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Watch this video to learn more about the differences between qualitative and quantitative and continuous and discrete data: How to Measure Your Results Ideal for validating hypotheses, and assessing causality and correlation. Ideal for doing discovery research and generating hypotheses.Ĭollecting structured information about outcomes and results. Structured interviews, close-ended survey questions, controlled experiments, natural experiments.Ĭollecting information about attributes, characteristics, or preferences. Open-response survey questions, interviews, focus groups, and ethnographies. Words, pictures, symbols, audio and video.Ĭontinuous or discrete numerical data, data that can be counted and expressed as numbers. It’s usually expressed as either a continuous or discrete number or percentage It usually expresses feelings, opinions, recounts experiences, etc.ĭata that is numerical or can be counted. Unstructured, typically text-based data that can’t be counted or expressed as a number. Quantitative data can be expressed in numbers or counted in some way, producing structured response data. Qualitative survey data is typically in the form of open-text responses and produces unstructured data. Which method you should use depends on what type of data you have. There are many ways to analyze survey results. The more detailed and segmented your survey results are, the more robust they will be.
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This makes them harder to replicate across populations. Unexamined survey results lack depth and nuance. A critical part of any academic research finding is that the results are reliable and replicable. Rigorous survey analysis is incredibly important outside of the business world too. It can show you which types of customers are the happiest and point to hypotheses about why. For example, a simple net promoter score (NPS) survey can tell you how happy or unhappy your customers are.īut segmenting your NPS data by industry, company size, or even job title is much more revealing. Survey analysis can make the difference between an unhelpful statistic and a valuable business insight. A cursory glance at your quantitative and qualitative data won’t do your research project justice. No matter how large or small your survey is, you can find hidden gems in the results if you dig deep enough. If you’re still in the survey design part of your research project, read more about how to design successful surveys. This article will not only make a case for why you should do in-depth survey analysis but will give you the tools and methods to do it well. But failing to rigorously analyze your survey results undermines your entire research project. It’s easy to spend 20 minutes looking at your survey results, pull out the most noteworthy statistic, and call it a day. You’ve launched your survey, collected hundreds of responses, and now sit on a treasure trove of data. You’ve spent hours crafting smart, strategic, and unbiased survey questions.