![]() They offer the added benefit of supporting multiple viewing levels. Sankey diagrams allow you to show complex processes visually, with a focus on a single aspect or resource that you want to highlight. Today, Sankey diagrams are used for presenting data flows and data connections across various disciplines. Originally, Sankey diagrams were named after Irish Captain Matthew Henry Phineas Riall Sankey, who used this type of diagram in 1898 in a classic figure showing the energy efficiency of a steam engine. The Sankey Diagram is one such powerful technique to visualize the association of data elements. ![]() Video tutorial: Sankey Diagrams in ATLAS.ti Windowsĭata Visualization is a great way to simplify the complexity of understanding relationships among data. However, it is still fun to explore this data further as you do get meaningful results.Visualizing the Code-Document Table Sankey Diagram Scientific conclusions cannot be drawn from it. If you may wonder about the low frequencies in the above table or histogram, it is worth noting that this is just a small sample project used here for illustrative purposes. See more on memo writing in the book Qualitative Data Analysis with ATLAS.ti. When we begin to describe this in a memo for this research question, we move from analysis to interpretation. Reading the data behind the numbers will likely give you clues regarding which explanation might be more appropriate. Another explanation could be that life as a parent gets easier with more experience. To explain the results, we could, for example, apply self-consistency theory to explain the findings arguing that parents with two or more children feel compelled to report positive effects as otherwise, they would need to question their own decision of why to have more than one child. You can find more information about this in the book Qualitative Data Analysis with ATLAS.ti.Īs you can see from the Figure below, each response was coded with sociodemographic codes like gender or number of children and codes that describe other aspects like various positive and negative effects of parenting. As each document contains responses from various respondents, sociodemographic characteristics needed to be coded. If you look at the sample project, you will find two documents (D3 and D4) that contain comments from multiple people on a parenting blog and comments on an article published by the New York Time Magazine. ![]() RQ1: Do parents with one child differ from parents with two or more children regarding the positive and negative effects of parenting they report? When reading through the examples, think about how you can transfer this knowledge to investigate the data in your projects. We will look at a few research questions and how to find answers to them. I will use the Children & Happiness sample project that you can find on the ATLAS.ti website. I will now show how to use the co-occurrence operators using the Code Co-occurrence Table. The co-occurrence operator is also used when running the Code Co-occurrence Explorer or Code Co-occurrence Table. Using the WITHIN operator, you can ask, for instance, for all quotations coded with ‘topic X WITHIN ‘question 5’ or by ‘speaker y’. The same applies when working with pre-coded survey or focus group data where ATLAS.ti automatically codes all questions/speakers. The WITHIN operator comes in very handy in such instances. Or if you have coded longer sections in your data like biographical periods in a person’s life and then did some more fine-grained coding within these periods. Think of video data where it might be essential whether action A was already going on before action B started or vice versa. ![]() ![]() With other types of data, they are, however, quite helpful. In interviews, people often jump back and forth in time or between contexts, and therefore it usually does not make much sense to use the specific embedding or overlap operators. The more general co-occurrence operator is quite helpful when working with transcripts. ![]()
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