When designing a survey, there are two main types of data you can collect: quantitative and qualitative. Quantitative data is numeric in nature. It can be statistically analyzed. It is used for more confirmatory analysis. In other words, when you already have established ideas, quantitative data is best used to confirm these ideas. The types of questions that are best answered by quantitative data are “how” and “what” questions.
Quantitative data allows researchers to measure trends, compare groups, and identify statistically significant differences. Because responses are structured and standardized, results can be easily summarized in charts, tables, and dashboards. This makes quantitative methods especially valuable for benchmarking performance over time or comparing results across departments, locations, or customer segments.
Ambivista’s Survey Suite offers various question types to collect quantitative data:
Multiple choice
Multiple choice questions are ideal when respondents must select one or more predefined options. These questions provide clear, structured responses that are easy to code and analyze. They work particularly well for demographic information, preferences, or behavioral frequency questions.
Matrix table
Matrix tables allow respondents to evaluate multiple items using the same response scale. This format is efficient and reduces repetition, especially when measuring attitudes across several related statements. It also keeps the survey visually organized and consistent.
Rank order
Rank order questions require respondents to prioritize options from most important to least important. This type of question is particularly useful when you want to understand relative importance rather than simple agreement or selection.
Dynamic sum
Dynamic sum questions ask respondents to allocate a total value—such as 100 points—across different options. This approach helps quantify trade-offs and better understand how participants distribute importance or resources among choices.
Side by side
Side-by-side questions allow multiple related questions to appear next to each other, often using consistent response scales. This format is helpful when comparing similar attributes across different products, services, or experiences.
In contrast, qualitative data deals more with words, phrases, and sentences. It seeks to answer the “why” question in research. With qualitative data, you can gather incredibly rich data. It taps into reasons, motivations, and processes. However, such data does not lend itself to statistical analyses, but rather qualitative data analyses.
Qualitative responses often reveal insights that structured questions may miss. They provide context behind ratings and allow respondents to explain their experiences in their own words. While the analysis process may require coding, thematic analysis, or text analytics tools, the depth of insight gained can be invaluable for strategic decision-making.
Ambivista’s Survey Suite offers a simple way to collect qualitative data—with text entry. If you are looking for a quick phrase or sentence, a single line response should be used. If you are looking for more of a paragraph, a multiple line response should be used. However, if you would like a more in-depth and thorough response, essay text is the best way to go.
Text entry
Text entry questions can be strategically placed after quantitative questions to provide elaboration. For example, following a satisfaction rating with “Please explain your response” allows you to pair measurable data with meaningful context. This combination strengthens both interpretation and action planning.
Both quantitative and qualitative data offer value. For this reason, it’s sometimes best to combine them within a survey. Using a mixed-methods approach enables you to confirm patterns with numbers while also uncovering the underlying reasons behind those patterns. If you need help designing the survey and selecting question types based on data, our Ph.D.-level consultants at Ambivista are here to help!
