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Record W2943656829 · doi:10.1590/1982-4327e2911

Passion Scale: Psychometric Properties and Factorial Invariance via Exploratory Structural Equation Modeling (ESEM)

2019· article· en· W2943656829 on OpenAlexaff
Evandro Morais Peixoto, Tatiana de Cássia Nakano, Ricardo Almeida Castillo, Leonardo Pestillo de Oliveira, Marcos Alencar Abaíde Balbinotti

Bibliographic record

VenuePaidéia (Ribeirão Preto) · 2019
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsStructural equation modelingPsychologyScale (ratio)Measurement invarianceEquivalence (formal languages)PassionAthletesContext (archaeology)Social psychologyApplied psychologyStatisticsMathematicsConfirmatory factor analysisPhysical therapyGeography

Abstract

fetched live from OpenAlex

Abstract Passion is an important element among the psychological processes involved in the performance of any activity, including sports practice. Given the scarcity of nationally valid and reliable instruments, this study has the purpose of presenting the adaptation processes of the Passion Scale to the Brazilian context. A total of 789 Brazilian athletes (age:16.62±3.20; 58.4% men) participated in the study. To evaluate their psychometric properties, the scale dimensionality was estimated through the Hull method and Exploratory Structural Equation Modeling, and the accuracy by composite reliability. The factorial invariance model was estimated between men and women, and between participants of different competitive levels. Results showed the two-factor structure of the scale, according to the theoretical hypothesis, with desirable accuracy indicators. Equivalence of the measurement model was demonstrated when evaluating participants of different sexes and different competitive levels. Results suggest adequacy of the Brazilian version for the evaluation of this construct.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.073
GPT teacher head0.280
Teacher spread0.206 · 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 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

Citations33
Published2019
Admission routes1
Has abstractyes

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Same venuePaidéia (Ribeirão Preto)Same topicMotivation and Self-Concept in SportsFrench-language works237,207