Mindset XR Module 18: Analysis and interpretation

There are scales rating questionnaires that can be used to collect numerical information. Data collected in this way can be continuous, meaning they can offer an absolute quantification of an aspect.

Example:

Think about scales to measure for instance, depressive symptom severity. They offer a value and is a numerical value within a range, from zero to fifty for example. If we take measurement of this type, it will allow us compare individual scores, and estimate difference in depression severity between individuals.

Some measures can also have what's called normative values, or standard score scores. These allow us to compare a score with general population norms. This is often the case for measures of cognition or thinking skills.

Using continuous and validated measures is often preferred in research trials as this can allow quantification of change. For instance, before and after an intervention, and comparison between the intervention and control groups.

Another type of data that is often used in research is categorical data. This is information that help us put individuals in different categories based on whether they meet a certain criteria. In research, this information is used to help to describe participants  or to form groups to compare.

This is the case for, for example, having a diagnosis of PTSD or not having it. Or for some personal characteristics, such as gender or ethnicity.

Another example:

Offering the same intervention for anxiety to males and females and compare them on their anxiety reduction after therapy.

Qualitative information which is made up of people's opinions, discussions, and verbal feedback interviews.

These are often, captured by recordings of people views and performing specific analysis on these. The information gathered using these methods tends to be richer, but difficult to quantify and generalise. Its interpretation is also more likely to suffer from some form of bias.

Descriptive statistics are used to summarise and describe features in dataset. This approach can give an idea of the central tendency of a distribution, and provide information such as the mean, the median, and the mode. These can help to understand average behavior of users and of dispersion. So, indexes like range, variants, and standard deviation, are often used to understand the variability in user experience and distribution like: frequency distribution, histograms, and bar charts, to visualize data pattern.

Visualizing and exploring the data and the exploratory data analysis can be useful to uncover underlying patterns, anomalies, and relationship. Visual techniques include scatter plots, box plots, heat maps, and cluster data. These are helpful to visualise the trends.

Type one errors are errors that are rejecting the null hypothesis when it is actually true. This often happens if the data is collected from a biased sample. So you're not collecting data from a representative sample of your population, and you make inferences and generalise from those.

Type two error is the opposite, when you're accepting the null hypothesis when it's actually false. These often happen when you're not recruiting enough participants or in line with your power calculation.

Identify themes and patterns in the qualitative data using interview transcript or open-ended survey responses.

Systematically coding and categorising textual information to quantify the presence of certain words, themes, or concepts.

Developing theory based on the data collected and is often used in small exploratory studies.

It's a useful method in the development phase of a product, or  when refining it. It tends to capture a rich amount of information, but is often limited if you want to assess the efficacy of an intervention.

This involves observing users as they interact with the application and identify issues.

This is the method where people are asked to compare two versions of an XR application and determine which one performed better in terms of user engagement and satisfaction.

Where experts in the field evaluate the application based on established usability principles.

It's a useful method in the development phase of a product, or  when refining it. It tends to capture a rich amount of information, but is often limited if you want to assess the efficacy of an intervention.

This is a technology that allows to monitor where and how a user looks within the VR environment, and it can help us to understand their attention and focus.

Evaluating heart rate and skin response can help to assess level of engagement, stress, excitement.

These can also support the evaluation of emotional states.

Lately, developments have also allowed to use electroencephalogram (EEG), which is a method for recording cortical brain activity alongside VR.