Methodology or “methodolatry”? An evaluation of focus groups and depth interviews
Bibliographic record
Abstract
Purpose The aim of this research was to consider whether focus groups have justifiably become a more frequently used qualitative market research technique because of a superior research outcome. Although focus groups have extrinsic advantages such as speed and cost, there is evidence that individual depth interviews have intrinsic advantages relating to the quality of the research outcome. Design/methodology/approach A parallel research study was undertaken examining a single business issue using both focus groups and individual interviews. Results of both processes were analysed for relevance to the business issue. Follow up individual interviews with participants of the focus groups were undertaken to assess the validity of the data collected, and to investigate the nature of the processes in the groups. Findings Group processes appear to have had considerable influence on the consensus view expressed in focus groups, which may not be representative of respondents' individual views. Both the groups and the interviews identified the principle issues relating to buyer motivations and processes, target markets and branding. The groups were unable to match the depth and detail generated by individual interviews and to uncover subtleties in attitudes. The interviews offered less breadth of data and contextual information. Practical implications Whilst groups may be less expensive and faster in data collection, individual interviews demonstrated a superior ability to inform marketing strategy by uncovering important underlying issues. Originality/value The findings indicate that groups do not justify their predominance as a market research method in preference to interviews on the grounds of quality of outcomes alone.
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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 | 0.475 | 0.447 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".