Gender‐Based Differential Prediction by Curriculum Samples for College Admissions

Niessen, A. Susan M.; Meijer, Rob R.; Tendeiro, Jorge N.

doi:10.1111/emip.12266

Cited by 7 publications

(5 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is plausible that the actual CV criteria demonstrate an unconscious gender bias towards males 37,38 . It is known that males find it easier to have abstracts accepted for presentation 39 or publication 40 .…”

Section: Discussionmentioning

confidence: 99%

“…It is plausible that the actual CV criteria demonstrate an unconscious gender bias towards males. 37,38 It is known that males find it easier to have abstracts accepted for presentation 39 or publication. 40 A substantial portion of CV points were awarded in this selection process for higher degrees, publication of research and research presentations.…”

Section: Curriculum Vitaementioning

confidence: 99%

See 1 more Smart Citation

Gender associations with selection into Australian Orthopaedic Surgical Training: 2007–2019

Incoll

Atkin²,

Frank

et al. 2021

ANZ Journal of Surgery

View full text Add to dashboard Cite

Background: Communities have better health outcomes when their clinicians reflect the diversity of the communities they serve. More than 50% of Australian medical school graduates are female, yet women represent less than 5% of Australian orthopaedic surgeons. Selection into orthopaedic surgical training in Australia is an annual, nation-wide process, based on curriculum vitae (CV), referee reports and performance in multiple mini-interviews (MMI). The influence of applicant gender on these selection scores was examined. Methods: The CV, referee reports and MMI scores used for selection for each year from 2007 to 2019 were analysed from the perspective of the applicant's gender. Results: Over the years of the study, male applicants had higher CV scores and referee report scores, which determined the gender proportions invited to interview. By contrast, the interview process and selection from interview did not demonstrate a gender association. Conclusion:We describe the impact of selection tools, utilized over the past 13 years, on the gender diversity of trainees commencing orthopaedic surgery training in Australia. Leaders in postgraduate training should examine commonly used selection procedures to identify and reduce the unconscious biases that may affect their performance and value.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Curriculum Vitaementioning

confidence: 99%

Gender associations with selection into Australian Orthopaedic Surgical Training: 2007–2019

Incoll

Atkin²,

Frank

et al. 2021

ANZ Journal of Surgery

View full text Add to dashboard Cite

show abstract

“…It describes cases where a grouping variable (e.g., gender) moderates the effect of a predictor variable (usually cognitive ability test scores) on the job-related outcome variable (e.g., Berry, 2015). These biases can be relatively easy diagnosed when the relations between few predictor variables and the criterion are assessed with regression analyses (Berry, 2015;Meade & Fetzer, 2009;Niessen, Meijer, & Tendeiro, 2019). When using ML modeling, on the other hand, the often large amount of predictor variables, complex interactions and the highly non-linear links to the criterion can make it incomparably more difficult to detect differential predictions.…”

Section: Challenge 3: Algorithmic Fairnessmentioning

confidence: 99%

“…relatively easily diagnosed when the relations between few predictor variables and the criterion are assessed with regression analyses (Berry, 2015;Meade & Fetzer, 2009;Niessen et al, 2019). When using ML modeling, on the other hand, the often large amount of predictor variables, complex interactions, and the highly nonlinear links to the criterion can make it incomparably more difficult to detect differential predictions.…”

Section: Challenge 3: Algorithmic Fairnessmentioning

confidence: 99%

Pitfalls of Machine Learning-Based Personnel Selection

Goretzko

Israel

2022

Journal of Personnel Psychology

View full text Add to dashboard Cite

Abstract. In recent years, machine learning (ML) modeling (often referred to as artificial intelligence) has become increasingly popular for personnel selection purposes. Numerous organizations use ML-based procedures for screening large candidate pools, while some companies try to automate the hiring process as far as possible. Since ML models can handle large sets of predictor variables and are therefore able to incorporate many different data sources (often more than common procedures can consider), they promise a higher predictive accuracy and objectivity in selecting the best candidate than traditional personal selection processes. However, there are some pitfalls and challenges that have to be taken into account when using ML for a sensitive issue as personnel selection. In this paper, we address these major challenges – namely the definition of a valid criterion, transparency regarding collected data and decision mechanisms, algorithmic fairness, changing data conditions, and adequate performance evaluation – and discuss some recommendations for implementing fair, transparent, and accurate ML-based selection algorithms.

show abstract

“…Data were used that were described and analyzed in Niessen, Meijer, and Tendeiro (2016, 2018a, 2018b. However, in these studies data were not analyzed with the aim of investigating the similarity between proctored and unproctored conditions and the use of a frequentist and Bayesian approach for predicting student performance.…”

Section: Datamentioning

confidence: 99%

Predicting Future Study Success: How A Bayesian Approach can be of Help to Analyze and Interpret Admission Test Scores

Tendeiro¹,

Niessen²,

Crișan³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Executive SummaryThe aim of this study was twofold: First, we investigated whether scores on an admission test lead to similar predictions in future study success when administered in a proctored-and an unproctored setting. Second, we explored how Bayesian modeling can be of help in interpreting admission-testing data. Results showed that the mode of administration of an admission test did not result in different models for predicting study success and that Bayesian modeling provide a very useful-and easy-to-interpret framework for predicting the probability of future study success. 3Arguably the most important aim of admission testing is the prediction of future academic success. Academic success is typically operationalized as GPA or study progress, but can also include leadership or citizenship (e.g., Stemler, 2012;Sternberg, 2010). In order to accept those students with the highest academic potential, students are admitted to college or graduate programs based on admission criteria such as scores on admission tests and other possible predictors such as high school performance (in the case of undergraduate admissions), undergraduate performance (in the case of graduate school admissions), biodata (such as life and work experience), personal statements, recommendations, and interviews (Clinedinst & Patel, 2018). Since access to higher education programs is an important determinant of later life outcomes, such as income, attitudes, and political behavior (Lemann, 1999, p. 6), it is important that admission procedures consist of fair and valid instruments and procedures.The widespread use of computers further allows for more varied forms of assessment, which makes admission testing even more difficult. Testing at a distance is now more common, although it does raise questions concerning the validity of the test results. Dishonest testing behavior (e.g., cheating) is more difficult to control in unproctored, online, tests. Furthermore, the security of test items is also potentially jeopardized, which may contribute to inflated test scores. Hence, it is crucial to ascertain that test takers who are assessed at a distance (i.e., unproctored) are not advantaged over test takers who are assessed in a proctored environment. In this study we investigate whether proctored and unproctored tests may lead to different test results, and to differences in prediction, which is of major importance in admission testing. If unproctored test-takers engage in cheating, we would expect that their academic performance is overpredicted, that is, they perform less well academically 4 than we would expect based on their admission test scores. We study differential prediction between unproctored and proctored test using real admission test data.Specifically, we compare scores across the two groups by means of the moderated multiple regression model proposed by Lautenschlager and Mendoza (1986), under both the frequentist and the Bayesian paradigm. Our goal is to investigate whether differential prediction of first year GPA exists betwe...

show abstract

Gender‐Based Differential Prediction by Curriculum Samples for College Admissions

Cited by 7 publications

References 65 publications

Gender associations with selection into Australian Orthopaedic Surgical Training: 2007–2019

Gender associations with selection into Australian Orthopaedic Surgical Training: 2007–2019

Pitfalls of Machine Learning-Based Personnel Selection

Predicting Future Study Success: How A Bayesian Approach can be of Help to Analyze and Interpret Admission Test Scores

Contact Info

Product

Resources

About