Data analysis is broad: what to use and how to use in training field
The use of quantitative and qualitative data.
Introduction:
When we take a step back and attempt to simplify data analysis, we can quickly see it boils down to two things: qualitative and quantitative data.
These two types of data are quite different, yet, they make up all of the data that will ever be analyzed.
Before diving into data analytics, it’s important to understand the key differences between qualitative and quantitative data.
Content:
What is the difference between quantitative and qualitative data?
Qualitative data is non-statistical and is typically unstructured or semi-structured in nature. This data isn’t necessarily measured using hard numbers used to develop graphs and charts. Instead, it is categorized based on properties, attributes, labels, and other identifiers.
Qualitative data can be used to ask the question “why.” It is investigative and is often open-ended until further research is conducted. Generating this data from qualitative research is used for theorizations, interpretations, developing hypotheses, and initial understandings. In training courses, qualitative data could be used to understand a theme, a social condition, a perception or for evaluate the learning process of the group of participants.
Qualitative data can be generated through:
- Texts and documents
- Audio and video recordings
- Images and symbols
- Interview transcripts and focus groups
- Observations and notes
It is very useful for collecting the insides of training or to prepare training by collecting enough information and insights on a specific topic or context. The qualitative data are very important when we would like to have a clear picture of a working framework.
Contrary to qualitative data, quantitative data is statistical and is typically structured in nature – meaning it is more rigid and defined. This type of data is measured using numbers and values, which makes it a more suitable candidate for data analysis.
Whereas qualitative is open for exploration, quantitative data is much more concise and close-ended. It can be used to ask the questions “how much” or “how many,” followed by conclusive information.
Quantitative data can be generated through:
- Tests
- Experiments
- Surveys
- Market reports
- Metrics
In training, we use a lot of quantitative data to measure the level of satisfaction in a group, to monitoring needs and expectations regarding a specific topic, etc.
Exercises:
How to apply it in everyday work]
Try to identify for each category which data you would collect during:
- preparatory work
- training activity
that could be useful for your better understanding of the topic and of the participants’ context
| Qualitative Data | Quantitative Data |
Reflection Questions:
- Do I really care about quantitative data?
- When I need to understand a new framework, how do I prepare my investigation (based on which data)?
- How do I feel prepared for collecting and analyzing data?
