Data and Analytics / Educational

Psychology: How to Analyze Qualitative Data

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It’s not the most obvious or typical , but psychology and data are proving to be a powerful example of data visualization. Psychology, like many other fields and industries, is embracing the advances in digital data and data visualization. Data analysis isn’t always a smooth process, especially when it comes to the data itself. Large data sets with countless variables and qualitative parameters aren’t easily analyzed because there are fewer numerical values. So how do you integrate psychological profiles into easily understood visuals?

If you don’t have numbers, what do you measure?

Psychology data can be difficult to analyze because of outside factors that aren’t easily pinpointed.  But chances are you have numbers in your data, it might not be visible at first but the values are there. Maybe it’s frequency, age, time durations, group size, gender, or more specifically reaction time, skin conductance, or agitation, there’s value in there. It’s the final equation or analytical structure that needs heavy scrutiny. With qualitative data sets, you’ll likely need a skilled data scientist (and a corresponding expert) to map and design a complex data trail. Calculating anything requires a strong balance of refined variables and data flow.

For example, there are scientists and clinicians trying to measure and predict behavior through a watch in subjects at risk of specific moods. The wristwatch collects physical data like exposure to sun and body temperature, as well as digital data from phone activity. Right now, mood forecasting isn’t precise, but it has seen fairly successful results.

The mood tracking wristwatch is incredibly complex, but it’s not a new concept. Much like how the watch tracks and compares individual activity, marketing teams use psychology to optimize experience, ads, and performance based on customer behavior. It is possible to map actions and translate them into calculative models, it’s just a matter of analyzing and mapping the data.

The data story and the visual

Every analysis ever done was looking for insight, proof, or a story, and psychology is no different. When organizing varied data, you’ll have to refine your bottom line. What parameters will you set and how will you collect you data? If the data exists, what does it measure? How many variables are there? Who will be part of the data? Maybe the bottom line is measuring global stress and after reaching out to sample groups around the world, the data shows the global population is more stressed in 2017 than ever. Now, this is a reasonable conclusion derived from the data even though there is no exact value. Note that the data samples do not measure why people feel this way, it measures how they feel. Asking about all stress factors would launch the analysis so far off course there would be no measurable end. But asking how anxious or stressed they feel reveals quantifiable data.

An in-depth analysis is easily overwhelmed by too many variables, but this is where data visualization can help fill the gap. Organization is part of the process that can uncover any number of foundational insights that can help bolster the final data story. Like, what do you need to create a comprehensive database of serial killers that also cross references victim profiles? A keen eye for psychological detail and resources. When Sasha Reid began creating her database, she was intent on linking cold cases that could indicate a serial killer. She wants her database to evolve into an aggregated tool that can be used to identify potential serial murders that are overlooked and profile the suspected killers. Reid also cross-references other databases to consolidate data analysis, but cannot rely on just one. Even though human development doesn’t follow an exclusive formula, data analysis has helped to capture notorious killers like Ted Bundy, making the analytical labor worth it.

The mental health narrative

It can be a collection of charts and graphs or a database of killers with over 600 variables of psychological and developmental traits, in any case data visualization is the key. You might wonder why anyone would go through so much to assign value to unpredictable data that doesn’t follow the rules of easy integration. Data, especially digital data, is an undeniable part of our lives. Psychology is just another field that’s seeing immense change and potential with data visualization. In a day and age when our favorite apps track and affect our behavior and our personal digital lives are yet another realm of psychology to be explored, data analysis is a must.


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