{"id":7297,"date":"2022-08-18T11:28:03","date_gmt":"2022-08-18T11:28:03","guid":{"rendered":"https:\/\/www.uxdesigninstitute.com\/blog\/?p=7297"},"modified":"2023-10-17T14:43:01","modified_gmt":"2023-10-17T14:43:01","slug":"analysing-ux-research","status":"publish","type":"post","link":"https:\/\/www.uxdesigninstitute.com\/blog\/analysing-ux-research\/","title":{"rendered":"Analysing UX research and synthesising results into valuable insights"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">UX research can be a valuable part of the UX design process. However, completing your study is only the first step in obtaining the insights you seek. Until you analyse the data you\u2019ve collected into a set of findings, synthesise those findings into insights and interpret the results, you won\u2019t be able to communicate how your research can help improve the user experience to your stakeholders and clients.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this post, we\u2019ll discuss how analysing UX research using quantitative and qualitative data will turn your findings into useful insights. We\u2019ll also look at how to interpret your findings. Here\u2019s what we\u2019ll cover:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#what-is-user-research-analysis\"><span style=\"font-weight: 400;\">What is user research analysis?<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#when-should-you-conduct-user-research-analysis\"><span style=\"font-weight: 400;\">When should you conduct user research analysis?<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#what-are-the-different-types-of-user-research-analysis\"><span style=\"font-weight: 400;\">What are the different types of user research analysis? (Quantitative vs. qualitative)<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#how-to-analyse-quantitative-research-data\"><span style=\"font-weight: 400;\">How to analyse quantitative research data<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#how-to-analyse-qualitative-research-data\"><span style=\"font-weight: 400;\">How to analyse qualitative research data<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#what-next\"><span style=\"font-weight: 400;\">What next? Synthesising and interpreting your user research data and presenting your findings<\/span><\/a><\/li>\n<\/ul>\n<h2><strong><a id=\"what-is-user-research-analysis\"><\/a>What is user research analysis?<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">User research analysis involves analysing the data you\u2019ve collected during your study. Analysis can be done in a variety of ways. The kind of analysis you choose to perform will depend on whether you\u2019ve collected <\/span><a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/quantitative-vs-qualitative-research\/\"><span style=\"font-weight: 400;\">qualitative or quantitative data<\/span><\/a><span style=\"font-weight: 400;\"> and what you were hoping to learn from your study. However, no matter what method you use, the goal of analysis is to identify the factual results of the study. In other words, it\u2019s during analysis that you use the data you collected to arrive at a set of <\/span><a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/presenting-ux-research-findings\/\"><span style=\"font-weight: 400;\">research findings<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<section id=\"promotion\" class=\"promotion-content-raw inlinepromo inlinepromo_professional-certificate-in-user-researchp-1 my-4\" style=\"\">\n\t<div class=\"w-container\">\n\t\t<div class=\"row align-items-center\">\n\t\t\t<div class=\"col-md-12 promotion-info\">\n                <a class=\"link-content\" href=\"https:\/\/www.uxdesigninstitute.com\/courses\/user-research?utm_source=blog&utm_medium=%20blog_panel_text&utm_campaign=blog_promo\" style=\"\">\n                    <p>[GET CERTIFIED IN USER RESEARCH]<\/p>\n                    <span>Take our Professional Certificate in User Research<\/span>\n                <\/a>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n<h3><strong>What\u2019s the difference between analysing UX research and synthesising UX research?<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">The findings you obtain from your analysis may be interesting, but they don\u2019t provide any real insight until you engage in synthesis. Synthesis is the process of bringing all the findings from analysis together to extract insights and conclusions from the data, as well as a set of actionable recommendations for the UX design of the product. While analysis provides a set of facts, synthesis makes those facts meaningful.<\/span><\/p>\n<p><a href=\"https:\/\/www.uxmatters.com\/mt\/archives\/2009\/04\/analysis-plus-synthesis-turning-data-into-insights.php\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Analysis and synthesis often happen at the same time<\/span><\/a><span style=\"font-weight: 400;\">. Yet, while we plan our analysis in anticipation of the questions we want to answer, synthesis is an emergent process through which we make connections and come up with possible insights as we go. Throughout this post, we\u2019ll talk about analysis and synthesis separately, but in reality, these processes are likely to overlap.<\/span><\/p>\n<h2><strong><a id=\"when-should-you-conduct-user-research-analysis\"><\/a>When should you conduct user research analysis?<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">The obvious answer to this question is after you\u2019ve finished collecting data from your user research study, but you\u2019ll need to think about analysis even before you start your user research. Before you begin, define a set of objectives and research questions that you want to answer and come up with ways to answer them.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Then, during the study, conduct analysis while your data is being collected. Analysis can help you ensure you\u2019re asking the right questions. For example, if you\u2019re conducting <\/span><a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/user-interviews-for-ux-research\/\"><span style=\"font-weight: 400;\">user interviews<\/span><\/a><span style=\"font-weight: 400;\"> and you conduct periodic analysis, you may find out you&#8217;re asking the wrong questions\u2014ones that don\u2019t really pertain to the variable you want to explore. The good thing is, if this is the case and you\u2019ve caught it early on, you haven\u2019t wasted the whole study on the wrong variable.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In addition to finding any errors, starting analysis during your study is also more efficient. But the truth is the majority of the real work in user research analysis happens at the end of the study. No matter what, though, the important thing is that you finish analysing all the data you collected before you interpret the results and draw conclusions.<\/span><\/p>\n<h2><strong><a id=\"what-are-the-different-types-of-user-research-analysis\"><\/a>What are the different types of user research analysis? (Quantitative vs. qualitative)<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">There are two types of user research, <\/span><a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/quantitative-vs-qualitative-research\/\"><span style=\"font-weight: 400;\">quantitative and qualitative<\/span><\/a><span style=\"font-weight: 400;\">, and they each require a different type of analysis.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Quantitative research gathers objective, numerical data and can help you answer questions about things like success rates, task times, and error rates. Quantitative data tells you the \u201cwhat\u201d and the \u201chow\u201d\u2014what do your users do \/ how do they interact with the product?\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Qualitative research gathers subjective, qualitative data, usually in the form of words (i.e. what the user says about their experience, how they feel, what they think about a product, etc.). When analysing qualitative research data, you\u2019re looking for themes and patterns across users\u2019 responses. Qualitative research can provide insight into what features are most important to users, how a given experience makes them feel, what they find difficult about a certain experience, and so on. Given the in-depth insights qualitative research can yield, analysing the results often takes longer than for quantitative research.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Now let\u2019s take a closer look at how to analyse both quantitative and qualitative data.\u00a0<\/span><\/p>\n<h2><strong><a id=\"how-to-analyse-quantitative-research-data\"><\/a>How to analyse quantitative research data<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">If you\u2019ve conducted a quantitative study, such as a survey with yes\/no or multiple choice questions, <\/span><a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/benefits-of-a-b-testing\/\"><span style=\"font-weight: 400;\">an A\/B test <\/span><\/a><span style=\"font-weight: 400;\">or an eye tracking test, you will be left with a large set of numerical data. Depending on how the data was collected, it will either already be laid out in a spreadsheet or will have to be entered into a spreadsheet manually, where each column corresponds to one question and each row includes one participant\u2019s answers.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The dataset in the spreadsheet will then be analysed statistically. <\/span><a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/7-programming-languages\/\"><span style=\"font-weight: 400;\">Programmes<\/span><\/a><span style=\"font-weight: 400;\"> like R or SPSS can be used to run statistical analysis or formulas can be plugged into a Google or Excel spreadsheet.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Before you start running statistical formulas on your data, go back to the original goals of the study and decide exactly what questions you want to answer.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, maybe you\u2019re curious to learn how long it takes a user to sign up for a newsletter. Or perhaps you\u2019re trying to find out if users are satisfied with the various steps of a checkout process. No matter what the goals are, make sure to concentrate your analysis there.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The good thing about quantitative analysis is that once you decide on the variables you want to analyse, it can be extremely quick and efficient to perform. Quantitative findings, such as the average time to complete a task, participants\u2019 satisfaction ratings with parts of a product or information on features they use the most, can lead to insights about whether the UX for a certain task should be refined and what features should be redesigned or eliminated from a product entirely.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Quantitative data can also be analysed to compare and contrast the way users from different demographic groups use a product. These findings can provide insight into the different use cases UX designers must keep in mind as they\u2019re creating the product\u2019s user experience.<\/span><\/p>\n<p><b>Learn more about quantitative analysis:<\/b> <a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/ux-kpis-and-how-to-measure-them\/\"><span style=\"font-weight: 400;\">The 7 Most Important UX KPIs (and how to measure them)<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2><strong><a id=\"how-to-analyse-qualitative-research-data\"><\/a>How to analyse qualitative research data<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">If you\u2019ve conducted a qualitative study, such as user interviews, focus groups or ethnographic research, you\u2019ll be left with a large amount of information in the form of words. If your participants didn\u2019t provide their answers in written form, you\u2019ll want to have all of the interviews or responses transcribed so you can easily read what participants said. While it can be expensive, it\u2019s worthwhile to use a service like <\/span><a href=\"http:\/\/rev.com\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Rev.com<\/span><\/a><span style=\"font-weight: 400;\"> to transcribe your interviews so your time is freed up to focus on other tasks.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Once the data is transcribed, you can organise it in a number of ways. One is to put it in a spreadsheet where each row represents the answers provided by a single participant. Another option is to upload that data to a qualitative analysis tool like <\/span><a href=\"https:\/\/www.qsrinternational.com\/nvivo-qualitative-data-analysis-software\/home\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">NVivo<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"https:\/\/www.dedoose.com\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Dedoose<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Just like with quantitative data, before you settle on a method for analysing the data qualitatively, you should revisit the original goals of your research and make sure that your analysis focuses on them.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, if you\u2019re designing a real estate app where users can find houses for sale, you\u2019ll want to focus on the demographics of potential users, what features they focus on most when searching for a home and what draws their attention to a given listing.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Although participants might have brought up other topics during your study, don\u2019t include them in your analysis if they don\u2019t pertain to your research goals.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There are several ways you can analyse qualitative data. Two popular options are content analysis and affinity mapping.<\/span><\/p>\n<h3><b>Content analysis<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Content analysis involves looking for patterns in the data and then coding them. It can be especially useful for evaluating long text data such as interview transcripts. Codes are essentially labels that you can apply to each chunk of text that brings up a particular topic.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, for the real estate app mentioned above, you might use codes such as budget, location and number of bedrooms. As you go through the text data, you will then label each chunk of data where these subjects are discussed.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here are the steps to perform a content analysis:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Decide on codes.<\/b><span style=\"font-weight: 400;\"> There are two ways to do this. On the one hand, you can come up with codes based on the topics you\u2019re hoping to find. If you\u2019re interested in seeing what people have to say on a house with a pool, you\u2019ll include the code \u201cpool\u201d in your analysis. On the other hand, you can see what topics emerge organically. If you review the data and notice that decks come up frequently, you\u2019ll include the code \u201cdeck\u201d in your analysis.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Assign codes as you work<\/b><span style=\"font-weight: 400;\">. This can be done manually or with a qualitative analysis program like those mentioned above.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Organise related codes into themes<\/b><span style=\"font-weight: 400;\">. Once you\u2019ve coded all the data, look for codes that speak to the same general topic and place these under larger umbrella categories called themes. For example, codes like \u201cgated community,\u201d \u201cgood schools\u201d and \u201csafe location\u201d can be grouped into a theme called \u201cneighbourhood.\u201d<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">If more than one member of your team is coding the data, you need to make sure everyone understands the codes the same way. To do this, before coding the entire dataset, each coder should code the same small part of the data and compare their work. If there is disagreement in the way the codes are applied, coders need to discuss the discrepancies until they\u2019ve agreed on how to apply the codes consistently.<\/span><\/p>\n<h3><b>Affinity mapping<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Another useful way to analyse the data is through <\/span><a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/affinity-diagrams-everything-you-need-to-know\/\"><span style=\"font-weight: 400;\">affinity mapping<\/span><\/a><span style=\"font-weight: 400;\">. Affinity mapping is a visual way of organising the data but, like content analysis, the overall goal is to identify patterns and themes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Although this is not the only way to conduct affinity mapping, <\/span><a href=\"https:\/\/www.userinterviews.com\/ux-research-field-guide-chapter\/research-analysis\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">User Interviews<\/span><\/a><span style=\"font-weight: 400;\"> recommends taking these four steps:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Write all the text data points on post-it notes. While this will likely lead to a lot of post-its, make sure to use as many as your project requires.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Stick all the post-its to a wall, whiteboard, or any other large surface that people can gather around.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Move the post-its into groups based on common themes that pertain to your research objectives. You may want to limit this process to about 20-30 minutes depending on how many post-its you have to organise and to prevent the affinity mapping session from sprawling.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Continue to re-organise the groups until time runs out or the team has come to a consensus. If time runs out and team members still disagree on the groups, have a discussion until everyone\u2019s on the same page.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Once you complete this process, you may want to label each group with an overarching theme that sums up the content.<\/span><\/p>\n<h2><strong><a id=\"what-next\"><\/a>What next? Synthesising and interpreting your user research data and presenting your findings<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">The final product of any user research analysis is interpreting your research and presenting your findings. Your presentation to stakeholders and clients should have sections for \u201ckey learnings\u201d and \u201crecommendations.\u201d In the \u201ckey learnings\u201d section you\u2019ll interpret your research data so it provides value. For example, say one of your insights is that there was a theme of \u201cbudget\u201d for the real estate app mentioned above. You interpret it in the form of a key learning, \u201cMake sure to feature budget prominently and not share anything above this price range.\u201d\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Then the \u201crecommendation\u201d makes this insight valuable by framing it as a recommended action. For example, the \u201crecommendations\u201d for the above key learning could be \u201cMake budget a major section of the website\u201d and \u201cdo not share anything above the user\u2019s price range, if they\u2019ve shared their budget.\u201d When possible, it helps to combine qualitative and quantitative findings for the biggest impact.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Interpreting the data is where you\u2019ll really probe the analysis for meaningful insights, and it\u2019s these insights that ultimately have the greatest benefit of any user research. Interpretation enables us to truly understand what users want, and don\u2019t want, from the product we\u2019re designing. Interpreting the research data and delivering a presentation that shares these insights increases our chances of creating product users will love. You can learn more about <\/span><a href=\"https:\/\/www.uxdesigninstitute.com\/blog\/presenting-ux-research-findings\/\"><span style=\"font-weight: 400;\">how to present your UX research findings in this guide<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Analysing UX research and synthesising the data that comes from it can play a huge part in the success of your end product. In this piece, we take a look at how analysing UX research will turn your findings into useful insights.<\/p>\n","protected":false},"author":35,"featured_media":7300,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[278],"tags":[],"class_list":["post-7297","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-design"],"_links":{"self":[{"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/posts\/7297","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/users\/35"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/comments?post=7297"}],"version-history":[{"count":9,"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/posts\/7297\/revisions"}],"predecessor-version":[{"id":9241,"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/posts\/7297\/revisions\/9241"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/media\/7300"}],"wp:attachment":[{"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/media?parent=7297"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/categories?post=7297"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.uxdesigninstitute.com\/blog\/wp-json\/wp\/v2\/tags?post=7297"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}