Frequently Asked Questions (FAQs)

MaxDiff (Maximum Difference Scaling) is a powerful survey technique widely used to measure consumer preferences and priorities. It presents participants with a series of choices and asks them to identify the most preferred and least preferred options. It is suitable for scenarios such as prioritizing product features, evaluating the importance of service items, analyzing preferences for brand attributes, and market segmentation research. It is especially well suited to studies that need to understand the real hierarchy of user needs.

Compared with traditional rating questions, MaxDiff analysis has the following advantages: 1) It avoids rating bias, as participants must make a choice instead of giving all options high scores; 2) It provides a ranking of relative importance, which is more in line with real-world decision-making scenarios; 3) It reduces cognitive burden, since only a small number of options need to be compared each time; 4) It produces more reliable preference data, making it suitable for statistical analysis; 5) It can identify the features that are truly important and avoid the "average score trap".

Key parameter recommendations: 1) Number of attributes per task: 3-5 is recommended. Too few makes comparison insufficient, while too many can confuse participants; 2) Number of tasks: The system calculates this automatically based on a formula, and the recommended range is 6-10 tasks; 3) Total number of attributes: 8-20 is recommended. Too few lacks comparative significance, while too many increases participant fatigue; 4) Label settings: You can customize the descriptions for "most important" and "least important" to better fit the research topic.

MaxDiff analysis is recommended in the following situations: 1) when you need to understand the relative importance ranking of multiple features or options; 2) when the research objective is to identify the most critical influencing factors; 3) when respondents are likely to show bias on rating questions (for example, giving everything high scores); 4) when you need preference data that can be statistically analyzed; 5) when the research budget is limited but high-quality data is required; 6) for scenarios such as B2B and B2C market research, product development, and service optimization.

Key metric interpretation: 1) Preference percentage: the most intuitive metric, showing the proportion of times an attribute is selected as the "best"; 2) Score: a comprehensive metric, calculated as (number of best choices - number of worst choices) / frequency; the larger the positive value, the more important it is; 3) Probability percentage: the likelihood that an attribute will be selected as the best, ranging from 0 to 1; 4) P-value: statistical significance, with values below 0.05 indicating reliable results; 5) Most important/least important count: shows the absolute selection frequency. It is recommended to focus on preference percentage and score, as these two metrics best reflect the true importance of an attribute.