Here’s a more concise recent post on how to manage qualitative data.
This post presents an overview of the unique challenges for managing a qualitative research database and includes 7 steps for aiding the creation of an orderly and coherent database. These steps will assist greatly in ensuring that your research study is successful, that analysis is credible, and data is not compromised.
Quantitative databases versus qualitative databases
Quantitative data is cleaned up and recorded before analysis takes place. However, with qualitative data cleanup and analysis proceeds in tandem. In small research projects the primary researcher often handles transcription and achieving this tandem process is less problematic.
In large-scale research, however, there is a division of labor involved which means data preparation is separated from analysis. Thus, developing a rigorous database management protocol is essential for two reasons. First, standardized transcripts prevent the production of incompatible data products. Second, it reduces the chance that analysis will be compromised or delayed. Creating a coherent, cohesive, and orderly database ensures high quality analysis is possible.
Creating Cohesion
Each transcript should not be treated as an independent word processing product but rather part of a larger collection of data stored in a standardized format. Whether working alone or in collaboration, an identical appearance and layout for all documents is very important.
For group projects standardization allows for problem-free delegation of transcription and for all researchers it reduces the time spent finding specific parts within the transcript. This frees the researcher to spend more attention on analysis.
7 Steps to Managing Qualitative Databases
These steps for managing qualitative databases can be applied to both manual and electronic analyses:
1) Keeping copies of important information.A data management system should also be backed up and backups updated as data preparation and analysis proceeds.
2) Arranging field notes or researcher commentary in a chronological, genre, cast-of-characters, event or activity, topical or quantitative data file schema.
3) Creating a system for labeling and storing interviews. This includes a unique name or case identifier for each file that communicates crucial information about the file to researchers.
4) Cataloging all documents and artifacts.
5) Providing for the safe storage of all materials.
6) Checking for missing data.
7) Developing a process for reading and reviewing text.
Following these steps will prevent research confusion at the stage of analysis. If, for instance, a database comprises text documents that are structured and organized differently then cross-comparison of data within transcripts becomes laborious, delaying the process of analysis. Confusion stemming from poor database management severely limits the amount of information that can be processed and remembered by researchers.
These seven steps help avoid research pandemonium by achieving three key goals: producing high-quality and accessible data, documenting what analyses have been carried out and the preservation of data and corresponding analyses after the study is complete.
Documentation of data activity is essential to maintaining data integrity and facilitating efficient write-ups during analysis. A competent system for tracking, processing and managing data is key to the successful and timely completion of a research study.
Source: McLellan, Eleanor, MacQueen, Kathleen M., and Neidig, Judith L. 2003. “Beyond the Qualitative Interview: Data Preparation and Transcription.” Field Methods 15(1): 63-84.