant Difference (MRSD) feature clearly highlights patterns in survey data to easily view statistically significant differences against multiple references.

We have released a new Harmoni feature for calculating statistically significant differences. With the new Multi-Reference Significant Difference (MRSD) feature, calculations can be performed against single or multiple reference groups, allowing researchers to measure probability and confidence levels more accurately. MSRD clearly highlights patterns in the data to quickly visualize key differences among variables and deliver higher quality insights.

Market research analyses have uncertainties built-in, simply because respondent samples are an approximation of the populations they represent. The MRSD feature in Harmoni addresses these uncertainties head-on by letting researchers easily investigate patterns among groups and understand key variables in context.

Not only does this add to the interpretative power of Harmoni, but it is also so easy to use. Users can assign reference groups with just a few clicks, customize confidence levels, and perform complex analysis while filtering on any chosen data point.

  • Allows the calculation of statistical differences against one or more reference groups, indicating results with clear visual markers.
  • Highlights patterns of these significant differences within the data, showing the main differences and relationships between variables.
  • Facilitates the creation of robust insights based on similarities, differences and overall patterns within the data.

“Consumer insights professionals need technology solutions that directly address the unique characteristics and complexities of market research data,” said Geoff Lowe, Director at Infotools. “Harmoni is purpose-built with this in mind, understanding the challenges of things like representative samples, data weighting and statistical significance. Our new MRSD feature quickly draws the user’s attention to differences and similarities in the data for greater understanding of the story that data is telling.”


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