Easily compare the results of different sub groups in this report template using the “Top 2 Box” metric.
Segments with significantly higher/lower values are highlighted.
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In this report template you select up to 15 sub group comparisons. The result from your survey are visualised as shown below.
Segment comparisons is also made for non-scale questions.
The comments on your survey’s open-end questions (Verbatim) are also included in the PowerPoint report. We have two different ways of presenting the comments depending on the amount of data.
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The “Top 2 Box” metric in market research refers to a method of analysis where survey responses are aggregated based on the two highest positive response categories for a particular question. Typically, these responses are represented by the top two boxes in a rating scale. The goal is to simplify data interpretation by focusing on the most favorable or positive sentiments expressed by respondents.
For example, if a survey question has a 5-point scale ranging from “Strongly Disagree” to “Strongly Agree,” the “Top 2 Box” would include the percentages or counts of respondents who selected “Agree” and “Strongly Agree.” This metric provides a concise summary of the proportion of respondents with positive sentiments, making it easier to identify trends or insights at a glance.
Using the “Top 2 Box” metric is common in market research and survey analysis, particularly when dealing with Likert-type scales, to highlight the prevalence of positive attitudes or behaviours among respondents.
Significance tests help to determine whether the differences between sub groups are statistically significant or if they could have occurred by chance.
Testing the significance of a sample proportion is used to answer whether the breakdown sample proportion is significantly greater or smaller than the total population proportion (one-tail test).
Colouring the values of groups significantly higher or lower than the total values makes it easier to distinguish them visually.
Our significance tests use Significance Level 0.05 (5%) as the standard, the most commonly used level in statistical testing. The significance test level refers to the predetermined thresholds used to determine whether the statistical test results are considered statistically significant. A significance level of 0.05 indicates a 5% chance (1 in 20) of rejecting the null hypothesis when it is true (Type I error).
Other common Significance Levels are 0.01 (1%) (lower probability of making Type I errors) and 0.1 (10%) (lower probability of making Type I errors).