LinkedIn as a new selection method: Psychometric properties and assessment approach
Bibliographic record
Abstract
Abstract Various surveys suggest LinkedIn is used as a screening and selection tool by many hiring managers. Despite this widespread use, fairly little is known about whether LinkedIn meets established selection criteria, such as reliability, validity, and legality (i.e., no adverse impact). We examine the properties of LinkedIn‐based assessments in two studies. Study 1 shows that raters reach acceptable levels of consistency in their assessments of applicant skills, personality, and cognitive ability. Initial ratings also correlate with subsequent ratings done 1‐year later (i.e., demonstrating temporal stability), with slightly higher correlations when profile updates are taken into account. Initial LinkedIn‐based ratings correlate with self‐reports for more visible skills (leadership, communication, and planning) and personality traits (Extraversion), and for cognitive ability. LinkedIn‐based hiring recommendations are positively associated with indicators of career success. Potential adverse impact is also limited. Profiles that are longer, include a picture, and have more connections are rated more positively. Some of those features are valid cues to applicants’ characteristics (e.g., applicants high on Conscientiousness have longer profiles). In Study 2, we show that an itemized LinkedIn assessment is more effective than a global assessment. Implications of these findings for selection and future research 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 | 0.031 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".