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Record W4387394421 · doi:10.5539/hes.v13n4p86

Development of a Grit Measurement Scale for Thai Dramatic Arts Students

2023· article· en· W4387394421 on OpenAlexvenueno aff
Tanapot Posamak, Satayu Songchan, Nateethorn Narkprom, Nattapon Yotha, Wuthikrai Pommarang

Bibliographic record

VenueHigher Education Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsGritPsychologyConfirmatory factor analysisGoodness of fitConstruct validityScale (ratio)StatisticsStructural equation modelingReliability (semiconductor)ValidityMathematicsSocial psychologyPsychometricsGeographyCartographyPhysicsPower (physics)

Abstract

fetched live from OpenAlex

The present study aimed to achieve two main objectives: 1) investigating the components and predictors of grit among Thai Dramatic Arts students, hereafter referred to as TDART students, in a Thai college; and 2) establishing and validating a grit scale specifically designed for TDART students. A total of three hundred and forty-five TDART students were selected using the Multi-stage Random Sampling method to participate in this study. The research instrument used was a grit measurement survey comprising 60 rating-scale items. Mean and standard deviation were employed for data analysis, and confirmatory factor analysis was used to assess construct validity. The findings revealed two distinct components of TDART students’ grit: passion and perseverance, forming a five-dimensional structure for the scale. The grit scale exhibited strong reliability with a value of 0.946, and the power of discrimination (rxy) ranged from 0.247 to 0.586. The results indicated acceptable construct validity for the grit scale, as evidenced by goodness of fit indices meeting criteria (Chi-Square=23.768, df=23, X2/df=1.033, p-value=0.417, CFI=1.000, TLI=1.000, RMSEA= 1.000, and SRMR=0.028).

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)0.001
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 score0.761
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.253
GPT teacher head0.460
Teacher spread0.207 · 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
Published2023
Admission routes1
Has abstractyes

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