Conducting Grounded Theory Studies in Times of Pandemic: Challenges, Benefits, and Practical Strategies
Bibliographic record
Abstract
The purpose of this chapter is to provide researchers with knowledge of the challenges and benefits of moving to virtual platforms to conduct grounded theory studies during the Covid-19 pandemic era and beyond. An overview of grounded theory will be discussed highlighting interviews as the backbone of data collection for this methodology and the need to create a realistic research plan to implement the many steps the research process and document the challenges of this process during pandemic. Implementing qualitative research, including grounded theory studies in times of pandemic has not been an easy task, therefore, leading to a change in the research environment and operationalization of the well-established methods. To this regard, (qualitative) researchers were forced to adapt to virtual methods while trying to preserve the high quality and methodological rigorous of their studies. Thus, before embarking in a grounded theory study, it is important to examine the pros and cons of using online strategies to be able to make decisions related to participants’ recruitment, data collection through interviews and field notes. This chapter also provides practical strategies for scholars to implement grounded theory studies by using virtual platforms and describe the authors personal experiences in developing and supervising grounded theory studies by highlighting the challenges and benefits to both study’s participants and researchers. As the pandemic endures, it is important that researchers are aware of alternative methods for operationalizing their studies and ways of overcoming the challenges of doing recruitment and data collection through virtual means. The authors will conclude by recommending that rather than become worried about the quality of data obtained from an online interview, researchers reflect on the impact that it may have had on capturing the essence of the processes (phenomena) being studied and highlight the need of including the challenges and limitations of the study in the final report. Keywords: qualitative studies; grounded theory; data collection; recruitment; virtual platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.434 | 0.354 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.020 | 0.047 |
| Scholarly communication | 0.045 | 0.059 |
| Open science | 0.010 | 0.031 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".