What are “quotations”, and why are they important for qualitative data analysis in ATLAS.ti? To begin condensing and making sense of all the rich information in your qualitative data, you can select each and every relevant or interesting bit of data – these are your quotations. Quotations thus form the most basic unit of analysis from which you can build your coding and analysis of the patterns and themes underlying your data.
In this best practice article, we will share some practical tips for querying your data in ATLAS.ti Web to find the answer(s) to your research question(s). In any piece of research, the most important thing that drives the whole research process is the research question. The research questions you pose serve as a crucial guide for deciding which qualitative methodology to utilize: what data to examine, how to collect it, and how to analyze it in order to provide an answer to your research question(s). Thus, once the data has been coded, you can query your quotations and codings to explore the relevant data that will help you answer your research question(s). ATLAS.ti is a flexible tool that can be adopted within just about any qualitative approach and facilitate your journey from conducting a literature review to analyzing your data to elaborate answers to your research question(s).
The quotation manager makes it easy for you to see and explore all of the quotations in your project. You can save stand-alone quotations, and you can associate codes to your quotations to organize and index your data. You can examine all your quotations, their associated codes, any comments you wrote, and you can query your data by applying a variety of flexible filters. You can find more advice on using filters in the quotation manager in our best practices article here.
A typical workflow may proceed as follows: a researcher notices there is some phenomenon in their field that lacks a coherent explanation or framework, so they pose a research question regarding this phenomenon of interest. The researcher then needs to decide which methodology is best suited for helping them answer this research question (e.g., should a deductiveor inductive approach be used, should data be collected from a specific case or a broader group of people experiencing the phenomenon, should data be analyzed to identify perceptions or processes, and so on). There are a great variety of qualitative methodologies, data, and analysis strategies, and we strongly encourage researchers to consider the range of approaches that may be helpful for them in their particular studies. On the other hand, we also understand the difficulties of navigating through all this information and making sense of which approaches are appropriate or not. For those interested in seeing an overview of different qualitative methodologies, you can find more information here. Once the research design has been laid out, the researcher then begins collecting and analyzing the data. This data can be imported to ATLAS.ti Web and coded to condense and synthesize the rich array of qualitative information. A code can be a word or short phrase that captures something in the data – a code is essentially a “tag” that you can attach to quotations to organize all the different bits of data. You can find more detailed information on coding data in ATLAS.ti Web here.
A very common dilemma qualitative researchers experience is figuring out how to make sense of all of the different segments of data and growing list of codes. After getting immersed in the data and extensively coding all of the different characteristics, concepts, patterns and so on that are present in the data, it is all too easy to feel one has lost their way along this winding path of qualitative analysis. If you ever find yourself in this situation, this is exactly when we recommend going back to your research question(s) and methodology – “take a step back” from your analysis and think about how your codings and data analysis are leading to an answer to your research question(s). With your data coded and research question(s) in mind, you can open the quotation manager and begin querying the data. Filter your coded data to contrast the different findings so that you can more easily identify the answer(s) to your research question(s).
When you create your account in ATLAS.ti Web, you can see the Demo Interview Project, which is an already-coded project that you can explore to try out the different features of ATLAS.ti Web. In this project, we interviewed three of the main people who are behind the creation of ATLAS.ti. Some of the research questions of interest were:
Once the data has been coded, we can “ask” our questions using the quotation manager and explore the data that can help us answer these questions. Thus, to examine the different perceived benefits that were mentioned by members of the top management team and members of the training team, we will apply some filter rules to compare and contrast the identified quotations:
Click on the “Save” button. Now the filter view is saved, and we can see this new filter view on the left-hand side of the quotation manager along with a nice bar chart visualization of the frequencies of these codes. The resulting quotations that match the filter rules appear on the right-hand side.
Now we can easily see all of the relevant data which will help us describe the CEO’s perceptions of the benefits to the Web version of ATLAS.ti. We can reflect on what was said and write our analytic notes in a memo. To continue answering our research question, we will repeat the above steps, but this time we will gather the data from members of the training team (i.e., we will select the document group “position: trainer & product specialist”). We can thus save another filter view to capture their perceptions.
You can save as many filter views as you want, so you can take advantage of the interactive quotation manager to filter and organize your developing insights. We now have two saved filter views, and we can easily switch between them to see what the CEO said and what the product specialists and trainers said.
Any combination of filter rules can be applied to query your data. For example, our second research question was “How are the characteristics of a good CAQDAS (computer-assisted qualitative data analysis software) related to an online CAQDAS?” To find the data that can help us answer this research question, we will create another filter view. This time, we want to see the data from all of our participants, but we want to filter according to the codes attached.
We can save the filter view and add our title and description. Now, we can see everything we found about characteristics of good CAQDAS, benefits of the Web version, and we can see where the two overlap (i.e., quotations that have both kinds of codes attached). You can also choose how you want the quotations sorted (e.g., should they appear in ascending/descending order by name, comment, document name, number of codes, creation date, or last update).
With this present article, we wish to provide practical advice on harnessing the quotation manager in ATLAS.ti Web to easily query the data, but our main objective is to provide some applied examples to illustrate how the quotation manager may be utilized to answer research question(s) of interest. All saved filter views will always appear in the quotation manager, so you can even continue adding new documents, coding them, and analyzing your data; if any new data fits your saved filter rules, they will also automatically appear in the corresponding filter view. The bar charts also make it easy to visualize how many quotations are attached to each code. You can download any of your quotation lists as Excel files so you can view them offline, as well. We hope ATLAS.ti Web serves you well in facilitating exploration of your queried data, documenting analytic insights in memos, and elaborating the answers to your research questions.