diagrammatic representation of qualitative data

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Barbara Felver, data visualization specialist in the Research and Data Analysis (RDA) Division of Washington State’s Dept. of Social and Health Services does a great job illustrating a variety of quantitative and qualitative data for research and survey reports. For example, see her work in this year’s foster parents’ survey: http://www.dshs.wa.gov/pdf/ms/rda/research/11/207.pdf

[…] 6 Ideas for Displaying Qualitative Data | Ann’s Blog […]

[…] In her new Blog Post, Ann K. Emery puts forward 6 ideas for displaying qualitative data: […]

Thanks for a great overview of options for displaying qualitative data – and terrific examples. We will expand the options on the BetterEvaluation.org catalogue of options.

Thanks for sharing. To combine pictures and written information makes it easy to cpature even complex data.

HOW DISPLAYING QUALITATIVE DATA WITH NEW MINDSIGHT FROM THOUGHT LEADER for the understanding.

Fantastic Ann! Because qualitative research makes my heart beat fast with joy; I love to see more attention being paid to data vis of such rich information. Thank you, thank you for helping to spread the word that qual is beautiful too!

These are great tips that embrace the “visual learner” among the diverse audience of targeted recipents of qualitive research findings.

Incredibly helpful! I manage focus group feedback and started trying to give a visual to the vast breadth and variety of the qualitative input!

[…] from Nielsen Norman.   Awesome tips from CRC colleagues for larger community surveys.   Achievable qual visualization ideas from Ann Emery.   Some tools for qual analysis and visualization from Tech for […]

[…] Achievable qual visualization ideas from Ann Emery. […]

[…] recently shared six ideas for displaying qualitative data. Later this year, I’ll publish a follow-up post with additional […]

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How to Visualize Qualitative Data

Are you looking for ways to display your qualitative data? The vast majority of data visualization resources focus on quantitative data. In this article, let’s look at some of your options for qualitative data visualization, like word clouds, photographs, icons, diagrams, and timelines.

I used to conduct qualitative-heavy research projects pretty much all day every day. Key informant interviews, bellwether interviews, document review, focus groups, you name it… So I know from personal experience that 99.9 percent of qualitative reports look like this:

Which is fine, unless you want someone to, ya know, actually read your report.

Here are several ideas for presenting qualitative data for your organization’s reports, presentations, handouts, infographics, and more.

Word Clouds

Data visualization novices love to love word clouds, while data visualization experts love to hate word clouds.

Word Clouds Are Okay for Visualizing One-Word Descriptions, But Not for Visualizing Allllllll Your Qualitative Data

Here’s the most obvious strategy for visualizing text-based data: the word cloud , also known as a tag cloud . Frequent words or phrases are shown in larger, bolder font. Less-frequent words or phrases are shown in a smaller font.

I’m not advocating for run-of-the-mill word clouds where you simply dump your interview transcripts into a word cloud and hope for the best. Simply looking at how often a word or phrase appears in your dataset is  not  a sufficient way to analyze your data!

Instead, I’m talking about intentional word clouds – when you’ve considered other options, and have purposefully chosen a word cloud as your visualization of choice.

Here’s one example. When I conduct interviews as part of a qualitative research project, I often included a question in the interview protocol such as, “What one word you use to describe _____?” For example, “Mr. Policymaker, what one word would you use to describe public opinion towards poverty in the United States?” or “Mrs. Principal, what one word would you use to describe this new initiative’s effect, if any, on your charter school?”

The rest of the interview is obviously focused on loooooooong responses, and fuller descriptions, and plenty of examples. But for one or two moments during the conversation, it’s nice to pause and throw in a semi-qualitative short question.

Then, you’d visualize those one-word responses in a word cloud, like this one from Students First:

People described their favorite teacher using only one word and the adjectives were visualized in a word cloud shaped like an apple.

Here’s another example from the Pew Research Center , in which they visualized one-word descriptions of Barack Obama with packed bubbles. Packed bubbles are similar to word clouds. In word clouds, word frequency is depicted through font size. In packed bubbles, word frequency is depicted through bubble size.

People described Barack Obama using only one word and the adjectives were visualized in a bubble cloud (and then color-coded by the sentiment or tone of that adjective).

Word Clouds Are Okay for Visualizing Before/After Comparisons

Word clouds are also great for before/after comparisons, like these tweets describing breakups. Does your study involve pre/post tests with a few open-ended questions? Did you interview participants at multiple intervals during the study? You could adapt this technique for nearly any time series design.

What our breakups look like on Twitter http://t.co/yRZ8fBgRxx Before and after: pic.twitter.com/XQg7JKMar5 — Motherboard (@motherboard) September 23, 2014

The Best Software Tools for Creating Word Clouds

There are tons of free and low-cost websites for generating word clouds. The Poll Everywhere blog summarized nine of their favorite tools . My personal favorites are Wordle and Tagxedo.

Wordle is a free website for making word clouds in an oval or rectangular shape. It’s pretty easy to use. You simply copy all of your text (e.g., from your interview transcripts) and paste the text into Wordle. Then, you can customize the word cloud’s fonts, colors, and general shape to your liking. I suggest matching the fonts and colors to your project’s existing branding guidelines so that your word cloud matches the rest of your report or slideshow.

Tagxedo is a free website for making word clouds in fun shapes, like an apple, heart, or outline of the United States. You can pull in qualitative data directly from a website or Twitter feed (you don’t have to copy and paste, so you save a step). And, with Tagxedo, you could even have your word cloud printed on a t-shirt, tote bag, or coffee mug. I see Tagxedo as more of a decorative option than a researchy option.

Showcase Open-Ended Survey Data Beside Closed-Ended Data

What’s better than quantitative data? Or better than qualitative data? Quantitative and qualitative data combined!

Use this technique when your survey has both closed-ended and open-ended questions. Tie the responses together in one chart to add much-needed context. In this example, a survey asked nonprofits to describe what it was like to work with an outside consultant. Then, the survey asked them why their experiences were good or bad. Who cares if 33 percent of nonprofits had excellent experiences but we don’t have examples that describe why?

Rather than simply listing out the open-ended responses in your appendix, showcase them beside your stacked column chart.

Read more about this technique in this post written by Johanna Morariu and I.

Include Photos Beside Participants’ Responses

For your non-anonymous reporting, how about inserting photos of the interviewees next to their ideas?

Strategies for use of #interpreter services in hospitals http://t.co/EpvPyvNcMZ , from @abtassociates @NHeLP_org &more pic.twitter.com/O8V05h3DoM — Sabriya Rice (@sabriyarice) September 2, 2014

Here’s an example from the New York Times, where they added photographs beside interviewees’ responses :

Include Icons Beside Descriptions and Responses

Icons are so easy to use that there’s really no excuse for not using them to break up long sections of text.

Islamic State is one of the world’s richest terror groups. Here’s why: http://t.co/C0DsXqHkfD pic.twitter.com/ikNThUwNhb — Wall Street Journal (@WSJ) August 28, 2014

The Center on Budget and Policy Priorities used icons to visualize how working-family tax credits can help at every stage of life :

And the Center on Budget and Policy Priorities used icons again to help their readers understand how low-wage workers face a cut in tax credits :

Create Diagrams to Explain Complex Concepts and Processes

You can create diagrams for many aspects of your project, like this diagram explaining what type of protective gear is keeping doctors safe from Ebola:

Breaking down the protective gear helping keep doctors safe from Ebola http://t.co/bQUDqFoD1X pic.twitter.com/tDjkiSLFvs — Washington Post (@washingtonpost) September 15, 2014

Graphic Timelines

Regular text-based timelines + diagrams, photos, and other images = graphic timelines. Timelining is especially valuable in situations in which you need to track how a program, initiative, or campaign unfolds over time.

Interactive Timelines

Static Timelines

Maybe you’re not the New York Times, I get it. Anyone can design a static timeline from the comfort of their PowerPoint slide.

A Graphic Timeline of the 21st Century Conservation Service Corps http://t.co/GmpTR3qfqv #21CSC #conservation pic.twitter.com/nGydFtkXkC — The Corps Network (@TheCorpsNetwork) August 12, 2014

Color-Coded Phrases

One of my all-time favorite examples of qualitative data visualization comes from the New York Times’ election coverage in 2016. They compared and contrasted speeches from Donald Trump and Hillary Clinton . First, the New York Times team presented miniature thumbnail images of each nominee’s convention speech. You can even click on the thumbnail images and they’ll expand so that you can read the transcript. I love how the thumbnails provide a birds-eye-view analysis of the qualitative themes from each speech simply by color-coding certain phrases.

Directly underneath the thumbnails, the New York Times team pulled out a few sample quotes so that readers can get a sense of what was said.

Want to try this technique yourself? Color-coded text is deceptively simple. I suggest highlighting the entire phrase in a light color, as the New York Times has done in this example. Make sure you don’t just color-coded the font itself. It’s challenging for our eyes to read green letters, but it’s pretty easy for our eyes to read black letters against light green background shading.

Comment and Share Your Ideas

How are you visualizing qualitative data interviews, focus groups, surveys, document reviews, and other qualitative data sources? Please link to your favorite resources below. Let’s give virtual high fives to the creators and celebrate a job well done.

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How to Use Creative Data Visualization Techniques for Easy Comprehension of Qualitative Research

“A picture is worth a thousand words!”—an adage used so often stands true even whilst reporting your research data. Research studies with overwhelming data can perhaps be difficult to comprehend by some readers or can even be time-consuming. While presenting quantitative research data becomes easier with the help of graphs, pie charts, etc. researchers face an undeniable challenge whilst presenting qualitative research data. In this article, we will elaborate on effectively presenting qualitative research using data visualization techniques .

Table of Contents

What is Data Visualization?

Data visualization is the process of converting textual information into graphical and illustrative representations. It is imperative to think beyond numbers to get a holistic and comprehensive understanding of research data. Hence, this technique is adopted to help presenters communicate relevant research data in a way that’s easy for the viewer to interpret and draw conclusions.

What Is the Importance of Data Visualization in Qualitative Research?

According to the form in which the data is collected and expressed, it is broadly divided into qualitative data and quantitative data. Quantitative data expresses the size or quantity of data in a countable integer. Unlike quantitative data, qualitative data cannot be expressed in continuous integer values; it refers to data values ​​described in the non-numeric form related to subjects, places, things, events, activities, or concepts.

What Are the Advantages of Good Data Visualization Techniques?

Excellent data visualization techniques have several benefits:

• Human eyes are often drawn to patterns and colors. Moreover, in this age of Big Data , visualization can be considered an asset to quickly and easily comprehend large amounts of data generated in a research study.
• Enables viewers to recognize emerging trends and accelerate their response time on the basis of what is seen and assimilated.
• Illustrations make it easier to identify correlated parameters.
• Allows the presenter to narrate a story whilst helping the viewer understand the data and draw conclusions from it.
• As humans can process visual images better than texts, data visualization techniques enable viewers to remember them for a longer time.

Different Types of Data Visualization Techniques in Qualitative Research

Here are several data visualization techniques for presenting qualitative data for better comprehension of research data.

1. Word Clouds

• Word Clouds is a type of data visualization technique which helps in visualizing one-word descriptions.
• It is a single image composing multiple words associated with a particular text or subject.
• The size of each word indicates its importance or frequency in the data.
• Wordle and Tagxedo are two majorly used tools to create word clouds.

2. Graphic Timelines

• Graphic timelines are created to present regular text-based timelines with pictorial illustrations or diagrams, photos, and other images.
• It visually displays a series of events in chronological order on a timescale.
• Furthermore, showcasing timelines in a graphical manner makes it easier to understand critical milestones in a study.

3. Icons Beside Descriptions

• Rather than writing long descriptive paragraphs, including resembling icons beside brief and concise points enable quick and easy comprehension.

4. Heat Map

• Using a heat map as a data visualization technique better displays differences in data with color variations.
• The intensity and frequency of data is well addressed with the help of these color codes.
• However, a clear legend must be mentioned alongside the heat map to correctly interpret a heat map.
• Additionally, it also helps identify trends in data.

5. Mind Map

• A mind map helps explain concepts and ideas linked to a central idea.
• Allows visual structuring of ideas without overwhelming the viewer with large amounts of text.
• These can be used to present graphical abstracts

Do’s and Don’ts of Data Visualization Techniques

It perhaps is not easy to visualize qualitative data and make it recognizable and comprehensible to viewers at a glance. However, well-visualized qualitative data can be very useful in order to clearly convey the key points to readers and listeners in presentations.

Are you struggling with ways to display your qualitative data? Which data visualization techniques have you used before? Let us know about your experience in the comments section below!

nicely explained

None. And I want to use it from now.

Would it be ideal or suggested to use these techniques to display qualitative data in a thesis perhaps?

Using data visualization techniques in a qualitative research thesis can help convey your findings in a more engaging and comprehensible manner. Here’s a brief overview of how to incorporate data visualization in such a thesis:

Select Relevant Visualizations: Identify the types of data you have (e.g., textual, audio, visual) and the appropriate visualization techniques that can represent your qualitative data effectively. Common options include word clouds, charts, graphs, timelines, and thematic maps.

Data Preparation: Ensure your qualitative data is well-organized and coded appropriately. This might involve using qualitative analysis software like NVivo or Atlas.ti to tag and categorize data.

Create Visualizations: Generate visualizations that illustrate key themes, patterns, or trends within your qualitative data. For example: Word clouds can highlight frequently occurring terms or concepts. Bar charts or histograms can show the distribution of specific themes or categories. Timeline visualizations can help display chronological trends. Concept maps can illustrate the relationships between different concepts or ideas.

Integrate Visualizations into Your Thesis: Incorporate these visualizations within your thesis to complement your narrative. Place them strategically to support your arguments or findings. Include clear and concise captions and labels for each visualization, providing context and explaining their significance.

Interpretation: In the text of your thesis, interpret the visualizations. Explain what patterns or insights they reveal about your qualitative data. Offer meaningful insights and connections between the visuals and your research questions or hypotheses.

Maintain Consistency: Maintain a consistent style and formatting for your visualizations throughout the thesis. This ensures clarity and professionalism.

Ethical Considerations: If your qualitative research involves sensitive or personal data, consider ethical guidelines and privacy concerns when presenting visualizations. Anonymize or protect sensitive information as needed.

Review and Refinement: Before finalizing your thesis, review the visualizations for accuracy and clarity. Seek feedback from peers or advisors to ensure they effectively convey your qualitative findings.

Appendices: If you have a large number of visualizations or detailed data, consider placing some in appendices. This keeps the main body of your thesis uncluttered while providing interested readers with supplementary information.

Cite Sources: If you use specific software or tools to create your visualizations, acknowledge and cite them appropriately in your thesis.

Hope you find this helpful. Happy Learning!

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A multidisciplinary systematic review of the use of diagrams as a means of collecting data from research subjects: application, benefits and recommendations

• Muriah J Umoquit 1 ,
• Peggy Tso 1 , 2 ,
• Helen ED Burchett 3 &
• Mark J Dobrow 1 , 2  

BMC Medical Research Methodology volume  11 , Article number:  11 ( 2011 ) Cite this article

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In research, diagrams are most commonly used in the analysis of data and visual presentation of results. However there has been a substantial growth in the use of diagrams in earlier stages of the research process to collect data. Despite this growth, guidance on this technique is often isolated within disciplines.

A multidisciplinary systematic review was performed, which included 13 traditional healthcare and non-health-focused indexes, non-indexed searches and contacting experts in the field. English-language articles that used diagrams as a data collection tool and reflected on the process were included in the review, with no restriction on publication date.

The search identified 2690 documents, of which 80 were included in the final analysis. The choice to use diagrams for data collection is often determined by requirements of the research topic, such as the need to understand research subjects' knowledge or cognitive structure, to overcome cultural and linguistic differences, or to understand highly complex subject matter. How diagrams were used for data collection varied by the degrees of instruction for, and freedom in, diagram creation, the number of diagrams created or edited and the use of diagrams in conjunction with other data collection methods. Depending on how data collection is structured, a variety of options for qualitative and quantitative analysis are available to the researcher. The review identified a number of benefits to using diagrams in data collection, including the ease with which the method can be adapted to complement other data collection methods and its ability to focus discussion. However it is clear that the benefits and challenges of diagramming depend on the nature of its application and the type of diagrams used.

Discussion/Conclusion

The results of this multidisciplinary systematic review examine the application of diagrams in data collection and the methods for analyzing the unique datasets elicited. Three recommendations are presented. Firstly, the diagrammatic approach should be chosen based on the type of data needed. Secondly, appropriate instructions will depend on the approach chosen. And thirdly, the final results should present examples of original or recreated diagrams. This review also highlighted the need for a standardized terminology of the method and a supporting theoretical framework.

Peer Review reports

Diagrams are graphic representations used to explain the relationships and connections between the parts it illustrates. There are many subcategories of the broader term 'diagram', which are distinguished by the elements they incorporate or their overall topic. Two dominant subcategories include 'concept maps' and 'mind maps'[ 1 ]. Diagrams are typically brought into the research process in later stages of data analysis or when summarizing and presenting final results. It is commonplace to see a diagram illustrating how concepts or themes relate to each other or to explain how the research data relates to an underlying theory. These diagrams can be developed through the researchers' inductive reasoning of the data collected or may be assisted by computer software[ 2 ].

The use of diagrams in earlier stages of the research process (i.e. to collect data) is a relatively new method and is not a common data collection approach at present. However, their use is developing in multiple disciplines, including healthcare research. Diagrams have been used to collect data from research subjects by asking them to either draw a diagram themselves or modify a prototypic diagram supplied by the researcher. The use of diagrams in data collection has been viewed favorably in helping to gather rich data on healthcare topics. These research topics are widely varied and include collecting information to improve patient safety with medication[ 3 ], understanding neighborhood characteristics related to mental well-being[ 4 ], mapping out healthcare networks[ 5 ], evaluating patient educational programs[ 6 , 7 ], understanding how different populations view microbial illnesses[ 8 ], diagramming as part of nursing education that is evidence-based[ 9 ] and involves critical thinking[ 10 , 11 ], to engage youth in healthcare consultations[ 12 ], and to gain insights on physician professional growth[ 13 ] and their accountability relationships[ 14 ].

Despite the increasing use of diagrams in data collection, there lacks a strong "supportive structure" (pg. 343) for researchers choosing this method[ 15 ]. The use of diagrams in data collection has developed independently in multiple disciplines under a number of different names, making knowledge transfer regarding this technique difficult. For example, little has been published on process mapping outside of the organizational literature until fairly recently[ 5 , 16 , 17 ]. This has limited the exchange of best practices between disciplines. Researchers are often starting from scratch when designing their diagramming data collection approaches and their analysis of the unique data collected[ 15 ].

By conducting a multidisciplinary systematic review, as defined in the PRISMA statement[ 18 ], we hope to consolidate lessons learned and offer recommendations for researchers in healthcare and other disciplines about how diagrams may be incorporated into their data collection process. The questions that guided our search for relevant studies were:

(1) What drives the selection of a diagramming approach for data collection?

(2) What are the different approaches to diagramming for data collection?

(3) What are the different approaches to analyzing data collected with diagramming?

(4) What are the benefits and challenges of using diagramming for data collection?

Diagramming techniques used for data collection in the research process is a challenging area to review, given the variable terminology across, and even within, fields. A preliminary survey of the literature helped identify some key terminology used in different disciplines (e.g. "graphic elicitation" or "participatory diagramming"). The terms used in the titles and abstracts of the preliminary articles identified, as well as the keywords used to index them in databases, formed the basis of our multidisciplinary search strategy. We combined these specific terms with general 'diagram' terms and with general 'data collection' and/or 'analysis' terms.

In December 2009, we electronically searched 13 indexed sources, including traditional health care related indexes and non-health focused indexes (EMBASE; HealthSTAR; Medline; Cumulative Index to Nursing and Allied Health Literature; GEOBASE; InfoTrac Environmental Issues & Policy eCollection; ProQUEST Dissertations; Design and Applied Arts Index; Education Resources Information Center; International Bibliography of the Social Sciences; PsychINFO; Public Affairs Information Services; and Social Science Citation Index). To ensure that all appropriate references were identified and to limit publication bias, non-indexed sources were also searched via general search tools (i.e. Google Scholar and Google Books) to uncover any additional publications. To supplement the search, 35 experts, identified by our searches, were contacted and asked to identify additional relevant articles and grey literature.

Reference Manager 11 was used to support the review. Following the removal of duplicates, articles were screened based on their title and abstract. The full-text was then screened for articles not excluded based on their title/abstract. Articles were excluded if they were not written in English, did not use diagramming techniques in the data collection process (i.e. research subjects did not create or edit diagrams) or were not evaluative or reflective about the data collection process and/or analysis of data collected from diagramming methods. No publication date or publication type restrictions were imposed; research studies, theoretical articles, method articles and opinion pieces were included if they met the above criteria.

The screening was undertaken by two authors (MJU, PT). Double screening was done at regular intervals to ensure inter-rater reliability. Further, the two researchers met weekly during the screening and data extraction phases to discuss the nuances of the articles and to resolve differences by deliberation until consensus was reached.

A total of 2690 references were identified, after the removal of duplicates. Given our search had no publication date restrictions and included dissertations, full-text articles were sometimes difficult to retrieve. Authors were contacted when the article could not be found online or through the University of Toronto's library system. While 4 articles were retrieved in this manner, 27 full-text articles still could not be found and were ultimately excluded. In total, 233 full-text articles were screened and a total of 80 articles were included in the study's review. Figure 1 presents a flow diagram of our search and screening. Data was extracted on the general characteristics of the articles and the four objectives detailed earlier (see Table 1 ).

Flow of articles through the systematic review .

General characteristics

Of the 80 articles included in our review, 53 were published studies[ 1 , 4 – 15 , 19 – 58 ], 19 were dissertations[ 59 – 77 ], 2 were books[ 78 , 79 ] and 6 represented grey literature[ 3 , 80 – 84 ], including unpublished working papers submitted by key experts and reports available on the internet. These articles were published between 1986 and 2010, with the majority published after 2000 and a substantial increase after 2006. This suggests that interest in these techniques has been increasing in recent years.

The most common discipline, determined by the lead author's affiliation and/or publication title, was from the education field. Other disciplines included healthcare, engineering, environmental science, geography, industrial design, psychology, and social science. The majority of articles clearly specified the study sample size, which averaged 36 research subjects, with a range of 2 to 243. Diagramming methods were used with a wide variety of research subjects, including students (elementary to graduate school), farmers, nurses, physicians, engineers, administrators and graphic designers.

What drives the selection of a diagramming approach for data collection?

The majority of articles specified at least one explicit reason why a form of diagramming was selected for data collection. These reasons fall into two broad categories: requirements or challenges of the research topic and the unique dataset that results from using diagrams.

The specific research topic examined was the most common reason for researchers choosing a diagramming technique for data collection. For some research topics, past studies have validated diagramming data collection techniques as a useful way to collect data. For example, research has established the usefulness of diagrams in collecting data about research subjects' knowledge or cognitive structures[ 19 – 22 , 59 , 80 ]. Diagrams in data collection have also been validated as a means of measuring changes over time[ 6 , 10 , 20 , 22 – 24 , 60 , 85 ] and differences between participant groups[ 20 , 25 , 61 , 81 ]. Diagramming methods were also sought out when research topics were not conducive to the more common qualitative data collection methods, such as interviews alone. These reasons include a research topic that deals with a population with linguistic, cultural, social or intuitional barriers the researcher wants to overcome[ 12 , 14 , 15 , 26 – 29 , 79 ] or with highly complex subject matter[ 12 , 14 , 25 , 30 – 32 , 85 ]. Examples of highly complex subject matters include the abstract nature of the research topic of 'pedagogical constructs'[ 25 ] and the multifaceted and diverse nature of 'clinical accountability relationships'[ 14 ].

Secondly, researchers sought out diagramming data collection methods because of the benefits previous studies found regarding the quality and uniqueness of the collected dataset. When research subjects drew diagrams without prompts, previous studies concluded that it minimized the influence of the researcher on the participant and their responses[ 1 , 33 – 35 , 61 , 62 , 81 , 82 ]. Studies have also found that diagramming is a reflective tool for the research subjects[ 28 , 29 , 36 , 63 ]. Since diagrams can represent both concrete and theoretical notions[ 37 ], diagramming offers a more holistic coverage of the topic[ 29 , 38 , 61 ], with more uncensored and unique data gathered[ 1 , 24 , 28 , 35 , 39 , 40 , 58 ] than more traditional qualitative data collection methods.

What are the different approaches to diagramming for data collection?

A range of applications were identified, which varied widely based on the degree of instruction, degree of freedom in diagram creation, the number of diagrams created or edited and the use of diagram in conjunction with other data collection methods.

Half of the studies did not report the details of the instructions provided to the research subjects, except for describing the basic request to create a diagram. One study explicitly observed that specific instructions are needed to ensure the research participants create a diagram and not another form of written material[ 32 ], such as a drawing or table. Simple or short instructions were often given to research subjects when the diagram sought by the researchers did not have to conform to a rigid structure, such as life-cycle[ 13 ] and professional practice diagrams[ 32 ].

When the researcher sought highly structured diagrams, the degree of instruction provided to the research subjects ranged from the preferred method of giving specific and detailed instructions on what elements should be included (e.g. hierarchies, arrows) to showing an example of the type of diagram the researcher would like the participant to create. For example, in comparison to other diagramming techniques, concept maps have a fairly rigid definition and a very specific set of elements that the end diagram should contain. Such diagrams may require more detailed instructions. One study had associate nursing degree students draw their own concept maps after a 20-minute introductory tutorial, presentation of a sample diagram, discussion and question period, and instructions listing all elements to be included (e.g. arrangement of items, hierarchal order, linking concepts with arrow, labeling propositions, identifying cross-links/relationships)[ 9 ]. In some instances, research subjects were given the opportunity to practice the diagramming method and receive corrective feedback prior to data collection[ 19 , 23 , 41 , 64 ].

It was most common for research subjects to create an original diagram on their own, in groups or a combination of both. Alternatively, some studies had the participant edit either a designated diagram provided by the researcher[ 3 , 14 , 15 , 42 ] or a researcher-created diagram generated by the researcher during the interview with participant input[ 4 , 5 , 7 , 43 ]. A few studies also chose the middle ground between original and prepared diagrams. For example, some provided a central concept or word to create the diagram around[ 14 , 44 ], or included some words or shapes to fill in on a prepared diagram[ 45 ] and others gave research subjects a list of words to use in the creation of their diagram[ 65 , 80 ].

Half of the studies used diagrams at multiple times within the data collection process as a means of comparison. A subset used a pre-/post- (or time series) approach to data collection, allowing researchers to track changes before, after, and sometimes during an intervention. This was found primarily within the discipline of education. For example, Rios asked a sample of teachers to create concept maps at multiple time intervals in order to identify their conceptual models and examine the impact of student interactions on the teachers' subject matter structure or vice versa[ 66 ].

Some researchers explicitly expressed the idea that diagrams alone would not capture complete perspectives from the research subjects[ 57 , 77 ], suggesting that diagrams should be used in combination with other data collection methods. The majority of the studies did use diagramming data collection techniques in addition to other methods. In some cases the additional data collection methods were used explicitly in conjunction with the diagramming techniques, such as creating the diagram within interviews or discussion of the diagrams in later focus groups or interviews[ 8 , 23 , 39 , 59 , 60 , 67 ].

What are the different approaches to analyzing data collected with diagramming?

The majority of the articles reported details about the analysis of diagram data. The use of only quantitative or qualitative analysis, or a mixture of both types of analysis was fairly equally distributed among the articles. Within each of these three categories of analysis, there were a variety of different techniques used that are briefly outlined below.

The majority of studies comparing diagrams across time or across research subjects chose either quantitative techniques only or a mix of qualitative and quantitative techniques for their analysis. Quantitative analysis techniques included counting (e.g. number of concepts identified, number of links between concepts, number of examples given, levels in hierarchy) and scoring. The two most common scoring methods were structural scoring and relational scoring[ 22 ]. Structural scoring refers to when weights are assigned to hierarchical structures, links between concepts and other elements. For example, a link between two concepts may be 10 points, an example 1 point and invalid examples and cross links 0 points[ 40 ]. Relational scoring reflects the quality or importance of each concept or link as determined by the researcher, by comparison to a similar diagram created by an expert or by other research subjects[ 36 , 38 , 60 , 62 , 68 ]. Some studies using quantitative analysis showed diagrams to illustrate how the final counting and scoring of a diagram was completed in the presentation of final study results[ 35 , 40 , 41 , 46 – 49 , 54 , 62 ]. For example, Kesby had focus groups in Zimbabwe create scored diagrams with local materials of rocks, string and bottle caps[ 54 ]. These diagrams were photographed and also reproduced on computer for legibility. Including an original diagram in the presentation of results also helps to orient the reader to the type of diagram that was used.

Studies which had diagrams completed or edited in the presence of the researcher, included additional data collection methods, and/or studies using less structured diagrams to collect data were likely to use either qualitative techniques only or a mix of qualitative and quantitative techniques for their analysis. For example, Haidet et al. gave medical school research subjects less structure and encouraged creativity in their diagramming exercise within interviews[ 13 ]. The diagram was used as a prompt to stimulate discussion, which was then analyzed through the interview transcripts. The diagram itself was then displayed in the final results to visually summarize the verbal exchange. Qualitative analysis included thematic and content analysis of the diagrams and additional data sources, such as transcripts, to identify prominent topics, themes and patterns in the diagrams[ 13 , 25 , 26 , 34 , 63 , 66 , 69 – 71 ].

In some studies that used mixed-methods, the collected diagrams were the primary source of data and guided the analysis of additional data sources, for example, by providing the core themes for transcript analysis. In other cases the additional data sources played the dominant role in analysis and the diagrams were used almost as verification or visual representation to illustrate conclusions[ 72 ].

What are the benefits and challenges of using diagramming for data collection?

Some of the benefits to using diagrams for data collection have already been discussed in the section on why researchers chose diagramming data collection approaches. In addition to these, diagramming approaches that were seen as complementary to other data collection approaches were commonly used in interviews and focus groups[ 60 , 73 ]. They were found to help focus discussions on particular themes[ 32 ] and enabled research subjects to more easily reflect on a topic or their beliefs by helping them to express thoughts in a more structured and organized manner[ 50 , 51 ]. The use of diagrams was also seen to increase recall[ 52 ] and self-reflectiveness[ 53 , 54 ]. In 1992, Powell found that interviews which made use of diagramming approaches were more introspective and tended to be more theoretical and philosophical than those that did not use diagramming methods[ 25 ].

Over half of the articles discussed at least one challenge of using a diagramming method for data collection. Interestingly, these challenges were often contradicted by other articles. All studies completed their data collection with diagrams, with some reporting that the diagramming allowed research subjects to overcome challenges of verbal communication[ 12 , 14 , 15 , 26 – 29 , 75 , 79 ]. However, many studies found that at least some of the research subjects expressed difficulty or discomfort with the diagramming task[ 1 , 9 , 10 , 14 , 28 , 35 , 55 , 56 , 58 , 62 , 74 , 76 , 83 ]. Some identified the ease and speed of data collection as benefits of using diagramming approaches[ 24 , 31 , 54 ], while others saw it as being time-intensive, particularly for analysis[ 6 , 11 , 26 , 62 ]. Related to the visual organization and structure of knowledge that the diagrams presented, an advantage to using diagramming approaches for data collection is their ability to obtain unique and unsolicited data[ 7 , 14 , 15 , 26 , 29 , 33 , 42 , 43 , 58 ]. In contrast, there were also concerns regarding the data it did not collect, such as non-verbal communication, that require the discretion and experience of researchers to identify and interpret[ 15 , 24 , 84 ]. These contradictions illustrate that the benefits and challenges to using diagramming approaches for data collection depend on the application and type of diagram used in each research study.

This systematic review represents the first overview of diagrams being used as a data collection approach in multiple disciplines. In 2006, Nesbit & Adesope[ 86 ] conducted a widely cited meta-analysis looking at peer-reviewed articles focusing on learning with concept and knowledge maps and found that the interest in using diagrams appeared to be on the rise[ 86 ]. While our systematic review concurs that interest is growing, it differs from their meta-analysis in two ways. Firstly our definition of a diagram is much broader, encompassing a variety of diagrams that extend beyond concept and knowledge maps. As well as the structured diagrams that Nesbit & Adescope focused on, our review also includes less structured diagrams, such as the life-cycle and professional practice discussed in our results section. Secondly, our focus is solely on diagrams being used as a data collection approach, whereas their meta-analysis included diagrams used as analysis techniques as well.

Given our broad definition of a diagram, we have reviewed approaches for collecting data through a wide spectrum of diagrams, from highly structured concept maps to less defined diagrams. We have provided an overview of the instruction options for research subjects, the creation and analysis of diagramming as a data collection approach, as well as highlighted some of the benefits and challenges. While there is variation regarding the guidance in instructions and approaches to the construction of different diagrams, use of diagrams as a data collection tool as a whole is clearly increasing in healthcare and in other disciplines. This systematic review is the first step in consolidating this information to assist in the refinement of this approach. For those considering using diagramming as a data collection approach, we offer three recommendations. Firstly, the diagrammatic approach should be chosen based on the type of data needed to answer the research question(s). Secondly, based on the diagrammatic approach chosen, it is important to select the appropriate instructions needed. Finally, presentation of final results should include examples of the original or recreated diagrams.

Choice of Diagramming Approach

The most important considerations for choosing the diagramming approach is the type of data needed to answer the research question (e.g., examining change over time, exploring people's experiences or views) and the type of analysis preferred by the researcher. For example, highly structured diagrams allowed for valid quantitative analysis, such as counting and ranking, which could be compared across research subjects. It should be noted that there is some controversy whether items should even be counted and that both an over- or under-reliance may be dangerous to the final research conclusions[ 28 ]. In comparison, other approaches that used a less structured diagrams relied heavily on qualitative analysis.

Instruction and Creation

The appropriateness of different approaches to instructions to guide diagram creation is an important consideration in ensuring the validity of data. It is clear from our review that the initial instructions given to research subjects varied in structure but had a great impact on the resulting diagrams and their potential for different analysis techniques. If researchers require highly structured diagrams it may be useful to give research subjects more detailed instructions and the opportunity for practice and feedback[ 30 , 64 ].

Presentation of results

The last recommendation is that studies using diagramming data collection approaches should include visual presentations of the findings in their results sections. The use of diagrams often results in the collection of unique and unsolicited data through a visual component, which can then be displayed along with the final analysis. Just as diagrams can provide data not easily obtained through verbal data collection techniques, visual presentation of the collected diagrams may provide insights not as easily grasped through verbal communication of study results. While providing a scanned image of the original diagram can be difficult at times, it is also possible to present a photograph of the original diagram or a computer-generated recreation of the diagram. This is especially important given the variation in terminology between disciplines, as it relays to the reader the type and structure of diagrams created or used.

In addition to the recommendations for researchers considering the use of diagramming data collection approaches, this multidisciplinary review also identifies areas where future research is needed. This review required a substantial amount of preliminary work to understand the terminology used to describe diagrams and this data collection approach across different disciplines and fields. The intent was to devise a sensitive search, so as to cast a wide net in order to capture articles in a range of disciplines where the terminology is not standardized. Cole et al. illustrate this issue by identifying over a dozen different terms that are used to describe concept maps[ 56 ]. Thus far, development of terminology has focused on the end result, i.e. what type of diagram is created based on the elements it contains, rather than focusing on the actual data collection approach itself. This has created different data collection approaches that use diagrams separately, isolating research done across disciplines and even within disciplines. Therefore, it is our recommendation that efforts are directed towards standardizing the terminology for this data collection method. This would allow researchers to maintain the work they have done regarding specific types of diagrams, whilst providing an umbrella term to help with the sharing of best practices. Future research should also be directed at identification of the underpinning theory of the method as the review demonstrated a gap in this area[ 15 , 32 , 35 ]. Such a theory may help to further inform researchers regarding the appropriate use and applications for diagramming data collection approaches.

A limitation of our review is that the database search strategy did not capture general articles on visual data collection methods, which may include specific information on diagramming data collection approaches. However the non-indexed searches and articles identified by experts did pick up some of these articles. While efforts were made to contact authors to retrieve articles from our search, twenty-seven full-text articles were irretrievable. Ten of these were dissertations and five were books. This may have contributed to an incomplete representation of what the literature has to offer about diagramming data collection approaches.

There has been a growing interest in the use of diagrams for data collection in the research process over recent years, as shown by the increase in publications and the wide range of approaches developed for diagramming data collection and diagram data analysis. As noted earlier, diagrams have been used to collect rich data on a variety of healthcare topics and it is expected that the use of this method will continue to grow. The results of this multidisciplinary systematic review provide an overview of the application of diagrams in research data collection and the methods for analyzing the unique datasets elicited. Recommendations are presented to assist researchers considering the use of diagrams in their data collection process. This review also highlighted the need for a standardized terminology of the method and a supporting theoretical framework.

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Umoquit, M.J., Tso, P., Burchett, H.E. et al. A multidisciplinary systematic review of the use of diagrams as a means of collecting data from research subjects: application, benefits and recommendations. BMC Med Res Methodol 11 , 11 (2011). https://doi.org/10.1186/1471-2288-11-11

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Buttoning up research: How to present and visualize qualitative data

15 Minute Read

There is no doubt that data visualization is an important part of the qualitative research process. Whether you're preparing a presentation or writing up a report, effective visualizations can help make your findings clear and understandable for your audience. 

In this blog post, we'll discuss some tips for creating effective visualizations of qualitative data. 

First, let's take a closer look at what exactly qualitative data is.

What is qualitative data?

Qualitative data is information gathered through observation, questionnaires, and interviews. It's often subjective, meaning that the researcher has to interpret it to draw meaningful conclusions from it. 

The difference between qualitative data and quantitative data

When researchers use the terms qualitative and quantitative, they're referring to two different types of data. Qualitative data is subjective and descriptive, while quantitative data is objective and numerical.

Qualitative data is often used in research involving psychology or sociology. This is usually where a researcher may be trying to identify patterns or concepts related to people's behavior or attitudes. It may also be used in research involving economics or finance, where the focus is on numerical values such as price points or profit margins. 

Before we delve into how best to present and visualize qualitative data, it's important that we highlight how to be gathering this data in the first place. ‍

How best to gather qualitative data

In order to create an effective visualization of qualitative data, ensure that the right kind of information has been gathered. 

Here are six ways to gather the most accurate qualitative data:

• Define your research question: What data is being set out to collect? A qualitative research question is a definite or clear statement about a condition to be improved, a project’s area of concern, a troubling question that exists, or a difficulty to be eliminated. It not only defines who the participants will be but guides the data collection methods needed to achieve the most detailed responses.
• ‍ Determine the best data collection method(s): The data collected should be appropriate to answer the research question. Some common qualitative data collection methods include interviews, focus groups, observations, or document analysis. Consider the strengths and weaknesses of each option before deciding which one is best suited to answer the research question.  ‍
• Develop a cohesive interview guide: Creating an interview guide allows researchers to ask more specific questions and encourages thoughtful responses from participants. It’s important to design questions in such a way that they are centered around the topic of discussion and elicit meaningful insight into the issue at hand. Avoid leading or biased questions that could influence participants’ answers, and be aware of cultural nuances that may affect their answers.
• ‍ Stay neutral – let participants share their stories: The goal is to obtain useful information, not to influence the participant’s answer. Allowing participants to express themselves freely will help to gather more honest and detailed responses. It’s important to maintain a neutral tone throughout interviews and avoid judgment or opinions while they are sharing their story. 
• ‍ Work with at least one additional team member when conducting qualitative research: Participants should always feel comfortable while providing feedback on a topic, so it can be helpful to have an extra team member present during the interview process – particularly if this person is familiar with the topic being discussed. This will ensure that the atmosphere of the interview remains respectful and encourages participants to speak openly and honestly.
• ‍ Analyze your findings: Once all of the data has been collected, it’s important to analyze it in order to draw meaningful conclusions. Use tools such as qualitative coding or content analysis to identify patterns or themes in the data, then compare them with prior research or other data sources. This will help to draw more accurate and useful insights from the results. 

By following these steps, you will be well-prepared to collect and analyze qualitative data for your research project. Next, let's focus on how best to present the qualitative data that you have gathered and analyzed.

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How to visually present qualitative data.

When it comes to how to present qualitative data visually, the goal is to make research findings clear and easy to understand. To do this, use visuals that are both attractive and informative. 

Presenting qualitative data visually helps to bring the user’s attention to specific items and draw them into a more in-depth analysis. Visuals provide an efficient way to communicate complex information, making it easier for the audience to comprehend. 

Additionally, visuals can help engage an audience by making a presentation more interesting and interactive.

Here are some tips for creating effective visuals from qualitative data:

• ‍ Choose the right type of visualization: Consider which type of visual would best convey the story that is being told through the research. For example, bar charts or line graphs might be appropriate for tracking changes over time, while pie charts or word clouds could help show patterns in categorical data. 
• ‍ Include contextual information: In addition to showing the actual numbers, it's helpful to include any relevant contextual information in order to provide context for the audience. This can include details such as the sample size, any anomalies that occurred during data collection, or other environmental factors.
• ‍ Make it easy to understand: Always keep visuals simple and avoid adding too much detail or complexity. This will help ensure that viewers can quickly grasp the main points without getting overwhelmed by all of the information. 
• ‍ Use color strategically: Color can be used to draw attention to certain elements in your visual and make it easier for viewers to find the most important parts of it. Just be sure not to use too many different colors, as this could create confusion instead of clarity. 
• ‍ Use charts or whiteboards: Using charts or whiteboards can help to explain the data in more detail and get viewers engaged in a discussion. This type of visual tool can also be used to create storyboards that illustrate the data over time, helping to bring your research to life. 

Visualizing qualitative data in Notably

Notably helps researchers visualize their data on a flexible canvas, charts, and evidence based insights. As an all-in-one research platform, Notably enables researchers to collect, analyze and present qualitative data effectively.

Notably provides an intuitive interface for analyzing data from a variety of sources, including interviews, surveys, desk research, and more. Its powerful analytics engine then helps you to quickly identify insights and trends in your data . Finally, the platform makes it easy to create beautiful visuals that will help to communicate research findings with confidence. 

Research Frameworks in Analysis

The canvas in Analysis is a multi-dimensional workspace to play with your data spatially to find likeness and tension. Here, you may use a grounded theory approach to drag and drop notes into themes or patterns that emerge in your research. Utilizing the canvas tools such as shapes, lines, and images, allows researchers to build out frameworks such as journey maps, empathy maps, 2x2's, etc. to help synthesize their data.

Going one step further, you may begin to apply various lenses to this data driven canvas. For example, recoloring by sentiment shows where pain points may distributed across your customer journey. Or, recoloring by participant may reveal if one of your participants may be creating a bias towards a particular theme.

Exploring Qualitative Data through a Quantitative Lens

Once you have begun your analysis, you may visualize your qualitative data in a quantitative way through charts. You may choose between a pie chart and or a stacked bar chart to visualize your data. From here, you can segment your data to break down the ‘bar’ in your bar chart and slices in your pie chart one step further.

To segment your data, you can choose between ‘Tag group’, ‘Tag’, ‘Theme’, and ‘Participant'. Each group shows up as its own bar in the bar chart or slice in the pie chart. For example, try grouping data as ‘Participant’ to see the volume of notes assigned to each person. Or, group by ‘Tag group’ to see which of your tag groups have the most notes.

Depending on how you’ve grouped or segmented your charts will affect the options available to color your chart. Charts use colors that are a mix of sentiment, tag, theme, and default colors. Consider color as a way of assigning another layer of meaning to your data. For example, choose a red color for tags or themes that are areas of friction or pain points. Use blue for tags that represent opportunities.

AI Powered Insights and Cover Images

One of the most powerful features in Analysis is the ability to generate insights with AI. Insights combine information, inspiration, and intuition to help bridge the gap between knowledge and wisdom. Even before you have any tags or themes, you may generate an AI Insight from your entire data set. You'll be able to choose one of our AI Insight templates that are inspired by trusted design thinking frameworks to stimulate generative, and divergent thinking. With just the click of a button, you'll get an insight that captures the essence and story of your research. You may experiment with a combination of tags, themes, and different templates or, create your own custom AI template. These insights are all evidence-based, and are centered on the needs of real people. You may package these insights up to present your research by embedding videos, quotes and using AI to generate unique cover image.

You can sign up to run an end to end research project for free and receive tips on how to make the most out of your data. Want to chat about how Notably can help your team do better, faster research? Book some time here for a 1:1 demo with your whole team.

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Tabular and diagrammatic presentation of bi-variate data

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• Published: September 1983
• Volume 50 , pages 537–540, ( 1983 )

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Chapter 18. Data Analysis and Coding

Introduction.

Piled before you lie hundreds of pages of fieldnotes you have taken, observations you’ve made while volunteering at city hall. You also have transcripts of interviews you have conducted with the mayor and city council members. What do you do with all this data? How can you use it to answer your original research question (e.g., “How do political polarization and party membership affect local politics?”)? Before you can make sense of your data, you will have to organize and simplify it in a way that allows you to access it more deeply and thoroughly. We call this process coding . [1] Coding is the iterative process of assigning meaning to the data you have collected in order to both simplify and identify patterns. This chapter introduces you to the process of qualitative data analysis and the basic concept of coding, while the following chapter (chapter 19) will take you further into the various kinds of codes and how to use them effectively.

To those who have not yet conducted a qualitative study, the sheer amount of collected data will be a surprise. Qualitative data can be absolutely overwhelming—it may mean hundreds if not thousands of pages of interview transcripts, or fieldnotes, or retrieved documents. How do you make sense of it? Students often want very clear guidelines here, and although I try to accommodate them as much as possible, in the end, analyzing qualitative data is a bit more of an art than a science: “The process of bringing order, structure, and interpretation to a mass of collected data is messy, ambiguous, time-consuming, creative, and fascinating. It does not proceed in a linear fashion: it is not neat. At times, the researcher may feel like an eccentric and tormented artist; not to worry, this is normal” ( Marshall and Rossman 2016:214 ).

To complicate matters further, each approach (e.g., Grounded Theory, deep ethnography, phenomenology) has its own language and bag of tricks (techniques) when it comes to analysis. Grounded Theory, for example, uses in vivo coding to generate new theoretical insights that emerge from a rigorous but open approach to data analysis. Ethnographers, in contrast, are more focused on creating a rich description of the practices, behaviors, and beliefs that operate in a particular field. They are less interested in generating theory and more interested in getting the picture right, valuing verisimilitude in the presentation. And then there are some researchers who seek to account for the qualitative data using almost quantitative methods of analysis, perhaps counting and comparing the uses of certain narrative frames in media accounts of a phenomenon. Qualitative content analysis (QCA) often includes elements of counting (see chapter 17). For these researchers, having very clear hypotheses and clearly defined “variables” before beginning analysis is standard practice, whereas the same would be expressly forbidden by those researchers, like grounded theorists, taking a more emergent approach.

All that said, there are some helpful techniques to get you started, and these will be presented in this and the following chapter. As you become more of an expert yourself, you may want to read more deeply about the tradition that speaks to your research. But know that there are many excellent qualitative researchers that use what works for any given study, who take what they can from each tradition. Most of us find this permissible (but watch out for the methodological purists that exist among us).

Qualitative Data Analysis as a Long Process!

Although most of this and the following chapter will focus on coding, it is important to understand that coding is just one (very important) aspect of the long data-analysis process. We can consider seven phases of data analysis, each of which is important for moving your voluminous data into “findings” that can be reported to others. The first phase involves data organization. This might mean creating a special password-protected Dropbox folder for storing your digital files. It might mean acquiring computer-assisted qualitative data-analysis software ( CAQDAS ) and uploading all transcripts, fieldnotes, and digital files to its storage repository for eventual coding and analysis. Finding a helpful way to store your material can take a lot of time, and you need to be smart about this from the very beginning. Losing data because of poor filing systems or mislabeling is something you want to avoid. You will also want to ensure that you have procedures in place to protect the confidentiality of your interviewees and informants. Filing signed consent forms (with names) separately from transcripts and linking them through an ID number or other code that only you have access to (and store safely) are important.

Once you have all of your material safely and conveniently stored, you will need to immerse yourself in the data. The second phase consists of reading and rereading or viewing and reviewing all of your data. As you do this, you can begin to identify themes or patterns in the data, perhaps writing short memos to yourself about what you are seeing. You are not committing to anything in this third phase but rather keeping your eyes and mind open to what you see. In an actual study, you may very well still be “in the field” or collecting interviews as you do this, and what you see might push you toward either concluding your data collection or expanding so that you can follow a particular group or factor that is emerging as important. For example, you may have interviewed twelve international college students about how they are adjusting to life in the US but realized as you read your transcripts that important gender differences may exist and you have only interviewed two women (and ten men). So you go back out and make sure you have enough female respondents to check your impression that gender matters here. The seven phases do not proceed entirely linearly! It is best to think of them as recursive; conceptually, there is a path to follow, but it meanders and flows.

Coding is the activity of the fourth phase . The second part of this chapter and all of chapter 19 will focus on coding in greater detail. For now, know that coding is the primary tool for analyzing qualitative data and that its purpose is to both simplify and highlight the important elements buried in mounds of data. Coding is a rigorous and systematic process of identifying meaning, patterns, and relationships. It is a more formal extension of what you, as a conscious human being, are trained to do every day when confronting new material and experiences. The “trick” or skill is to learn how to take what you do naturally and semiconsciously in your mind and put it down on paper so it can be documented and verified and tested and refined.

At the conclusion of the coding phase, your material will be searchable, intelligible, and ready for deeper analysis. You can begin to offer interpretations based on all the work you have done so far. This fifth phase might require you to write analytic memos, beginning with short (perhaps a paragraph or two) interpretations of various aspects of the data. You might then attempt stitching together both reflective and analytical memos into longer (up to five pages) general interpretations or theories about the relationships, activities, patterns you have noted as salient.

As you do this, you may be rereading the data, or parts of the data, and reviewing your codes. It’s possible you get to this phase and decide you need to go back to the beginning. Maybe your entire research question or focus has shifted based on what you are now thinking is important. Again, the process is recursive , not linear. The sixth phase requires you to check the interpretations you have generated. Are you really seeing this relationship, or are you ignoring something important you forgot to code? As we don’t have statistical tests to check the validity of our findings as quantitative researchers do, we need to incorporate self-checks on our interpretations. Ask yourself what evidence would exist to counter your interpretation and then actively look for that evidence. Later on, if someone asks you how you know you are correct in believing your interpretation, you will be able to explain what you did to verify this. Guard yourself against accusations of “ cherry-picking ,” selecting only the data that supports your preexisting notion or expectation about what you will find. [2]

The seventh and final phase involves writing up the results of the study. Qualitative results can be written in a variety of ways for various audiences (see chapter 20). Due to the particularities of qualitative research, findings do not exist independently of their being written down. This is different for quantitative research or experimental research, where completed analyses can somewhat speak for themselves. A box of collected qualitative data remains a box of collected qualitative data without its written interpretation. Qualitative research is often evaluated on the strength of its presentation. Some traditions of qualitative inquiry, such as deep ethnography, depend on written thick descriptions, without which the research is wholly incomplete, even nonexistent. All of that practice journaling and writing memos (reflective and analytical) help develop writing skills integral to the presentation of the findings.

Remember that these are seven conceptual phases that operate in roughly this order but with a lot of meandering and recursivity throughout the process. This is very different from quantitative data analysis, which is conducted fairly linearly and processually (first you state a falsifiable research question with hypotheses, then you collect your data or acquire your data set, then you analyze the data, etc.). Things are a bit messier when conducting qualitative research. Embrace the chaos and confusion, and sort your way through the maze. Budget a lot of time for this process. Your research question might change in the middle of data collection. Don’t worry about that. The key to being nimble and flexible in qualitative research is to start thinking and continue thinking about your data, even as it is being collected. All seven phases can be started before all the data has been gathered. Data collection does not always precede data analysis. In some ways, “qualitative data collection is qualitative data analysis.… By integrating data collection and data analysis, instead of breaking them up into two distinct steps, we both enrich our insights and stave off anxiety. We all know the anxiety that builds when we put something off—the longer we put it off, the more anxious we get. If we treat data collection as this mass of work we must do before we can get started on the even bigger mass of work that is analysis, we set ourselves up for massive anxiety” ( Rubin 2021:182–183 ; emphasis added).

The Coding Stage

A code is “a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data” ( Saldaña 2014:5 ). Codes can be applied to particular sections of or entire transcripts, documents, or even videos. For example, one might code a video taken of a preschooler trying to solve a puzzle as “puzzle,” or one could take the transcript of that video and highlight particular sections or portions as “arranging puzzle pieces” (a descriptive code) or “frustration” (a summative emotion-based code). If the preschooler happily shouts out, “I see it!” you can denote the code “I see it!” (this is an example of an in vivo, participant-created code). As one can see from even this short example, there are many different kinds of codes and many different strategies and techniques for coding, more of which will be discussed in detail in chapter 19. The point to remember is that coding is a rigorous systematic process—to some extent, you are always coding whenever you look at a person or try to make sense of a situation or event, but you rarely do this consciously. Coding is the process of naming what you are seeing and how you are simplifying the data so that you can make sense of it in a way that is consistent with your study and in a way that others can understand and follow and replicate. Another way of saying this is that a code is “a researcher-generated interpretation that symbolizes or translates data” ( Vogt et al. 2014:13 ).

As with qualitative data analysis generally, coding is often done recursively, meaning that you do not merely take one pass through the data to create your codes. Saldaña ( 2014 ) differentiates first-cycle coding from second-cycle coding. The goal of first-cycle coding is to “tag” or identify what emerges as important codes. Note that I said emerges—you don’t always know from the beginning what will be an important aspect of the study or not, so the coding process is really the place for you to begin making the kinds of notes necessary for future analyses. In second-cycle coding, you will want to be much more focused—no longer gathering wholly new codes but synthesizing what you have into metacodes.

You might also conceive of the coding process in four parts (figure 18.1). First, identify a representative or diverse sample set of interview transcripts (or fieldnotes or other documents). This is the group you are going to use to get a sense of what might be emerging. In my own study of career obstacles to success among first-generation and working-class persons in sociology, I might select one interview from each career stage: a graduate student, a junior faculty member, a senior faculty member.

Second, code everything (“ open coding ”). See what emerges, and don’t limit yourself in any way. You will end up with a ton of codes, many more than you will end up with, but this is an excellent way to not foreclose an interesting finding too early in the analysis. Note the importance of starting with a sample of your collected data, because otherwise, open coding all your data is, frankly, impossible and counterproductive. You will just get stuck in the weeds.

Third, pare down your coding list. Where you may have begun with fifty (or more!) codes, you probably want no more than twenty remaining. Go back through the weeds and pull out everything that does not have the potential to bloom into a nicely shaped garden. Note that you should do this before tackling all of your data . Sometimes, however, you might need to rethink the sample you chose. Let’s say that the graduate student interview brought up some interesting gender issues that were pertinent to female-identifying sociologists, but both the junior and the senior faculty members identified as male. In that case, I might read through and open code at least one other interview transcript, perhaps a female-identifying senior faculty member, before paring down my list of codes.

This is also the time to create a codebook if you are using one, a master guide to the codes you are using, including examples (see Sample Codebooks 1 and 2 ). A codebook is simply a document that lists and describes the codes you are using. It is easy to forget what you meant the first time you penciled a coded notation next to a passage, so the codebook allows you to be clear and consistent with the use of your codes. There is not one correct way to create a codebook, but generally speaking, the codebook should include (1) the code (either name or identification number or both), (2) a description of what the code signifies and when and where it should be applied, and (3) an example of the code to help clarify (2). Listing all the codes down somewhere also allows you to organize and reorganize them, which can be part of the analytical process. It is possible that your twenty remaining codes can be neatly organized into five to seven master “themes.” Codebooks can and should develop as you recursively read through and code your collected material. [3]

Fourth, using the pared-down list of codes (or codebook), read through and code all the data. I know many qualitative researchers who work without a codebook, but it is still a good practice, especially for beginners. At the very least, read through your list of codes before you begin this “ closed coding ” step so that you can minimize the chance of missing a passage or section that needs to be coded. The final step is…to do it all again. Or, at least, do closed coding (step four) again. All of this takes a great deal of time, and you should plan accordingly.

Researcher Note

People often say that qualitative research takes a lot of time. Some say this because qualitative researchers often collect their own data. This part can be time consuming, but to me, it’s the analytical process that takes the most time. I usually read every transcript twice before starting to code, then it usually takes me six rounds of coding until I’m satisfied I’ve thoroughly coded everything. Even after the coding, it usually takes me a year to figure out how to put the analysis together into a coherent argument and to figure out what language to use. Just deciding what name to use for a particular group or idea can take months. Understanding this going in can be helpful so that you know to be patient with yourself.

—Jessi Streib, author of The Power of the Past and Privilege Lost 

Note that there is no magic in any of this, nor is there any single “right” way to code or any “correct” codes. What you see in the data will be prompted by your position as a researcher and your scholarly interests. Where the above codes on a preschooler solving a puzzle emerged from my own interest in puzzle solving, another researcher might focus on something wholly different. A scholar of linguistics, for example, may focus instead on the verbalizations made by the child during the discovery process, perhaps even noting particular vocalizations (incidence of grrrs and gritting of the teeth, for example). Your recording of the codes you used is the important part, as it allows other researchers to assess the reliability and validity of your analyses based on those codes. Chapter 19 will provide more details about the kinds of codes you might develop.

Saldaña ( 2014 ) lists seven “necessary personal attributes” for successful coding. To paraphrase, they are the following:

• Having (or practicing) good organizational skills
• Perseverance
• The ability and willingness to deal with ambiguity
• Flexibility
• Creativity, broadly understood, which includes “the ability to think visually, to think symbolically, to think in metaphors, and to think of as many ways as possible to approach a problem” (20)
• Commitment to being rigorously ethical
• Having an extensive vocabulary [4]

Writing Analytic Memos during/after Coding

Coding the data you have collected is only one aspect of analyzing it. Too many beginners have coded their data and then wondered what to do next. Coding is meant to help organize your data so that you can see it more clearly, but it is not itself an analysis. Thinking about the data, reviewing the coded data, and bringing in the previous literature (here is where you use your literature review and theory) to help make sense of what you have collected are all important aspects of data analysis. Analytic memos are notes you write to yourself about the data. They can be short (a single page or even a paragraph) or long (several pages). These memos can themselves be the subject of subsequent analytic memoing as part of the recursive process that is qualitative data analysis.

Short analytic memos are written about impressions you have about the data, what is emerging, and what might be of interest later on. You can write a short memo about a particular code, for example, and why this code seems important and where it might connect to previous literature. For example, I might write a paragraph about a “cultural capital” code that I use whenever a working-class sociologist says anything about “not fitting in” with their peers (e.g., not having the right accent or hairstyle or private school background). I could then write a little bit about Bourdieu, who originated the notion of cultural capital, and try to make some connections between his definition and how I am applying it here. I can also use the memo to raise questions or doubts I have about what I am seeing (e.g., Maybe the type of school belongs somewhere else? Is this really the right code?). Later on, I can incorporate some of this writing into the theory section of my final paper or article. Here are some types of things that might form the basis of a short memo: something you want to remember, something you noticed that was new or different, a reaction you had, a suspicion or hunch that you are developing, a pattern you are noticing, any inferences you are starting to draw. Rubin ( 2021 ) advises, “Always include some quotation or excerpt from your dataset…that set you off on this idea. It’s happened to me so many times—I’ll have a really strong reaction to a piece of data, write down some insight without the original quotation or context, and then [later] have no idea what I was talking about and have no way of recreating my insight because I can’t remember what piece of data made me think this way” ( 203 ).

All CAQDAS programs include spaces for writing, generating, and storing memos. You can link a memo to a particular transcript, for example. But you can just as easily keep a notebook at hand in which you write notes to yourself, if you prefer the more tactile approach. Drawing pictures that illustrate themes and patterns you are beginning to see also works. The point is to write early and write often, as these memos are the building blocks of your eventual final product (chapter 20).

In the next chapter (chapter 19), we will go a little deeper into codes and how to use them to identify patterns and themes in your data. This chapter has given you an idea of the process of data analysis, but there is much yet to learn about the elements of that process!

Qualitative Data-Analysis Samples

The following three passages are examples of how qualitative researchers describe their data-analysis practices. The first, by Harvey, is a useful example of how data analysis can shift the original research questions. The second example, by Thai, shows multiple stages of coding and how these stages build upward to conceptual themes and theorization. The third example, by Lamont, shows a masterful use of a variety of techniques to generate theory.

Example 1: “Look Someone in the Eye” by Peter Francis Harvey ( 2022 )

I entered the field intending to study gender socialization. However, through the iterative process of writing fieldnotes, rereading them, conducting further research, and writing extensive analytic memos, my focus shifted. Abductive analysis encourages the search for unexpected findings in light of existing literature. In my early data collection, fieldnotes, and memoing, classed comportment was unmistakably prominent in both schools. I was surprised by how pervasive this bodily socialization proved to be and further surprised by the discrepancies between the two schools.…I returned to the literature to compare my empirical findings.…To further clarify patterns within my data and to aid the search for disconfirming evidence, I constructed data matrices (Miles, Huberman, and Saldaña 2013). While rereading my fieldnotes, I used ATLAS.ti to code and recode key sections (Miles et al. 2013), punctuating this process with additional analytic memos. ( 2022:1420 )

Example 2:” Policing and Symbolic Control” by Mai Thai ( 2022 )

Conventional to qualitative research, my analyses iterated between theory development and testing. Analytical memos were written throughout the data collection, and my analyses using MAXQDA software helped me develop, confirm, and challenge specific themes.…My early coding scheme which included descriptive codes (e.g., uniform inspection, college trips) and verbatim codes of the common terms used by field site participants (e.g., “never quit,” “ghetto”) led me to conceptualize valorization. Later analyses developed into thematic codes (e.g., good citizens, criminality) and process codes (e.g., valorization, criminalization), which helped refine my arguments. ( 2022:1191–1192 )

Example 3: The Dignity of Working Men by Michèle Lamont ( 2000 )

To analyze the interviews, I summarized them in a 13-page document including socio-demographic information as well as information on the boundary work of the interviewees. To facilitate comparisons, I noted some of the respondents’ answers on grids and summarized these on matrix displays using techniques suggested by Miles and Huberman for standardizing and processing qualitative data. Interviews were also analyzed one by one, with a focus on the criteria that each respondent mobilized for the evaluation of status. Moreover, I located each interviewee on several five-point scales pertaining to the most significant dimensions they used to evaluate status. I also compared individual interviewees with respondents who were similar to and different from them, both within and across samples. Finally, I classified all the transcripts thematically to perform a systematic analysis of all the important themes that appear in the interviews, approaching the latter as data against which theoretical questions can be explored. ( 2000:256–257 )

Sample Codebook 1

This is an abridged version of the codebook used to analyze qualitative responses to a question about how class affects careers in sociology. Note the use of numbers to organize the flow, supplemented by highlighting techniques (e.g., bolding) and subcoding numbers.

01. CAPS: Any reference to “capitals” in the response, even if the specific words are not used

01.1: cultural capital 01.2: social capital 01.3: economic capital

(can be mixed: “0.12”= both cultural and asocial capital; “0.23”= both social and economic)

01. CAPS: a reference to “capitals” in which the specific words are used [ bold : thus, 01.23 means that both social capital and economic capital were mentioned specifically

02. DEBT: discussion of debt

02.1: mentions personal issues around debt 02.2: discusses debt but in the abstract only (e.g., “people with debt have to worry”)

03. FirstP: how the response is positioned

03.1: neutral or abstract response 03.2: discusses self (“I”) 03.3: discusses others (“they”)

Sample Coded Passage:

* Question: What other codes jump out to you here? Shouldn’t there be a code for feelings of loneliness or alienation? What about an emotions code ?

Sample Codebook 2

This is an example that uses "word" categories only, with descriptions and examples for each code

Further Readings

Elliott, Victoria. 2018. “Thinking about the Coding Process in Qualitative Analysis.” Qualitative Report 23(11):2850–2861. Address common questions those new to coding ask, including the use of “counting” and how to shore up reliability.

Friese, Susanne. 2019. Qualitative Data Analysis with ATLAS.ti. 3rd ed. A good guide to ATLAS.ti, arguably the most used CAQDAS program. Organized around a series of “skills training” to get you up to speed.

Jackson, Kristi, and Pat Bazeley. 2019. Qualitative Data Analysis with NVIVO . 3rd ed. Thousand Oaks, CA: SAGE. If you want to use the CAQDAS program NVivo, this is a good affordable guide to doing so. Includes copious examples, figures, and graphic displays.

LeCompte, Margaret D. 2000. “Analyzing Qualitative Data.” Theory into Practice 39(3):146–154. A very practical and readable guide to the entire coding process, with particular applicability to educational program evaluation/policy analysis.

Miles, Matthew B., and A. Michael Huberman. 1994. Qualitative Data Analysis: An Expanded Sourcebook . 2nd ed. Thousand Oaks, CA: SAGE. A classic reference on coding. May now be superseded by Miles, Huberman, and Saldaña (2019).

Miles, Matthew B., A. Michael Huberman, and Johnny Saldaña. 2019. Qualitative Data Analysis: A Methods Sourcebook . 4th ed. Thousand Oaks, CA; SAGE. A practical methods sourcebook for all qualitative researchers at all levels using visual displays and examples. Highly recommended.

Saldaña, Johnny. 2014. The Coding Manual for Qualitative Researchers . 2nd ed. Thousand Oaks, CA: SAGE. The most complete and comprehensive compendium of coding techniques out there. Essential reference.

Silver, Christina. 2014. Using Software in Qualitative Research: A Step-by-Step Guide. 2nd ed. Thousand Oaks, CA; SAGE. If you are unsure which CAQDAS program you are interested in using or want to compare the features and usages of each, this guidebook is quite helpful.

Vogt, W. Paul, Elaine R. Vogt, Diane C. Gardner, and Lynne M. Haeffele2014. Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods . New York: The Guilford Press. User-friendly reference guide to all forms of analysis; may be particularly helpful for those engaged in mixed-methods research.

• When you have collected content (historical, media, archival) that interests you because of its communicative aspect, content analysis (chapter 17) is appropriate. Whereas content analysis is both a research method and a tool of analysis, coding is a tool of analysis that can be used for all kinds of data to address any number of questions. Content analysis itself includes coding. ↵
• Scientific research, whether quantitative or qualitative, demands we keep an open mind as we conduct our research, that we are “neutral” regarding what is actually there to find. Students who are trained in non-research-based disciplines such as the arts or philosophy or who are (admirably) focused on pursuing social justice can too easily fall into the trap of thinking their job is to “demonstrate” something through the data. That is not the job of a researcher. The job of a researcher is to present (and interpret) findings—things “out there” (even if inside other people’s hearts and minds). One helpful suggestion: when formulating your research question, if you already know the answer (or think you do), scrap that research. Ask a question to which you do not yet know the answer. ↵
• Codebooks are particularly useful for collaborative research so that codes are applied and interpreted similarly. If you are working with a team of researchers, you will want to take extra care that your codebooks remain in synch and that any refinements or developments are shared with fellow coders. You will also want to conduct an “intercoder reliability” check, testing whether the codes you have developed are clearly identifiable so that multiple coders are using them similarly. Messy, unclear codes that can be interpreted differently by different coders will make it much more difficult to identify patterns across the data. ↵
• Note that this is important for creating/denoting new codes. The vocabulary does not need to be in English or any particular language. You can use whatever words or phrases capture what it is you are seeing in the data. ↵

A first-cycle coding process in which gerunds are used to identify conceptual actions, often for the purpose of tracing change and development over time.  Widely used in the Grounded Theory approach.

A first-cycle coding process in which terms or phrases used by the participants become the code applied to a particular passage.  It is also known as “verbatim coding,” “indigenous coding,” “natural coding,” “emic coding,” and “inductive coding,” depending on the tradition of inquiry of the researcher.  It is common in Grounded Theory approaches and has even given its name to one of the primary CAQDAS programs (“NVivo”).

Computer-assisted qualitative data-analysis software.  These are software packages that can serve as a repository for qualitative data and that enable coding, memoing, and other tools of data analysis.  See chapter 17 for particular recommendations.

The purposeful selection of some data to prove a preexisting expectation or desired point of the researcher where other data exists that would contradict the interpretation offered.  Note that it is not cherry picking to select a quote that typifies the main finding of a study, although it would be cherry picking to select a quote that is atypical of a body of interviews and then present it as if it is typical.

A preliminary stage of coding in which the researcher notes particular aspects of interest in the data set and begins creating codes.  Later stages of coding refine these preliminary codes.  Note: in Grounded Theory , open coding has a more specific meaning and is often called initial coding : data are broken down into substantive codes in a line-by-line manner, and incidents are compared with one another for similarities and differences until the core category is found.  See also closed coding .

A set of codes, definitions, and examples used as a guide to help analyze interview data.  Codebooks are particularly helpful and necessary when research analysis is shared among members of a research team, as codebooks allow for standardization of shared meanings and code attributions.

The final stages of coding after the refinement of codes has created a complete list or codebook in which all the data is coded using this refined list or codebook.  Compare to open coding .

A first-cycle coding process in which emotions and emotionally salient passages are tagged.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

• Diagrammatic Representation of Data

Suppose you are interested to compare the marks of your mates in a test. How can you make the comparison interesting? It can be done by the diagrammatic representations of data. You can use a bar diagram, histograms, pie-charts etc for this.  You will be able to answer questions like –

How will you find out the number of students in the various categories of marks in a certain test? What can you say about the marks obtained by the maximum students? Also, how can you compare the marks of your classmates in five other tests? Is it possible for you to remember the marks of each and every student in all subjects? No! Also, you don’t have the time to compare the marks of every student. Merely noting down the marks and doing comparisons is not interesting at all. Let us study them in detail.

Suggested Videos

Bar diagram.

This is one of the simplest techniques to do the comparison for a given set of data. A bar graph is a graphical representation of the data in the form of rectangular bars or columns of equal width. It is the simplest one and easily understandable among the graphs by a group of people.

Browse more Topics under Statistical Description Of Data

• Introduction to Statistics
• Textual and Tabular Representation of Data
• Frequency Distribution
• Frequency Polygon  
• Cumulative Frequency Graph or Ogive

Construction of a Bar Diagram

• Draw two perpendicular lines intersecting each other at a point O. The vertical line is the y-axis and the horizontal is the x-axis.
• Choose a suitable scale to determine the height of each bar.
• On the horizontal line, draw the bars at equal distance with corresponding heights.
• The space between the bars should be equal.

Properties of a Bar Diagram

• Each bar or column in a bar graph is of equal width.
• All bars have a common base.
• The height of the bar corresponds to the value of the data.
• The distance between each bar is the same.

Types of Bar Diagram

A bar graph can be either vertical or horizontal depending upon the choice of the axis as the base. The horizontal bar diagram is used for qualitative data. The vertical bar diagram is used for the quantitative data or time series data. Let us take an example of a bar graph showing the comparison of marks of a student in all subjects out of 100 marks for two tests.

With the bar graph, we can also compare the marks of students in each subject other than the marks of one student in every subject. Also, we can draw the bar graph for every student in all subjects.

We can use another way of diagrammatical representation of data. If we are working with a continuous data set or grouped dataset, we can use a histogram for the representation of data.

• A histogram is similar to a bar graph except for the fact that there is no gap between the rectangular bars. The rectangular bars show the area proportional to the frequency of a variable and the width of the bars represents the class width or class interval.
• Frequency means the number of times a variable is occurring or is present. It is an area graph. The heights of the rectangles are proportional to the corresponding frequencies of similar classes.

Construction of Histogram

• Choose a suitable scale for both the axes to determine the height and width of each bar
• On the horizontal line, draw the bars with corresponding heights
• There should be no gap between two consecutive bars showing the continuity of the data
• If the grouped frequencies are not continuous, the first thing to do is to make them continuous

It is done by adding the average of the difference between the lower limit of the class interval and the upper limit of the preceding class width to the upper limits of all the classes. The same quantity is subtracted from the lower limits of the classes.

Properties of Histogram

• Each bar or column in a bar graph is of equal width and corresponds to the equal class interval
• If the classes are of unequal width then the height of the bars will be proportional to the ration of the frequencies to the width of the classes
• All bars have a common base
• The height of the bar corresponds to the frequency of the data

Suppose we have a data set showing the marks obtained out of 100 by a group of 35 students in statistics. We can find the number of students in the various marks category with the help of the histogram.

A line graph is a type of chart or graph which shows information when a series of data is joined by a line. It shows the changes in the data over a period of time. In a simple line graph, we plot each pair of values of (x, y). Here, the x-axis denotes the various time point (t), and the y-axis denotes the observation based on the time.

Properties of a Line Graph

• It consists of Vertical and Horizontal scales. These scales may or may not be uniform.
• Data point corresponds to the change over a period of time.
• The line joining these data points shows the trend of change.

Below is the line graph showing the number of buses passing through a particular street over a period of time:

Solved Examples for diagrammatic Representation of Data

Problem 1: Draw the histogram for the given data.

Solution: This grouped frequency distribution is not continuous. We need to convert it into a continuous distribution with exclusive type classes. This is done by averaging the difference of the lower limit of one class and the upper limit of the preceding class. Here, d = ½ (19 – 18) = ½ = 0.5. We add 0.5 to all the upper limits and we subtract 0.5 from all the lower limits.

The corresponding histogram is

Draw a line graph for the production of two types of crops for the given years.

Solution: The required graph is

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• Diagrammatic Presentation Of Data

Diagrammatic Presentation of Data

The diagrammatic presentation of data gives an immediate understanding of the real situation to be defined by the data in comparison to the tabular presentation of data or textual representations. It translates the highly complex ideas included in numbers into a more concrete and quickly understandable form pretty effectively. Diagrams may be less certain but are much more efficient than tables in displaying the data. There are many kinds of diagrams in general use. Amongst them the significant ones are the following:

(i) Geometric diagram

(ii) Frequency diagram

(iii) Arithmetic line graph

Also check: Meaning and Objective of Tabulation

Basics of Diagrammatic Presentation

Concept of Diagrammatic Presentation

• It is a technique of presenting numeric data through pictograms, cartograms, bar diagrams, and pie diagrams. It is the most attractive and appealing way to represent statistical data. Diagrams help in visual comparison and they have a bird’s eye view.
• Under pictograms, we use pictures to present data. For example, if we have to show the production of cars, we can draw cars. Suppose the production of cars is 40,000, we can show it by a picture having four cars, where 1 car represents 10,000 units.
• Under cartograms, we make use of maps to show the geographical allocation of certain things.
• Bar diagrams are rectangular and placed on the same base. Their heights represent the magnitude/value of the variable. The width of all the bars and the gaps between the two bars are kept the same.
• Pie diagram is a circle that is subdivided or partitioned to show the proportion of various components of the data.
• Out of the given diagrams, only one-dimensional bar diagrams and pie diagrams are there in our scope.

General Guidelines

Title: Every diagram must be given a suitable title which should be small and self-explanatory.

Size: The size of the diagram should be appropriate, i.e., neither too small nor too big.

Paper used: Diagrams are generally prepared on blank paper.

Scale: Under one-dimensional diagrams, especially bar diagrams, the y-axis is more important from the point of view of the decision of scale because we represent magnitude along this axis.

Index: When two or more variables are presented and different types of line/shading patterns are used to distinguish, an index must be given to show their details.

Selection of proper type of diagram: It is very important to select the correct type of diagram to represent data effectively.

Advantages of Diagrammatic Presentation

(1) Diagrams are attractive and impressive:   The data presented in the form of diagrams can attract the attention of even a common man.

(2) Easy to remember:    (a)  Diagrams have a great memorising effect. (b)  The picture created in mind by the diagrams last much longer than those created by figures presented through the tabular forms.

(3) Diagrams save time : (a)  They present complex mass data in a simplified manner. (b)  The data presented in the form of diagrams can be understood by the user very quickly.

(4) Diagrams simplify data:   Diagrams are used to represent a huge mass of complex data in a simplified and intelligible form which is easy to understand.

(5) Diagrams are useful in making comparison:   It becomes easier to compare two sets of data visually by presenting them through diagrams.

(6) More informative :   Diagrams not only depict the characteristics of data but also bring out other hidden facts and relations which are not possible from the classified and tabulated data.

Types of One-Dimensional Diagram

One-dimensional diagram is a diagram in which only the length of the diagram is considered. It can be drawn in the form of a line or various types of bars.

The following are the types of one-dimensional diagram.

(1) Simple bar diagram

Simple bar diagram consists of a group of rectangular bars of equal width for each class or category of data.

(2) Multiple bar diagram

This diagram is used when we have to make a comparison between two or more variables like income and expenditure, import and export for different years, marks obtained in different subjects in different classes, etc.

(3) Subdivided bar diagram

This diagram is constructed by subdividing the bars in the ratio of various components.

(4) Percentage bar diagram

The subdivided bar diagram presented on a percentage basis is known as the percentage bar diagram.

(5) Broken-scale bar diagram

This diagram is used when the value of one observation is very high as compared to the other.

To gain space for the smaller bars of the series, the larger bars may be broken.

The value of each bar is written at the top of the bar.

(6) Deviation bar diagram

Deviation bars are used to represent net changes in the data like net profit, net loss, net exports, net imports, etc.

Meaning of Pie Diagram

A pie diagram is a circle that is divided into sections. The size of each section indicates the magnitude of each component as a part of the whole.

Steps involved in constructing pie diagram

• Convert the given values into percentage form and multiply it with 3.6’ to get the amount of angle for each item.
• Draw a circle and start the diagram at the 12 O‘clock position.
• Take the highest angle first with the protector (D) and mark the lower angles successively.
• Shade different angles differently to show distinction in each item.

Solved Questions

Q.1. Why is a diagrammatic presentation better than tabulation of data?

It makes the data more attractive as compared to tabulation and helps in visual comparison.

Q.2. Why do media persons prefer diagrammatic presentation of data?

Because it has an eye-catching effect and a long-lasting impact upon its readers/viewers.

Q.3. What will be the degree of an angle in the pie diagram if a family spends 50% of its income in food?

(50 ÷ 100) X 360 (Or) 50 x 3.6 = 180’

Q.4. Which bar diagram is used to show two or more characteristics of the data?

Multiple bar diagram

Q.5. Mention the sum of all the angles formed at the centre of a circle.

Q.6. Name a bar diagram where the height of all the bars is the same.

Percentage bar diagram

Q.7. Which diagram can be used to depict various components of a variable?

Subdivided bar diagram

Q.8. What is a multiple bar diagram?

A multiple bar diagram is one that shows more than one characteristic of data.

Q.9. Which bar diagram is used to represent the net changes in data?

Deviation bar diagram

Q.10. What is the other name of the subdivided bar Diagram?

Component bar diagram

The above-mentioned concept is for CBSE Class 11 Statistics for Economics – Diagrammatic Presentation of Data. For solutions and study materials, visit our website or download the app for more information and the best learning experience.

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