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Record W2011401817 · doi:10.1080/1091367x.2014.942453

Measurement Invariance of the Passion Scale Across Three Samples: An ESEM Approach

2014· article· en· W2011401817 on OpenAlexaff
Benjamin J. I. Schellenberg, Katie E. Gunnell, Amber D. Mosewich, Daniel S. Bailis

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2014
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of OttawaUniversity of Manitoba
Fundersnot available
KeywordsPassionAthletesPsychologyScale (ratio)Measurement invarianceStructural equation modelingConfirmatory factor analysisSocial psychologyRecreationStatisticsMathematicsGeographyEcologyPhysical therapyMedicineCartography

Abstract

fetched live from OpenAlex

Sport and exercise psychology researchers rely on the Passion Scale to assess levels of harmonious and obsessive passion for many different types of activities (Vallerand, 2010). However, this practice assumes that items from the Passion Scale are interpreted with the same meaning across all activity types. Using exploratory structural equation modeling (ESEM), we tested this assumption by examining the invariance of scores from the Passion Scale across groups of recreational athletes/exercisers (N = 562), competitive athletes (N = 438), and sports fans (N = 256). We found that the ESEM analysis fit the data better than the more common independent clusters confirmatory factor analysis (ICM-CFA) approach and yielded lower correlations between harmonious and obsessive passion factors. Using ESEM, we found evidence of configural, weak, and partial strong invariance across the three groups. Evidence of partial strong invariance provides tentative support for comparing levels of harmonious and obsessive passion across activities.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.327
Teacher spread0.264 · 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.

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

Citations24
Published2014
Admission routes1
Has abstractyes

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