The use of qualitative over quantitative data research methods usually depends on the type and scope of the research project. There are also hybrid models that use both types of data. In this article, we will focus on when, why, and how to use qualitative research methods, and how to ensure that your qualitative research is of good quality.
Why Qualitative Research?
Quantitative research deals with numbers, graphs, and charts. This type of research is often used to test or verify hypotheses, assumptions, and theories. At its core, this type of research is used to establish facts about a subject or topic. Facts that can be proven or established by further quantitative research, such as experiments, survey questions, and observations, can be counted or numbered.
Qualitative research, on the other hand, is expressed in words rather than numbers. We use qualitative research to understand experiences, thoughts, or concepts. By using qualitative data in qualitative research, we can gather deep knowledge about topics that are often poorly understood. These insights can be gathered using tools, such as open-ended survey questions, behavioral observations described through words rather than numbers, and literature reviews that have explored similar theories and ideas.
Analysis of qualitative research
A qualitative research project is always dependent on quality analysis, whether it is a quantitative or qualitative study. There are different approaches to analyzing qualitative data, including thematic, discourse, and textual analyses. Let’s take a brief look at each one:
Thematic analysis:
This type of qualitative data analysis is when a set of text, like open-ended survey questions, or interview transcripts, is examined to determine common themes or overarching ideas and patterns.
Discourse analysis:
This is a technique used to study spoken and/or written language, specifically in relation to social context. The goal is to understand how certain language use is utilized in real-world situations.
Textual analysis:
This is a broader term for research methods that are used to describe and interpret, in order to understand, various texts. This can include literal, subtext, assumptions, and symbolism, as well as values that the text is revealing to the researcher.
Methods to Enhance Data Quality in Qualitative Research
The quality of research in all types of research, including the analysis of collected data, is optimized when everything is planned before the beginning of the research. By providing a comprehensive plan for the research, the results and quality of the research are fully guaranteed.
When planning your qualitative research, consider the following steps:
First, ensure that any collected qualitative data is reliable and clearly organized. Once you have collected your data, prepare and organize it for analysis.
As you review and analyze your data, you will use the data coding tools you developed during the research design phase. Of course, as you review the data, you may need to modify or fine-tune your data coding systems.
As you explore the data, assign different codes to what you find. It does not matter whether you have used discourse, thematic, or textual analysis techniques. Data analysis codes help you identify clear themes and patterns.
Data Quality Control in Qualitative Research
Quality control in all types of research, including the quality of the researcher and his research team, begins even before the research begins. For example, what is the credibility of the research team? Are there potential inherent biases based on the composition of the research team and research participants? If so, how are these orientations controlled? What do the participants know about the researcher? How are the assumptions of the researcher and his team or the participants considered?
When it comes to the actual study design, is the best technique being used? How are participants selected for the study? Are participants selected in a way that minimizes assumptions, biases, and other weaknesses that may be associated with the research? For example, if the participants are hard-selected, this can definitely affect the quality of the research. Additionally, choose carefully how participants are included. Different techniques cause different levels of information quality. For example, a face-to-face interview may be intimidating for a participant and may not be as honest as a phone or email response.
Data Quality Control Procedures
The actual process of data analysis should include considerations of how, when, and where to collect and analyze data. For example, were observed notes taken in front of the participants? Were there other people present in addition to the researchers and participants? Or does the research use audio or visual files to collect data? Were follow-up interviews conducted or did data collection include follow-up questions? All these aspects can improve or undermine the quality of the data and subsequent analysis, depending on how they are included in the research conclusions.
For example, how many data coders are working with the data? How has their training been and have they been analyzed as a team for stability and reliability? Did the participants provide feedback on the analysis of the data collected? When were themes and patterns identified? Before or after data collection and analysis? When participants are quoted, are these quotes identified within the data?
As you can see, the quality of your qualitative research can literally depend on hundreds of aspects and details. To design and implement a qualitative research project, the study design itself should consider as many quality indicators as possible to reduce or eliminate weaknesses.
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