When comparing the two kinds of research, qualitative data is generally more flexible and can be more difficult to measure. This article will discuss the differences between quantitative and qualitative research and how you can use them in tandem to yield powerful results. In both forms, the methodology is based on themes. Explanatory sequential research gathers numerical data and then analyses it to answer qualitative questions. On the other hand, the exploratory sequential analysis seeks to understand the phenomenon first and then back up the results with quantitative data.
Qualitative research is flexible:
There are three primary steps in conducting qualitative research. These steps are the research question, design, and decision. The research question specifies the problem under study and the research purpose. In qualitative research, the process of data collection is not fixed. Researchers may change their methods depending on new data and insight. They must also be aware of employment law issues when conducting qualitative research. They should choose a flexible work arrangement based on the assignment guidelines.
First step:
The first step in qualitative research is collecting multiple data forms. This information can be in documents, interviews, focus groups, discussions, and case studies. The next step is to organize the data. The data collected through these methods is analyzed using codes to identify themes and trends. Many qualitative researchers conduct interviews with people or groups rather than focusing on quantitative data.
Qualitative data analysis is based on themes:
Thematic analysis can be used to analyze. In the qualitative tradition, a code serves as a shorthand for a complex piece of information. Qualitative researchers typically have a name for each code, a short description of its meaning, and examples of text that fits that code. The latter is particularly useful when coding over a long period. Themes may be expressed as multiple sub-themes.
Thematic analysis:
Thematic analysis is one of the most popular approaches to qualitative data analysis. It emphasizes patterns of meaning in a study and is useful for all kinds of research. Identifying themes is based on six stages: familiarization, code formulation, thematic review, and report formation. While articles are used widely in qualitative research, thematic analysis is a particularly flexible approach for qualitative research.
Qualitative data lacks depth:
Researchers who focus on qualitative research often argue that their results lack depth, while quantitative researchers struggle to represent the same complexity with fewer data. However, the distinction between the two methods is not always clear-cut, with some scholars suggesting that both types of research are essentially interpretive. In any case, both types’ goal is to understand human behaviour and experience better.
Quantitative research:
In quantitative research, correlational data determines the relationships between variables, whereas qualitative data seeks to identify cause and effect. However, qualitative data is often better at recalling the nuances of a process as it unfolds. Because qualitative information is more complex and detailed, it is often better suited to uncovering fine descriptive distinctions. Despite this limitation, qualitative research is still an invaluable tool.
Qualitative data can be difficult to measure:
While quantitative data is easy to collect, qualitative information is elusive. Unlike quantitative data, which is categorized by numbers and can be analyzed statistically, qualitative data has no numerical value. Instead, it’s classified by labels, attributes, and other identifiers. Nominal data are organized by tags and do not offer quantitative value. This article will cover a few methods for “structuring” qualitative data.
