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Record W4200285924 · doi:10.1027/1015-5759/a000689

Measurement Invariance of Two Different Short Forms of Social Interaction Anxiety Scale (SIAS) and Social Phobia Scale (SPS) in Chinese and US Samples

2021· article· en· W4200285924 on OpenAlexaff
Jiayi Zhong, Junsheng Liu, Gangmin Xu, Hong Zheng, Julie C. Bowker, Robert J. Coplan

Bibliographic record

VenueEuropean Journal of Psychological Assessment · 2021
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyMeasurement invarianceScale (ratio)Social anxietyAnxietyScale invarianceScalar (mathematics)Confirmatory factor analysisClinical psychologyDevelopmental psychologySocial psychologyStructural equation modelingStatisticsMathematicsPsychiatryPhysics

Abstract

fetched live from OpenAlex

Abstract. The goal of this study was to examine the measurement invariance of two different commonly used short forms of the Social Interaction Anxiety Scale (SIAS) and the Social Phobia Scale (SPS) across Chinese and US samples. Participants were 850 (52% females) Chinese and 399 (57% females) US undergraduate students. A two-factor model was identified as the best fitting baseline model for both short-forms of the SIAS and SPS. Full scalar invariance was established for the Peters short form, whereas the Fergus short form only achieved partial scalar invariance. Results of structured means analysis indicated that Chinese participants scored higher than US participants in social anxiety. Some cultural implications for the use of these two sets of short forms are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.395
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2021
Admission routes1
Has abstractyes

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Same venueEuropean Journal of Psychological AssessmentSame topicAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesFrench-language works237,207