Types of Data

Qualitative data

Qualitative data is non-numerical and descriptive. It often comes from open-ended questions, interviews, or observations.
This type of data provides insight into attitudes, behaviours, and experiences.
Some key characteristics of qualitative data include depth and detail. It seeks to understand the ‘why’ and ‘how’ of behaviour and experiences.
Analysing qualitative data involves interpretation and categorization. Methods like content or thematic analysis may be used.
Common criticisms of qualitative data include subjectivity, risk of bias, and difficulty in generalising findings.

Quantitative data is expressed in numbers and can be quantified. It often arises from experiments, surveys with close-ended questions, or structured observations.
This data type can provide generalised results and statistically significant findings.
Key characteristics of quantitative data are objectivity and measurability. It looks for statistical relationships, differences, or similarities.
Methods for analysing quantitative data usually involve statistical procedures. These results are often easier to represent visually (like graphs or charts).
Critics of quantitative data argue it might lack context and depth, possibly oversimplifying complex psychological phenomena.

Primary data is collected by the researcher specifically for their study. It is raw, first-hand, and relevant to the research question.
This type of data can be either qualitative or quantitative and can offer control over the data collection process.
However, gathering primary data can be time-consuming and expensive.
Secondary data is data that was collected by someone else for a different purpose.
This data can offer a larger data set and save time and resources as the data is already collected.
The limitations include potential irrelevance to the research question, questionable reliability and validity, and lack of control over data collection.

Mixed-method data involves the use of both qualitative and quantitative data in the same study.
This method seeks to use the strengths of both data types to provide a fuller understanding of a research question.
It can help reduce the weaknesses and limitations associated with using only one data type.
However, interpreting mixed-method data can be complex and time-consuming, requiring a detailed understanding of both data types.