What are interviews for? A qualitative study of employment interview goals and design
Bibliographic record
Abstract
Abstract The employment interview is among the most versatile of staffing tools. Yet, the interview is rarely studied as a multipurpose tool. If the interview is used to serve multiple goals, then the interview can be effective (i.e., valid), and effectively designed, in multiple ways. The current study uses qualitative methodology to develop an inductive theory of interview goals and design based on conversational interviews with 29 experienced professional interviewers. Transcript data were analyzed with template analysis grounded in a postpositive epistemology and objectivist ontology. Results suggested that the interview is primarily used to serve three broad goals: performing a targeted assessment , making a positive impression , and informing the applicant . Interviewers reported a variety of strategies for adapting the interview to achieve and balance these goals. In short, findings suggest that the interview is used in multiple ways that have received very little research attention. These findings imply that the concept of interview validity should be expanded to include multiple interviewing goals, and that interview design should be understood as a complex function of these goals. Further implications for the research, theory, and practice of employment interviews are discussed.
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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 | 0.099 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".