How do you collect and analyze qualitative data for assessing research impact?
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Before you start collecting qualitative data, you need to have a clear idea of what you want to achieve and what you want to know. Research impact goals are the intended changes or benefits that your research aims to produce for specific groups or sectors. Research impact questions are the specific inquiries that guide your data collection and analysis. For example, you may want to know how your research influenced the policy, practice, or behavior of a certain group, or how your research contributed to social, economic, or environmental outcomes. You can use frameworks such as the Research Impact Pathway Analysis (RIPA) or the Payback Framework to help you define your research impact goals and questions.
Qualitative data sources are the people, documents, or artifacts that provide relevant information about your research impact. Qualitative data methods are the techniques or procedures that you use to collect, record, or organize the data. Depending on your research impact goals and questions, you may choose different sources and methods. For example, you may use interviews, focus groups, or surveys to gather feedback from your research users or beneficiaries, or you may use document analysis, case studies, or storytelling to capture the evidence of your research impact. You can use tools such as NVivo, Dedoose, or Qualtrics to help you manage your qualitative data collection and analysis.
Qualitative data collection and analysis involve ethical and quality issues that you need to consider and address. Ethical issues include obtaining informed consent, protecting confidentiality and privacy, and avoiding harm or bias. Quality issues include ensuring validity, reliability, and credibility of your data and findings. You can use strategies such as triangulation, reflexivity, peer review, or member checking to enhance the ethical and quality standards of your qualitative data collection and analysis. You can also use guidelines such as the COREQ, SRQR, or RATS to help you report your qualitative data collection and analysis methods and results.
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One thing I find important to consider when engaging in qualitative data collection is, to consider the ethical considerations as dynamic, and having the capacity to shift a number of aspects within the research methodology. This can be especially the case when in a naturalistic environment. However, when observing, interviewing in this type of environment, the authenticity that is evident really comes to the fore as a marker of quality.
Qualitative data analysis is the process of identifying, interpreting, and presenting patterns, themes, or meanings from your data. Qualitative data analysis can be done using different approaches and techniques, depending on your research impact goals and questions, your data sources and methods, and your theoretical or conceptual framework. For example, you may use thematic analysis, content analysis, discourse analysis, or narrative analysis to explore the main topics, concepts, or stories from your data. You may also use coding, memoing, mapping, or visualization to organize, categorize, or display your data. You can use tools such as NVivo, Dedoose, or Atlas.ti to help you conduct your qualitative data analysis.
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Depending on your research goals and questions, as well as your data sources and methods, there are various methods you can utilize. Thematic analysis, content analysis, discourse analysis, and narrative analysis are just a few examples of techniques that can help you identify patterns, themes, and meanings within your data. To further enhance your analysis, consider using coding, memoing, mapping, or visualization techniques to organize and categorize your data in a meaningful way. Thankfully, there are user-friendly tools like NVivo, Dedoose, or Atlas.ti available to assist you throughout the qualitative data analysis process.
Qualitative data synthesis and interpretation are the steps of integrating, comparing, and explaining your qualitative data findings. Qualitative data synthesis and interpretation can help you answer your research impact questions, draw conclusions, and make recommendations. You can use different methods or models to synthesize and interpret your qualitative data findings, such as meta-synthesis, meta-ethnography, realist synthesis, or contribution analysis. You can also use tools such as NVivo, Dedoose, or MAXQDA to help you perform your qualitative data synthesis and interpretation.
Qualitative data impact story is the narrative that you create and share to demonstrate the value and significance of your research impact. Qualitative data impact story can help you engage and influence your stakeholders and audiences, and increase the visibility and recognition of your research. You can use different formats or platforms to communicate and disseminate your qualitative data impact story, such as reports, articles, blogs, podcasts, videos, or infographics. You can also use tools such as Canva, Piktochart, or Adobe Spark to help you design and produce your qualitative data impact story.
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