by Colin on June 23, 2010
Qualrus is known as the “intelligent” qualitative analysis program because it learns your coding patterns and begins to offer active assistance with coding. It’s the only QDA program of it’s kind.
The program you see today is the result of rock-solid research by Edward Brent and Pawel Slusarz a few years back. Their seminal paper “Feeling the Beat: Intelligent Coding Advice from Metaknowledge in Qualitative Research” outlines their strategy for increasing the efficiency and quality of coding textual data by tapping into the great store of knowledge gathered by the coding process itself.
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by Colin on June 8, 2010
It’s easy to overlook the memo feature in Qualrus, but used correctly it can be a valuable way to keep your project on track. Memos can remind you of why you took an action, flag a section of your project to examine at a later date, or communicate your thoughts to fellow coders working on the same project.
Qualrus gives you the ability to attach memo fields to the project, each source, each segment, each code, each link, each view, and each script. Adding a memo to the project, source, segment or code are the most common actions by Qualrus users, so we’ll cover that functionally in this walk-through.
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by Colin on February 3, 2010
Data analysis software like Qualrus allows you to flexibly organize evolving observations by defining codes and links. The point is to classify and arrange your qualitative data in a way that allows you to thoroughly and accurately analyze, describe and communicate the information you’ve collected.
Primarily, structure is imposed on your data through the coding process. Qualrus’s code editor, along with its powerful Boolean searches and built-in QTools provide quick access to filtered and sorted information.
But there’s another way Qualrus assists with your data analysis that most people ignore: visualization.
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by Colin on January 2, 2010
When you’re working on a qualitative data analysis project with a team, it’s essential to know how to import components of one project into other projects. For example, you might split up larger projects between 2 or 3 different coders, then combine your results into one master project to generate reports.
Importing project elements also allows you to reuse work you’ve completed on previous projects. A coding scheme developed for a project involving the analysis of presidential inaugural addresses might turn out to be useful for another project classifying media election coverage.
Qualrus makes it easy to import components from other projects such as codes, scripts and link types, or completely merge two projects into one.
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by Colin on November 4, 2009
We recently completed our 5-part series on mastering qualitative data analysis using Qualrus.
If you missed a step, or simply want to go back and review, the entire series is posted right here on our blog:
PHASE 1: Prepare your data for analysis
PHASE 2: Get to know and love your data
PHASE 3: Let the Coding Begin!
PHASE 4: Refine your coding scheme
PHASE 5: Report the Results
Questions? Comments? Send a note to support@ideaworks.com
by Colin on November 2, 2009
(Part 5 of 5 from the series: 5 Steps to Mastering Qualitative Data Analysis)
By now you’ve put a lot of work into your project. After collecting your data, you prepared it for analysis, got acquainted with some general themes, then scrupulously marked and coded segments. In Phase 4, you took a step back and refined your coding scheme, checking your work for inconsistencies and redundancy.
At this point, your data are neatly organized and ready to be reported. As you write up your final document, it’s important to refer often to your data. It’s easy to get side-tracked while discussing a particularly interesting theme and overlook less remarkable results.
Qualrus can generate a number of reports designed to illuminate different aspects of your project. To ensure that you fairly and completely characterize your project data, keep these reports handy while writing your final manuscript.
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by Colin on October 13, 2009
(Part 4 of 5 from the series: 5 Steps to Mastering Qualitative Data Analysis)
So you’re done taking a first pass at coding. Nice! The goal of coding is to separate the content from the fluff, while marking your meaningful content with appropriate themes.
The next step is to review the work we’ve done and look for redundant codes, groups of similar codes and categories that need to be split up.
Fortunately, Qualrus puts everything we need in one place: the Code Editor.
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by Colin on October 6, 2009
(Part 3 of 5 from the series: 5 Steps to Mastering Qualitative Data Analysis)
Now that you know and love your data, it’s time to begin coding.
Coding is the central process of qualitative research, where you begin giving meaning to your unstructured information. Your goal is to accurately classify data with appropriate, consistent themes.
We do this by designating certain parts of our source as a segment, and assigning one or more codes to that segment. A segment can include any length of text, but each segment should represent a coherent thought (we generally create segments 2-6 sentences long).
When reading through a particular passage, ask yourself: “What is the fundamental meaning of this section? What themes or ideas are discussed?”
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by Colin on September 14, 2009
(Part 2 of 5 from the series: 5 Steps to Mastering Qualitative Data Analysis)
Once your data are collected, organized, formatted correctly and imported into Qualrus it’s time to explore.
In the left pane (Project overview) of the main Qualrus window, click the “+” sign next to “Sources”. This will reveal the list of every source associated with your project. Alternately, the drop-down menu immediately above the left pane displays this information as well.
Double-click a particular source to open it in the center pane. You can open multiple sources at once; each source shows up as a separate tab in the main window.
Now, open up all your sources and start reading. Your goal is to get a sense of the data as a whole.
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by Colin on September 1, 2009
As a designer in search of the perfect user experience, I’m used to employing quantitative measures to capture user patterns and preferences. Information about popular links, click-thru rates, average time spent on particular pages, traffic sources and browser specs (usually collected by Google Analytics) give me a sense of what our users are doing and how they do it.
Last week, I got a chance to journey beyond my computer screen and help lead a focus group testing a project in development, the Peer Advising System (PAS). After some brief instructions, our potential users were let loose to explore the application. And I got to watch.
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