Factor structures of the Korean version of the Need for Cognition Scale Short Form (K-NfC-S) among Korean older adults
Bibliographic record
Abstract
Objectives This study examined the factor structure of the short-form Korean version of the Need for Cognition Scale (K-NfC-S) among older adults in South Korea.Method Drawn from the 2020 Korean Media Panel Study, a total of 2,281 adults aged 65 years and older were analysed. We measured the need for cognition using the 15-item K-NfC-S. The sample was randomly assigned to distinguish between Sample 1 (n = 1,117) for exploratory factor analysis (EFA) and Sample 2 (n = 1,164) for confirmatory factor analysis (CFA). We conducted EFA and CFA using SPSS version 26.0 and AMOS 26.Results EFA results showed that the K-NfC-S demonstrated a two-factor structure of positively and negatively phrased items. However, CFA results revealed that both the one-factor model with correlated uniqueness among positively phrased items (TLI = 0.945, CFI = 0.972, RMSEA = 0.051) and the one-factor model with only positively phrased items (TLI = 0.924, CFI = 0.939, RMSEA = 0.072) exhibited good fit.Conclusion The findings suggest that method effects may influence the factor structure of the K-NfC-S among older adults. This study highlights the importance of using appropriate methodological approaches for measuring the need for cognition, with implications for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".