Validity and Reliability of Survey Items in Employer Perspective Construct on the Quality of ECCE: Rasch Measurement Model Analysis
Bibliographic record
Abstract
This paper examines and verifies the reliability of a survey instrument on the long term impact of early childhood and childcare education (ECCE) toward human capital development. The survey consists of separate questionnaires based on four perspectives; Individual Success, Peers, Parents and Employer perspective. This study highlights the reliability of item constructs from the employer perspective questionnaire distributed in the pilot study. This instrument was developed based on 56 items, and was further categorised into three sub-constructs; 1) individual character, 2) soft skills; and 3) good citizenship. Rasch Measurement Model analysis supported by Winsteps software version 3.73 was utilised to examine reliability of item and person, polarity of item and suitability of item. Findings from analysis of reliability of item indicated that Individual Character subconstruct showed a good level of reliability, whilst Soft Skills and Good Citizenship subconstructs showed reliability below par. Further analysis on polarity of item indicated all items scored positive values to measure the construct. While analysis on item fit revealed that a total of 6 items from the three subconstructs were discarded as they did not meet the criteria specified in the Rasch Model.
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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 | 0.052 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".