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Record W2968882090 · doi:10.1177/0033294119868800

Examining the Factor Structure and Psychometric Properties of the Chinese Version of the Life Orientation Test in Older Chinese Immigrants

2019· article· en· W2968882090 on OpenAlexafffundabout
Vivian Huang, Kitty Ching Lo, Alexandra Fiocco

Bibliographic record

VenuePsychological Reports · 2019
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of CanadaRyerson University
KeywordsPsychologyOptimismConfirmatory factor analysisPessimismDiscriminant validityExploratory factor analysisConvergent validityPsychometricsVariance (accounting)Test (biology)Developmental psychologyClinical psychologyInternal consistencySocial psychologyStructural equation modelingStatistics

Abstract

fetched live from OpenAlex

The current study examined the factor structure and psychometric properties of the Chinese version of the Revised Life Orientation Test (CLOT-R) in a sample of 342 community-dwelling older Chinese immigrants in Canada (mean age = 71.99, SD = 5.62; 58.5% female). Confirmatory factor analysis demonstrated that the CLOT-R yields a two-factor model with one item cross-loading on two latent constructs of optimism and pessimism. Analysis further revealed poor internal consistency and convergent validity. Evidence for discriminant and convergent validity was found between optimism and perceived stress, as well as optimism and quality of life. Compared with the factor structure reported in previous Chinese-speaking samples, the modified two-factor structure found in the current group of older Chinese immigrants could be attributed to the heterogeneity of the sample and possible configural variance across culture and age. Overall, the current findings suggest that the CLOT-R may not be a reliable and valid measure to assess dispositional optimism and pessimism among older Chinese immigrants. Theoretical implications and suggestions for further scale development and research is 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 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)0.000
metaresearch head score (Gemma)0.001
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 score0.015
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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

Citations9
Published2019
Admission routes3
Has abstractyes

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