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Record W1876690473 · doi:10.1177/0734282915599460

A Cross-National Validation of the Academic Expectations Stress Inventory With Chinese and Korean High School Students

2015· article· en· W1876690473 on OpenAlexaff
Xiaozhou Zhang, Virginia M. C. Tze, Erin E. Buhr, Robert M. Klassen, Lia M. Daniels

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2015
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyConfirmatory factor analysisMeasurement invarianceAmotivationCynicismClinical psychologyMainland ChinaConvergent validityDevelopmental psychologyConstruct validityPsychometricsStructural equation modelingSocial psychologyChinaStatistics

Abstract

fetched live from OpenAlex

The current study provided evidence for the factor structure of the Academic Expectation Stress Inventory (AESI) in a sample of 213 Mainland Chinese and 184 South Korean high school students. We examined cross-national invariance of the AESI using multiple-group confirmatory factor analysis across two Asian cultural samples. Results suggested a unidimension rather than two-factor structure (self and teachers/parents) in both settings. Results also showed evidence of reliability, convergent validity (significant correlations with depression, emotional exhaustion, and cynicism), and divergent validity (nonsignificant correlations with amotivation and efficacy reduction). ANCOVA results indicated that perceived overall academic expectation stress was significantly higher among Mainland Chinese students than for their South Korean counterparts. Furthermore, there were no differences between males and females for the Korean adolescents while females reported higher expectation scores than males in the Chinese adolescent sample.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.440

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 head0.037
GPT teacher head0.443
Teacher spread0.406 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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