Qualitative Data Management

Introduction

Qualitative research can lead to results that are rich in content, but it is also a process often characterized by extensive amounts of data, and conclusions drawn from a narrative that is subjective in nature (Silverman, 2016). Levine's (1985) perspective on the need for a systematic approach to managing such data highlights an important aspect of how one should conduct qualitative research. This paper examines the issues can arise in qualitative research if a systematic approach to data management is not employed. It also provides a focus on the potential repercussions for data retrieval and analysis. Finally, it applies biblical principles will to emphasize the importance of systematic order, transparency, and diligence in data management from a godly perspective.

Data Overload and Retrieval Issues

Systematic data management is akin to the shepherd's approach in the Parable of the Lost Sheep, who attentively keeps count of his flock and immediately notices when even one sheep is missing. In the context of qualitative research, the "sheep" symbolizes the individual pieces of data - interviews, field notes, transcripts, and more - that the researcher gathers during the study (Bazeley, 2013).

Just as the shepherd uses a systematic approach to account for each sheep, a researcher must employ a systematic approach to manage each piece of data. This approach could involve coding, categorizing, and documenting data sources, timestamps, and context (Matta, 2019). Failure to manage data in this meticulous, organized manner can result in the figurative "lost sheep" of the research project - valuable pieces of data that become misplaced, forgotten, or buried under the weight of the growing dataset.

The inability to promptly locate and retrieve specific pieces of data can lead to significant obstacles in the research process. It hampers the analysis by obscuring potentially valuable insights, themes, or patterns that may only become visible upon careful scrutiny of all collected data (Patton, 2015). These obscured insights may be vital to answer the research question effectively and to form a comprehensive understanding of the studied phenomenon.

Data retrieval is the process of identifying and extracting relevant data from a database. It is a major component of data management, especially in the context of research. In qualitative research, data retrieval can involve pulling specific pieces of information from a large set of collected data, such as transcripts of interviews, field notes, documents, images, and audio or video recordings. There are many reasons, a researcher may need to do this. For instance, data often need to be examined more than once. Plus, after data is initially collected, and indexed or stored, researchers must retrieve it to perform their analysis. This may involve pulling specific data segments that relate to a particular theme or pattern, or retrieving all data related to a specific research question or hypothesis. Sometimes, data is re-analyzed in light of new insights or theories that emerge, which requires further retrieval and analysis. Or, if researchers need to confirm or double-check their findings, they will need to retrieve the original data for verification. This is an essential part of ensuring the reliability and validity of the research. Again, if other researchers or reviewers have questions about the study or its findings, the original researchers may need to retrieve specific data to address those inquiries. This is part of the transparency and reproducibility in research.

The absence of a systematic approach to data management can harm data retrieval. If data are not organized and documented efficiently, it can be challenging to locate the needed information when it is needed (Levine, 1985). That is why a systematic approach to data management is needed and should include planning for data retrieval in qualitative research. Planning would involve organizing, labeling, categorizing, and storing data in a way that makes it easy to find and extract the needed information later. This level of organization mirrors the biblical principle of stewardship: resources (or, in the case of research, data) have to be carefully managed to maximize their usefulness and value (Matthew 25:14-30).

From a more practical perspective, poor data management can lead to time wastedand time is valuable in all research. Researchers do not want to be sifting through data to find the piece they need, as they might be on a deadline and now they have less time for analysis and interpretation (Matta, 2019). The researcher needs to be mindful of the same problems when it comes to data overload. Data overload is basically the same as information overload: it is a situation in which the person finds himself dealing with more data than he knows what to do with. It is too much to process, in other words. It is also a major challenge in many fields, including research, due to the vast quantities of data that modern technologies enable one to collect and access (Bawden & Robinson, 2009).

In the context of qualitative research, data overload can occur when researchers collect large amounts of data (such as lengthy interview transcripts, extensive field notes, substantial document archives, etc.). With so much data, they find...…but also before the academic community and society at large. This biblical guidance underscores the importance of systematic data management as a means to ensure the honesty and integrity of research, as well as fostering trust and credibility within the wider research community.

Increased Risk of Ethical Breaches

Data management that is not systematic also increases the risk of ethical breaches. Confidentiality and privacy can be compromised if data are not properly stored, managed, and de-identified, particularly when dealing with sensitive information (Orb et al., 2001). This violates the biblical principle of respecting the dignity of individuals as shown in the golden rule, "Do unto others as you would have them do unto you" (Luke 6:31).

Confidentiality refers to the obligation of the researcher to protect the participant's identifiable information from unauthorized access or disclosure. Without a systematic method for data management, identifiable data may inadvertently be left exposed, which could lead to unauthorized access, misuse, or even data breaches (Matta, 2019). For example, if a researcher does not have a consistent and secure system for storing interview transcripts, there is a risk of these documents being accessed by people who are not authorized to view them, thereby violating participants' confidentiality.

Privacy, on the other hand, pertains to the right of participants to control the disclosure of their personal information. When managing sensitive data, it is crucial to ensure that the data are properly de-identified, meaning that any identifiers linking the data to specific individuals are removed or disguised (Bazeley, 2013). Without a systematic approach to this process, there's a risk that some data may not be properly de-identified, potentially leading to the identification of participants and violation of their privacy.

The golden rule principle in Luke 6:31 should reinforce the notion of research ethics. This principle extends to research practices, because it emphasizes the importance of respecting other peopleand that includes respecting peoples concerns about dignity, confidentiality, and privacy when they are research participants. Just as we would want our own personal information treated with respect and safeguarded, so too should we treat the personal information of research participants. Thus, a systematic approach to data management, which safeguards confidentiality and privacy, reflects the application of this biblical principle in the conduct of qualitative research.

Conclusion

The management of data in qualitative research is a critical aspect that should be approached systematically to ensure efficient data retrieval, reliable and valid analysis, reproducibility, transparency, and ethical integrity. As shown in this paper, the absence of a systematic approach can lead to numerous issues that could compromise the research quality…