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Record W2120527403 · doi:10.1177/1356336x14534363

Does relative age influence motor test performance of fourth grade pupils?

2014· article· en· W2120527403 on OpenAlexaff
Nick Wattie, Maike Tietjens, Jörg Schorer, Marie-Christine Ghanbari, Bernd Strauß, Ilka Seidel, Joseph Baker

Bibliographic record

VenueEuropean Physical Education Review · 2014
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsYork University
FundersMinistry of Culture and Sport
KeywordsSprintQuartileStatistical significancePsychologyPhysical educationJumpingTest (biology)Analysis of varianceMotor skillMulti-stage fitness testBalance testBalance (ability)Long jumpDemographyPhysical fitnessPhysical therapyJumpMedicineDevelopmental psychologyMathematicsConfidence intervalStatisticsMathematics education

Abstract

fetched live from OpenAlex

The aim of the current study was to explore relative age’s influence on physical and motor tests among fourth grade children (9 to 10 years) from Germany. Data from 1218 children (49% female) who had performed the German Motor Ability Test (Bös et al., 2009) were analysed. The test battery, which was comprised of physical and motor tests, included 20 m sprint, balance backwards, jumping sideways, stand and reach, push-ups, sit-ups, standing broad jump, and six-minute run. Analyses of variance only revealed statistically significant effects for height, weight, and 20 m sprint time ( p < .01) among boys, with relatively older boys performing better than relatively younger boys. For the girls, the only significant difference between quartiles was for height ( p < .01), with the oldest quartiles being taller than the younger quartiles. These results may have implications for statistical vs. practical significance, sampling, and how youth are evaluated in physical education classes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.014
GPT teacher head0.294
Teacher spread0.280 · 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 teacher head, not a consensus.

Study designOther design
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

Citations13
Published2014
Admission routes1
Has abstractyes

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