The Adaptation and Validation of the New Indices of Religious Orientation Revised Scale
Bibliographic record
Abstract
Objective. The purpose of this study was to adapt and validate the New Indices of ReligiousOrientation Revised (NIROR) scale, which has been developed in different cultural settings, for astudy of Pakistani university students. The NIROR was developed by Francis, Fawcett, Robbins,and Stairs (2016), consisting of 27 items for Canadian-Christian respondents. It measures intrinsicreligious orientation, extrinsic religious orientation, and quest religious orientation.Method. In this study, we have culturally adapted the scale for Pakistani sample. The validityindices were ascertained on a sample of 300 participants, these included; undergraduate, graduate,and post-graduate students taken from different departments of four universities of Islamabad.Results. The EFA, using the common factor analysis method, resulted in the final structure of thescale into 18 items with four factors, the first factor’s reliability was ? = .87, the second factor’s? = .82, the third factor’s ? = .79 and the fourth factor’s ? =.75. EFA was followed by the CFA ona new sample (n=498) to confirm the factors’ structure. The CFA revealed a good model fit for thefour factors solution of this scale that is ?2 (125) = 291.34; ?2 /df = 2.33; root mean square errorof approximation (RMSEA) = .05; goodness-of-fit index (GFI) = .94; Tucker-Lewis index (TLI)= .93; confirmatory fit index (CFI) = .94; normed fit index (NFI) =.90.Conclusion. It is concluded that the adapted version with four factors is a reliable and validmeasure to be used for Muslim adults in the Pakistani context.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".