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Record W2170585828 · doi:10.1177/2158244014551526

Psychometric Properties of the Drive for Muscularity Attitudes Questionnaire Among Irish Men

2014· article· en· W2170585828 on OpenAlexaff
Travis A. Ryan, Todd G. Morrison

Bibliographic record

VenueSAGE Open · 2014
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIrishConfirmatory factor analysisPsychologyConstruct validityPsychometricsClinical psychologyConstruct (python library)Scale (ratio)Sample (material)Measure (data warehouse)Structural equation modelingApplied psychologyDevelopmental psychologySocial psychologyStatisticsComputer science

Abstract

fetched live from OpenAlex

The Drive for Muscularity Attitudes Questionnaire (DMAQ) was developed to measure men’s desire to attain an idealized muscular body. To date, the cross-cultural suitability of this measure has received limited attention. The current study addressed this omission by testing the psychometric properties of the DMAQ using an online sample of Irish men ( N = 327). Confirmatory factor analysis revealed that a unidimensional model adequately matched observed data (i.e., fit indices suggested acceptable model fit). Analyses also showed that the DMAQ yielded reliable and construct valid scores, suggesting that the scale holds promise as an indicant of the drive for muscularity among Irish men. Strengths and limitations associated with this study are discussed, such as advantages and disadvantages of Internet research. Directions for future research are given, including the need for more psychometric work.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.337
Teacher spread0.297 · 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".

Quick stats

Citations6
Published2014
Admission routes1
Has abstractyes

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