How to Manage Qualitative Data: A Step-by-Step Guide

Definition of Qualitative Data: Data that approximates or characterizes but does not measure the attributes, characteristics, properties, etc., of a thing or phenomenon.
Steps for managing qualitative data
Data management is a challenging, integral, and vital part of qualitative research and is crucial to ensure successful studies. Being able to organize your data, i.e., converting your raw data to a final concise report, is an essential skill in scientific academic research. Major research projects can easily generate millions of words. Fortunately, recent advances in computer technology and software have made it possible to manage these mountains of words more efficiently.
Hence, in this article, we detail several steps that will aid in creating a well-ordered and coherent database. These steps will help to ensure that your research is successful and that the analysis is credible. It will also ensure that your data are not compromised.
Steps for managing qualitative data are as follows:
1. ACCURACY: Check if your data are of sufficient quality and accuracy before conducting a major analysis.
2. MAINTAIN COPIES: Prepare backups of the data management system. These backups should be updated as data preparation and analysis proceeds.
3. ARRANGEMENT: Field notes or researcher commentary should be arranged in a chronological, genre, cast-of-characters, event or activity, and topical or quantitative data file schema.
4. ORGANIZATION: Combine related themes into major categories. Label these categories and create file (or Word document) for each major category.
5. LABELING: Create a system for labeling and storing interviews. This can be conducted using a unique name or case identifier for each file. These should reveal crucial information about the file to researchers.
6. CATALOGUING: Catalogue all documents and artifacts.
7. SAFE STORAGE: All materials should be safely stored for future reference when writing or doing research paper editing.
8. MISSING DATA: Check for missing data.
9. REVIEWING TEXT: Developing a method for reading and reviewing text. Quality control procedures should be established.
10. KEEP TRACK OF SOURCES: Ensure that the source of all the data can be identified, such as by individual, site, and date. Develop a data tracking system.
11. LOOK FOR TRENDS: While reading the text, look for key ideas and mark emerging themes. Use abbreviations or symbols (codes) to label key themes, ideas, concepts, etc.
12. TRANSCRIPTION/TRANSLATION: Establish and document transcription/translation procedures.
Note: These steps can be applied to both electronic and manual databases.
Another aspect of managing qualitative data is file naming. You should consider including the following elements in the file-naming approach: Participant ID number, Site of data collection, Date of data collection, data collection method, etc.
Managing your qualitative data using these steps will prevent any confusion about data during analysis. For example, for a database comprising documents that are structured and organized differently, cross-comparison of data within transcripts becomes difficult, further delaying analysis. Confusion due to poor database management severely limits the amount of information that can be processed and remembered by researchers.
These steps will help in producing high-quality and accessible data, documenting what analyses have been performed and preserving data and corresponding analyses after the study has been completed. Also, writing and research paper editing becomes easier.