Decoding Taylor Swift: A data-driven deep dive into the Swiftie psyche ๐ฑ๐ปโโ๏ธ
Taylor Swift's music has captivated millions, but what do her fans really think about her extensive catalog? We've crunched the numbers, analyzed the data, and uncovered some fascinating insights into how Swifties perceive and categorize their favorite artist's work. Let's dive in!
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The great debate: openers, encores, and everything in between โ.หโฎ๐งโฎห.โ
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Our study asked fans to categorize Swift's songs into potential opening numbers, encores, and songs they'd rather not hear (affectionately dubbed "Nah" songs). The results? As diverse as Swift's discography itself!
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Opening with a bang ๐ฅ
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Swifties seem to agree that high-energy tracks make for the best concert openers, but the results are more nuanced than previously suggested. "Shake It Off" emerged as the clear favorite for opening a concert, with 17 votes. "Love Story" follows closely behind with 14 votes, showing that nostalgia indeed plays a significant role. Interestingly, both "Cruel Summer" and "Blank Space" tied for third place with 13 votes each.
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This mix of songs from different eras of Swift's career suggests that fans appreciate both her newer hits and classic favorites when it comes to kicking off a show. The strong showing for "Love Story" does indeed speak to the power of nostalgia in concert experiences. It's worth noting that "...Ready for It?", while a popular song, received fewer votes (9) for the opening slot than might have been expected.
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Encore extravaganza ๐ค
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When it comes to encores, fans seem to favor a diverse mix of Taylor Swift's discography, with a surprising tie at the top. "Slut!" (Taylor's Version), "exile", "Guilty as Sin?", and "Bad Blood (Remix)" all received the highest number of votes with 13 each. This variety showcases the breadth of Swift's career and the different aspects of her artistry that resonate with fans for a memorable show finale.
โ
Close behind are "evermore", "Wildest Dreams", "ME!", "Love Story", and "Lavender Haze", each garnering 12 votes. It's particularly interesting to see both newer tracks and classic hits like "Love Story" maintaining strong popularity for the encore slot. This balance suggests that Swifties appreciate both nostalgia and Swift's artistic evolution when it comes to closing out a concert experience.
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The "Nah" list ๐
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Interestingly, some of Taylor Swift's tracks found themselves on the "Nah" list, indicating that fans might prefer not to hear them in a concert setting. "Clara Bow" tops this category with 13 votes, closely followed by "You're On Your Own, Kid", "You're Losing Me", and "Delicate", each receiving 12 votes.
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This doesn't necessarily mean fans dislike these songs - they might just feel they're not well-suited for live performances or don't fit as well into a concert setlist. It's particularly surprising to see "Delicate" on this list, given its popularity. The presence of both newer tracks like "Clara Bow" and older ones like "Delicate" suggests that the "Nah" list isn't tied to a specific era of Swift's career, but rather to individual song preferences in a live concert context.
โ
It's worth noting that even popular songs can end up on this list, highlighting the complex relationship fans have with different tracks in various contexts. This data provides an interesting insight into how Swifties perceive songs differently when considering them for a live performance versus general listening.
1. The "Midnights" Connection: Songs from "Midnights" like "Midnight Rain", "The Black Dog", and "The Tortured Poets Department" showed high similarity in set list placement. This suggests fans see these tracks working well in similar parts of a concert, perhaps as a cohesive segment showcasing the album's distinct sound.
โ
2. Cross-album transitions: There's an intriguing connection between "Guilty as Sin?" and "exile", with a high similarity percentage. This indicates fans see these songs from different albums as complementary in a live setting, potentially suggesting a smooth transition point in the set list that bridges different eras of Swift's career.
โ
3. The show-stoppers: "Shake It Off" stands out as dissimilar to most other songs in terms of placement. This likely reflects its perceived role as a high-energy, statement piece that occupies a unique position in the set list, perhaps as an opener, closer, or peak moment.
โ
4. Set list evolution: There's a noticeable pattern of higher similarity between songs from the same or adjacent eras, suggesting fans envision distinct segments for different periods of Swift's career within the concert. This could indicate a preference for a chronological journey through her discography or strategic placement of different styles throughout the show.
โ
5. Thematic groupings: Some songs from different albums showed higher similarity, such as "Is It Over Now? (Taylor's Version)" and "You're On Your Own, Kid". This suggests fans see them working well together in the set list based on thematic or emotional connections rather than just album cohesion.
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What does it all mean?! ๐๐ผ๐
โ
This card sort data paints a picture of an artist who continually evolves while maintaining certain core elements that define her work. Swift's ability to create cohesive album experiences, make bold stylistic shifts, and maintain thematic threads throughout her career is reflected in how fans perceive and categorize her songs. Moreover, the diversity of opinions on song categorization - with 59 different songs suggested as potential openers - speaks to the depth and breadth of Swift's discography. It also highlights the personal nature of music appreciation; what one fan sees as the perfect opener, another might categorize as a "Nah".
โ
In the end, this analysis gives us a fascinating glimpse into the complex web of associations in Swift's discography. It shows us not just how Swift has evolved as an artist, but how her fans have evolved with her, creating deep and sometimes unexpected connections between songs across her entire career. Whether you're a die-hard Swiftie or a casual listener, or a weirdo who just loves a good card sort, one thing is clear: Taylor Swift's music is rich, complex, and deeply meaningful to her fans. And with each new album, she continues to surprise, delight, and challenge our expectations.
โ
Conclusion: shaking up our understanding ๐ฅค๐ค
โ
This deep dive into the Swiftie psyche through a card sort reveals the complexity of Taylor Swift's discography and fans' relationship with it. From strategic song placement in a dream setlist to unexpected cross-era connections, we've uncovered layers of meaning that showcase Swift's artistry and her fans' engagement. The exercise demonstrates how a song can be a potential opener, mid-show energy boost, poignant closer, or a skip-worthy track, highlighting Swift's ability to create diverse, emotionally resonant music that serves various roles in the listening experience.
โ
The analysis underscores Swift's evolving career, with distinct album clusters alongside surprising connections, painting a picture of an artist who reinvents herself while maintaining a core essence. It also demonstrates how fan-driven analyses like card sorting can be insightful and engaging, offering a unique window into music fandom and reminding us that in Swift's discography, there's always more to discover. This exercise proves valuable whether you're a die-hard Swiftie, casual listener, or someone who loves to analyze pop culture phenomena.
I have two huge content structures I would like to A/B test. Do you think Treejack would be appropriate?"
โ Mike
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Hi Mike (and excellent question)!
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Firstly, yes, Treejack is great for testing more than one content structure. Itโs easy to run two separate Treejack studies โ even more than two. Itโll help you decide which structure you and your team should run with, and it wonโt take you long to set them up.
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When youโre creating the two tree tests with your two different content structures, include the same tasks in both tests. Using the same tasks will give an accurate measure of which structure performs best. Iโve done it before and I found that the visual presentation of the results โ especially the detailed path analysis pietrees โ made it really easy to compare Test A with Test B.
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Plus (and this is a big plus), if you need to convince stakeholders or teammates of which structure is the most effective, you canโt go past quantitative data, especially when its presented clearly โ itโs hard to argue with hard evidence!
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Hereโs two example of the kinds of results visualizations you could compare in your A/B test: the pietree, which shows correct and incorrect paths, and where people ended up:
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And the overall Task result, which breaks down success and directness scores, and has plenty of information worth comparing between two tests:
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Keep in mind that running an A/B tree test will affect how you recruit participants โ it may not be the best idea to have the same participants complete both tests in one go. But itโs an easy fix โ you could either recruit two different groups from the same demographic, or test one group and have a gap (of at least a day) between the two tests.
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Iโve one more quick question: why are your two content structures โhugeโ?
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I understand that sometimes these things are unavoidable โ you potentially work for a government organization, or a university, and you have to include all of the things. But if not, and if you havenโt already, you could run an open card sort to come up with another structure to test (think of it as an A/B/C test!), and to confirm that the categories youโre proposing work for people.
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You could even run a closed card sort to establish which content is more important to people than others (your categories could go from โVery importantโ to โUnimportantโ, or โUse everydayโ to โNever useโ, for example). You might be able to make your content structure a bit smaller, and still keep its usefulness. Just a thought... and of course, you could try to get this information from your analytics (if available) but just be cautious of this because of course analytics can only tell you what people did and not what they wanted to do.
Taylor Swift's music has captivated millions, but what do her fans really think about her extensive catalog? We've crunched the numbers, analyzed the data, and uncovered some fascinating insights into how Swifties perceive and categorize their favorite artist's work. Let's dive in!
โ
The great debate: openers, encores, and everything in between โ.หโฎ๐งโฎห.โ
โ
Our study asked fans to categorize Swift's songs into potential opening numbers, encores, and songs they'd rather not hear (affectionately dubbed "Nah" songs). The results? As diverse as Swift's discography itself!
โ
Opening with a bang ๐ฅ
โ
Swifties seem to agree that high-energy tracks make for the best concert openers, but the results are more nuanced than previously suggested. "Shake It Off" emerged as the clear favorite for opening a concert, with 17 votes. "Love Story" follows closely behind with 14 votes, showing that nostalgia indeed plays a significant role. Interestingly, both "Cruel Summer" and "Blank Space" tied for third place with 13 votes each.
โ
This mix of songs from different eras of Swift's career suggests that fans appreciate both her newer hits and classic favorites when it comes to kicking off a show. The strong showing for "Love Story" does indeed speak to the power of nostalgia in concert experiences. It's worth noting that "...Ready for It?", while a popular song, received fewer votes (9) for the opening slot than might have been expected.
โ
โ
Encore extravaganza ๐ค
โ
When it comes to encores, fans seem to favor a diverse mix of Taylor Swift's discography, with a surprising tie at the top. "Slut!" (Taylor's Version), "exile", "Guilty as Sin?", and "Bad Blood (Remix)" all received the highest number of votes with 13 each. This variety showcases the breadth of Swift's career and the different aspects of her artistry that resonate with fans for a memorable show finale.
โ
Close behind are "evermore", "Wildest Dreams", "ME!", "Love Story", and "Lavender Haze", each garnering 12 votes. It's particularly interesting to see both newer tracks and classic hits like "Love Story" maintaining strong popularity for the encore slot. This balance suggests that Swifties appreciate both nostalgia and Swift's artistic evolution when it comes to closing out a concert experience.
โ
โ
The "Nah" list ๐
โ
Interestingly, some of Taylor Swift's tracks found themselves on the "Nah" list, indicating that fans might prefer not to hear them in a concert setting. "Clara Bow" tops this category with 13 votes, closely followed by "You're On Your Own, Kid", "You're Losing Me", and "Delicate", each receiving 12 votes.
โ
This doesn't necessarily mean fans dislike these songs - they might just feel they're not well-suited for live performances or don't fit as well into a concert setlist. It's particularly surprising to see "Delicate" on this list, given its popularity. The presence of both newer tracks like "Clara Bow" and older ones like "Delicate" suggests that the "Nah" list isn't tied to a specific era of Swift's career, but rather to individual song preferences in a live concert context.
โ
It's worth noting that even popular songs can end up on this list, highlighting the complex relationship fans have with different tracks in various contexts. This data provides an interesting insight into how Swifties perceive songs differently when considering them for a live performance versus general listening.
1. The "Midnights" Connection: Songs from "Midnights" like "Midnight Rain", "The Black Dog", and "The Tortured Poets Department" showed high similarity in set list placement. This suggests fans see these tracks working well in similar parts of a concert, perhaps as a cohesive segment showcasing the album's distinct sound.
โ
2. Cross-album transitions: There's an intriguing connection between "Guilty as Sin?" and "exile", with a high similarity percentage. This indicates fans see these songs from different albums as complementary in a live setting, potentially suggesting a smooth transition point in the set list that bridges different eras of Swift's career.
โ
3. The show-stoppers: "Shake It Off" stands out as dissimilar to most other songs in terms of placement. This likely reflects its perceived role as a high-energy, statement piece that occupies a unique position in the set list, perhaps as an opener, closer, or peak moment.
โ
4. Set list evolution: There's a noticeable pattern of higher similarity between songs from the same or adjacent eras, suggesting fans envision distinct segments for different periods of Swift's career within the concert. This could indicate a preference for a chronological journey through her discography or strategic placement of different styles throughout the show.
โ
5. Thematic groupings: Some songs from different albums showed higher similarity, such as "Is It Over Now? (Taylor's Version)" and "You're On Your Own, Kid". This suggests fans see them working well together in the set list based on thematic or emotional connections rather than just album cohesion.
โ
What does it all mean?! ๐๐ผ๐
โ
This card sort data paints a picture of an artist who continually evolves while maintaining certain core elements that define her work. Swift's ability to create cohesive album experiences, make bold stylistic shifts, and maintain thematic threads throughout her career is reflected in how fans perceive and categorize her songs. Moreover, the diversity of opinions on song categorization - with 59 different songs suggested as potential openers - speaks to the depth and breadth of Swift's discography. It also highlights the personal nature of music appreciation; what one fan sees as the perfect opener, another might categorize as a "Nah".
โ
In the end, this analysis gives us a fascinating glimpse into the complex web of associations in Swift's discography. It shows us not just how Swift has evolved as an artist, but how her fans have evolved with her, creating deep and sometimes unexpected connections between songs across her entire career. Whether you're a die-hard Swiftie or a casual listener, or a weirdo who just loves a good card sort, one thing is clear: Taylor Swift's music is rich, complex, and deeply meaningful to her fans. And with each new album, she continues to surprise, delight, and challenge our expectations.
โ
Conclusion: shaking up our understanding ๐ฅค๐ค
โ
This deep dive into the Swiftie psyche through a card sort reveals the complexity of Taylor Swift's discography and fans' relationship with it. From strategic song placement in a dream setlist to unexpected cross-era connections, we've uncovered layers of meaning that showcase Swift's artistry and her fans' engagement. The exercise demonstrates how a song can be a potential opener, mid-show energy boost, poignant closer, or a skip-worthy track, highlighting Swift's ability to create diverse, emotionally resonant music that serves various roles in the listening experience.
โ
The analysis underscores Swift's evolving career, with distinct album clusters alongside surprising connections, painting a picture of an artist who reinvents herself while maintaining a core essence. It also demonstrates how fan-driven analyses like card sorting can be insightful and engaging, offering a unique window into music fandom and reminding us that in Swift's discography, there's always more to discover. This exercise proves valuable whether you're a die-hard Swiftie, casual listener, or someone who loves to analyze pop culture phenomena.
Cards have been created, sorted and sorted again. The participants are all finished and youโre left with a big pile of awesome data that will help you improve the user experience of your information architecture. Now what?Whether youโve run an open, hybrid or closed card sort online using an information architecture tool or youโve run an in person (moderated) card sort, it can be a bit daunting trying to figure out where to start the card sort analysis process.
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About this guide
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This two-part guide will help you on your way! For Part 1, weโre going to look at how to interpret and analyze the results from open and hybrid card sorts.
In open card sorts, participants sort cards into categories that make sense to them and they give each category a name of their own making.
In hybrid card sorts, some of the categories have already been defined for participants to sort the cards into but they also have the ability to create their own.
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Open and hybrid card sorts are great for generating ideas for category names and labels and understanding not only how your users expect your content to be grouped but also what they expect those groups to be called.In both parts of this series, Iโm going to be talking a lot about interpreting your results using Optimal Workshopโs online card sorting tool, OptimalSort, but most of what Iโm going to share is also applicable if youโre analyzing your data using a spreadsheet or using another tool.
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Understanding the two types of analysis: exploratory and statistical
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Similar to qualitative and quantitative methods, exploratory and statistical analysis in card sorting are two complementary approaches that work together to provide a detailed picture of your results.
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Exploratory analysis is intuitive and creative. Itโs all about going through the data and shaking it to see what ideas, patterns and insights fall out. This approach works best when you donโt have the numbers (smaller sample sizes) and when you need to dig into the details and understand the โwhyโ behind the statistics.
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Statistical analysis is all about the numbers. Hard data that tells you exactly how many people expected X to be grouped with Y and more and is very useful when youโre dealing with large sample sizes and when identifying similarities and differences across different groups of people.
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Depending on your objectives - whether you are starting from scratch or redesigning an existing IA - youโll generally need to use some combination of both of these approaches when analyzing card sort results. Learn more about exploratory and statistical analysis in Donna Spencerโs book.
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Start with the big picture
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When analyzing card sort results, start by taking an overall look at the results as a whole. Quickly cast your eye over each individual card sort and just take it all in. Look for common patterns in how the cards have been sorted and the category names given by participants. Does anything jump out as surprising? Are there similarities or differences between participant sorts? If youโre redesigning an existing IA, how do your results compare to the current state?If you ran your card sort using OptimalSort, your first port of call will be the Overview and Participants Table presented in the results section of the tool.If you ran a moderated card sort using OptimalSortโs printed cards, now is a good time to double check you got them all. And if you didnโt know about this handy feature of OptimalSort, itโs something to keep in mind for next time!The Participants Table shows a breakdown of your card sorting data by individual participant. Start by reviewing each individual card sort one by one by clicking on the arrow in the far left column next to the Participants numbers.
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Viewing individual participant card sorts in detail.
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From here you can easily flick back and forth between participants without needing to close that modal window. Donโt spend too much time on this โ youโre just trying to get a general impression of what happened.Keep an eye out for any card sorts that you might like to exclude from the results. For example participants who have lumped everything into one group and havenโt actually sorted the cards. Donโt worry - excluding or including participants isnโt permanent and can be toggled on or off at anytime.If you have a good number of responses, then the Participant Centric Analysis (PCA) tab (below) can be a good place to head next. Itโs great for doing a quick comparison of the different high-level approaches participants took when grouping the cards.The PCA tab provides the most insight when you have lots of results data (30+ completed card sorts) and at least one of the suggested IAs has a high level of agreement among your participants (50% or more agree with at least one IA).
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Participant Centric Analysis (PCA) tab for an open or hybrid card sort in OptimalSort.
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The PCA tab compares data from individual participants and surfaces the top three ways the cards were sorted. It also gives you some suggestions based on participant responses around what these categories could be called but try not to get too bogged down in those - youโre still just trying to gain an overall feel for the results at this stage.Now is also a good time to take a super quick peek at the Categories tab as it will also help you spot patterns and identify data that youโd like to dive deeper into a bit later on!Another really useful visualization tool offered by OptimalSort that will help you build that early, high-level picture of your results is the Similarity Matrix. This diagram helps you spot data clusters, or groups of cards that have been more frequently paired together by your participants, by surfacing them along the edge and shading them in dark blue. It also shows the proportion of times specific card pairings occurred during your study and displays the exact number on hover (below).
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OptimalSortโs Similarity Matrix showing that โFlat sandalsโ and โCourt shoesโ were paired by 91% of participants (31 times) in this example study.
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In the above screenshot example we can see three very clear clusters along the edge: โAnkle Bootsโ to โSlippersโ is one cluster, โSocksโ to โStockings & Hold Upsโ is the next and then we have โScarvesโ to โSunglassesโ. These clusters make it easy to spot the that cards that participants felt belonged together and also provides hard data around how many times that happened.Next up are the dendrograms. Dendrograms are also great for gaining an overall sense of how similar (or different) your participantsโ card sorts were to each other. Found under the Dendrogram tab in the results section of the tool, the two dendrograms are generated by different algorithms and which one you use depends largely on how many participants you have.
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If your study resulted in 30 or more completed card sorts, use the Actual Agreement Method (AAM) dendrogram and if your study had fewer than 30 completed card sorts, use the Best Merge Method (BMM) dendrogram.The AAM dendrogram (see below) shows only factual relationships between the cards and displays scores that precisely tell you that โX% of participants in this study agree with this exact groupingโ.In the below example, the study shown had 34 completed card sorts and the AAM dendrogram shows that 77% of participants agreed that the cards highlighted in green belong together and a suggested name for that group is โBlingโ. The tooltip surfaces one of the possible category names for this group and as demonstrated here it isnโt always the best or โrecommendedโ one. Take it with a grain of salt and be sure to thoroughly check the rest of your results before committing!
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AAM Dendrogram in OptimalSort.
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The BMM dendrogram (see below) is different to the AAM because it shows the percentage of participants that agree with parts of the grouping - it squeezes the data from smaller sample sizes and makes assumptions about larger clusters based on patterns in relationships between individual pairs.The AAM works best with larger sample sizes because it has more data to work with and doesnโt make assumptions while the BMM is more forgiving and seeks to fill in the gaps.The below screenshot was taken from an example study that had 7 completed card sorts and its BMM dendrogram shows that 50% of participants agreed that the cards highlighted in green down the left hand side belong to โAccessories, Bottoms, Topsโ.
BMM Dendrogram in OptimalSort.
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Drill down and cross-reference
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Once youโve gained a high level impression of the results, itโs time to dig deeper and unearth some solid insights that you can share with your stakeholders and back up your design decisions.Explore your open and hybrid card sort data in more detail by taking a closer look at the Categories tab. Open up each category and cross-reference to see if people were thinking along the same lines.Multiple participants may have created the same category label, but what lies beneath could be a very different story. Itโs important to be thorough here because the next step is to start standardizing or chunking individual participant categories together to help you make sense of your results.In open and hybrid sorts, participants will be able to label their categories themselves. This means that you may identify a few categories with very similar labels or perhaps spelling errors or different formats. You can standardize your categories by merging similar categories together to turn them into one.OptimalSort makes this really easy to do - you pretty much just tick the boxes alongside each category name and then hit the โStandardizeโ button up the top (see below). Donโt worry if you make a mistake or want to include or exclude groupings; you can unstandardize any of your categories anytime.
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Standardizing categories in OptimalSort.
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Once youโve standardized a few categories, youโll notice that the Agreement number may change. It tells you how many participants agreed with that grouping. An agreement number of 1.0 is equal to 100% meaning everyone agrees with everything in your newly standardized category while 0.6 means that 60% of your participants agree.Another number to watch for here is the number of participants who sorted a particular card into a category which will appear in the frequency column in dark blue in the right-hand column of the middle section of the below image.
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Categories table after groupings called โAccessoriesโ and โBagsโ have been standardized.
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A closer look at the standardized category for โAccessoriesโ.
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From the above screenshot we can see that in this study, 18 of the 26 participant categories selected agree that โCat Eye Sunglassesโ belongs under โAccessoriesโ.Once youโve standardized a few more categories you can head over to the Standardization Grid tab to review your data in more detail. In the below image we can see that 18 participants in this study felt that โBackpacksโ belong in a category named โBagsโ while 5 grouped them under โAccessoriesโ. Probably safe to say the backpacks should join the other bags in this case.
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Standardization Grid in OptimalSort.
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So thatโs a quick overview of how to interpret the results from your open or hybrid card sorts.Here's a link to Part 2 of this series where we talk about interpreting results from closed card sorts as well as next steps for applying these juicy insights to your IA design process.
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Further reading
Card Sorting 101 โ Learn about the differences between open, closed and hybrid card sorts, and how to run your own using OptimalSort.