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Record W3097010126 · doi:10.1080/23311886.2020.1844927

The multicollinearity between youth sport environment questionnaire and team assessment diagnostic measurement in sport settings

2020· article· en· W3097010126 on OpenAlexaff
Yuto Yasuda, David M. Paskevich

Bibliographic record

VenueCogent Social Sciences · 2020
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMulticollinearityPsychologyApplied psychologyTeam sportCohesion (chemistry)Mental modelSocial psychologyIndustrial and organizational psychologyAthletesRegression analysisComputer scienceMedicine

Abstract

fetched live from OpenAlex

With interdisciplinary effort, shared mental model from organizational psychology has been introduced in recent years. Even though the concept of shared mental model is established in sport psychology, it still has an operational problem. That is, different researchers have used different measures. The purpose of this research was to examine the multicollinearity between the Team Assessment Diagnostic Measurement (TADM) questionnaire, which measures shared mental model, and the Youth Sport Environment Questionnaire (YSEQ), which measures group cohesion. The participants were competitive youth soccer players. TADM and YSEQ were measured at the end of the season. Findings showed that the TADM was highly correlated with task cohesion (r = 0.81) even though VIF did not indicate multicollinearity. Therefore, TADM should be used with caution. Also, based on the definition of the shared mental model, Pathfinder or card sorting is recommended rather than using questionnaires.

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)0.029
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.076
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.330
Teacher spread0.270 · 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 designObservational
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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