Investigating the Factor Structure of the Love Attitude Scale (LAS) with Malaysian Samples
Bibliographic record
Abstract
Love is an emotion often experienced by individuals involved in romantic relationships. As a result, love has become an important determining factor in entering marriage among couples. This experience of love may vary across individuals and cultures. Hence, love style measurement serves as an indicator in choosing appropriate partner. We investigate the reliability and validity of the Love Attitude Scale (LAS) in this study. This scale has 24 items which measures six love styles namely Eros, Ludus, Storge, Pragma, Mania and Agape. Respondents were 200 individuals ranging from 20-25 years old (100 male and 100 females). Respondents involved in this study were individuals with a partner and have experiences in love. Data were analyzed using alpha Cronbach analysis and factor analysis. Results from factorial analysis showed that the Love Attitude Scale succeeded in extracting six factors as suggested with 67.56% variance. The eigen values ranged from 1.04 to 4.44. Results showed medium high alpha Cronbach value for five dimensions, specifically, ?=0.79 for Eros, ?=0.87 for Storge, ?=0.82 for Pragma, ?=0.72 for Mania, and ?=0.83 for Agape. However, Ludus showed the lowest alpha Cronbach value which was ?=0.39. Findings indicated that this scale is appropriate for use in the Malaysian context and the love styles dimension as suggested by LAS is appropriate for cross 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".