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Record W2910656001 · doi:10.5539/jel.v8n1p206

Turkish Adaptation of Caring Climate Scale and Reviewing Psychometry Properties: Validation and Reliability Study

2019· article· en· W2910656001 on OpenAlexvenueno aff
Turan Çetinkaya, Ceren Mutluer

Bibliographic record

VenueJournal of Education and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaPsychologyScale (ratio)TurkishExploratory factor analysisConfirmatory factor analysisContext (archaeology)PsychometricsLikert scaleAthletesApplied psychologyClinical psychologySocial psychologyStatisticsPhysical therapyDevelopmental psychologyStructural equation modelingMedicineMathematicsGeographyCartography

Abstract

fetched live from OpenAlex

The research objective in this study is to make adaptation of Caring Climate Scale (CCS) that is developed by Newton et al. (in 2007) to Turkish and examine its psychometric properties. The scale comprises of one-dimension and 13 items. 468 students who have been studying and doing physical exercise as certified athletes in the following Schools of Physical Education and Sports of Ahi Evran University, Marmara University, Akdeniz University, Selcuk University and Ege University constitute the research group of the study. Internal consistency, factor analysis of substance, test-retest and criterion-related validity studies applied for determining the psychometric properties of scale. The Cronbach’s alpha internal consistency coefficient value of the scale was determined as 0.801 for total point. The obtained data from the exploratory and confirmatory factor analysis has corroborative features for the single-factor structure of scale. Besides, the scale provided high-test–test-retest scores in analyses conducted. In this context, it is evaluated that the scale is a valid and reliable measuring instrument for Turkish sample group.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.038
GPT teacher head0.334
Teacher spread0.296 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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