Unproctored Internet Testing in Personnel Selection: A Test of Cheating Deterrence Strategies
Bibliographic record
Abstract
Unproctored Internet testing (UIT) has become increasingly popular in the context of personnel recruitment and selection.Despite the widespread use of UIT, its fairness and overall impact on the hiring process is still called into question (Ryan & Ployhart, 2014).In consideration of the issues surrounding UIT, the purpose of this thesis was to examine job applicant cheating behaviour.Job candidates (n=110) were recruited through a series of job advertisements.Mild deception was used to cover up the true purpose of the research.Participants were administered a cognitive ability and cheating detection test as part of the regular recruitment process, and upon completion were invited to complete a follow-up survey.Contrary to popular practice, cheating deterrence strategies in the form of warning messages did not prevent candidate cheating.Furthermore, cheating was associated with overall lower cognitive ability.These results are discussed in relation to the Theory of Planned Behaviour, as well as the implications for future research.my Master's degree.This has been a long journey and he was there as my support system from day one.The countless discussions we had together over the past year, and the expert, structured feedback I received from him made the completion of my thesis possible.I have learned and grown under your guidance and the competencies I have developed during this process have been added to my skill set as invaluable learning.Another big thank you to Etelle Bourassa who also played a big role in my return to Carleton and encouraged me to complete my program.I would also like to thank my committee members, Dr. Campbell and Dr. Sears.Your valuable insight and feedback contributed to an even stronger Master's thesis.Thank you all for taking the time to be a part of this journey.I would also like to extend my appreciation and complete gratitude to Suzanne Simpson and Lorraine McKay (my current employers of over 5 years) for believing in my research topic and providing me with the support I needed to return to school and complete my degree.Thank you for seeing the value in the work I was trying to accomplish.Another person I would like to acknowledge is Shamarukh Chowdhury for her numerous reviews and strong guidance on my statistical approach -this was a key contribution to the overall quality of my work and I am forever thankful for the time and effort she put into helping me bring my vision into fruition.I also want to thank my good friend Colin
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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 | 0.040 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".