An Exploratory Study Investigating the Purpose of Unstructured Interview Questions
Bibliographic record
Abstract
Interviews are central to selection. The literature recommends that practitioners rely on structured interviews as opposed to unstructured interviews (Schmidt & Hunter, 1998), however practitioners prefer unstructured methods (Dipboye, 1994) and little research directly compares the effectiveness of individual structured elements to their unstructured counterparts. Given that different structured question types have been studied in the literature (Taylor & Small, 2002), but that practitioners often prefer unstructured methods, the present research explores why interviewers use unstructured questions. We use signaling theory as an organization framework, and conduct an exploratory, qualitative study using Mechanical Turk. Results revealed the following primary purposes of using unstructured interview questions: capturing the candidates’ nature, collecting supplemental information, circumventing the candidates’ façade, and comforting the candidate. These results provide the first steps required to understanding the role of unstructured interview questions in selection and provide industry with some interesting insights about how interview questions are being used for selection.
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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 | 0.041 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".