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Record W4391876290 · doi:10.1080/15305058.2024.2318424

A meta-analysis of the relationship between Wonderlic test scores and school success

2024· article· en· W4391876290 on OpenAlexaff
Chet Robie, Sabah Rasheed, Stephen D. Risavy, Piers Steel

Bibliographic record

VenueInternational Journal of Testing · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsUniversity of CalgaryWilfrid Laurier University
Fundersnot available
KeywordsPsychologyTest (biology)Clinical psychologyMeta-analysisMedicineInternal medicine

Abstract

fetched live from OpenAlex

This meta-analysis examined the validity of an alternative to traditional assessments called the Wonderlic which is a brief measure of general mental ability. Our results showed significant, positive correlations between Wonderlic scores and academic performance in general ( r̅ = .26), between Wonderlic scores and undergraduate GPA in particular ( r̅ = .27, ρ¯ = .33), and between Wonderlic scores and retention ( r̅ =.09, ρ¯ = .12). We also identified several significant moderators of the relationship between Wonderlic scores and relevant outcomes (e.g., test publisher reported coefficients were larger than those reported by other sources). Subgroup differences in test scores were in the same range as other post-secondary admissions assessments (e.g., ACT and SAT scores). Overall, the Wonderlic has similar levels of subgroup differences and is less strongly related to GPA than traditional assessments but still retains useful levels of predictiveness and is a shorter, less expensive assessment that requires less preparation than the ACT or SAT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.034
Bibliometrics0.0050.005
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.825
GPT teacher head0.549
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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