A Comparison of Component Structures for the Australian Inventory of Family Strengths (AIFS) Between an Australian and Malaysian Sample
Bibliographic record
Abstract
Formal research on family strengths in the Southeast Asian region outside of Australia is scant. Specifically, no known family strengths measures have been developed or adapted for the purpose of measuring family strengths in Malaysia. In the present study, exploratory component analysis (Gorsuch, 1997) was used to compare the component structures of the Australian Inventory of Family Strengths (AIFS) scale for a rural population of Malay families from Malaysia (n = 200), and those from the original Australian sample (n = 605) as reported in the Australian Family Strengths Research Project (Geggie et al., 2000). Initial findings from principal component analysis with varimax (orthogonal) rotation indicated similar but not identical primary component structures for both samples. Upon further analysis, correlation results indicated that the factor components of the Australian sample did not replicate particularly well for the Malaysian sample, with only one out of four components demonstrating a significant level of invariance. For further research, the authors suggest a Malaysian version of the AIFS to be developed along the same lines as the Australian version of the instrument, i.e., by first using focus group data with Malaysian families to determine the specific constructs of strong families specific to the Malaysian cultural 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".