Factor loadings are like a quality check for your survey items. High loadings (typically ≥ 0.5, ideally ≥ 0.7) mean your questions really capture the concept you’re studying. Low loadings suggest some items may be confusing, irrelevant, or simply not pulling their weight.
Here’s how to improve them.
1. Refine Your Survey Items
- Use clear and simple wording. Ambiguity lowers correlations with the intended construct.
- ❌ “I feel organizational support in a variety of contexts.”
- ✅ “My organization supports me in my daily work.”
- Stay focused. Each item should measure only one idea.
- ❌ “I am satisfied with my pay and career opportunities.”
- ✅ Split into two items: “I am satisfied with my pay.” and “I am satisfied with career opportunities.”
2. Remove Weak Items
- After running an Exploratory Factor Analysis (EFA) or CFA, look at loadings:
- Items <0.4 often should be dropped.
- Items 0.4–0.5 may be kept if theoretically important.
- Removing weak items can raise the average loading and improve the clarity of your factor.
3. Increase the Number of Related Items
- Add more items that measure the same construct in slightly different ways.
- Example: If “Work Engagement” has low loadings, instead of just “I feel engaged in my work,” add items like:
- “I am enthusiastic about my job.”
- “Time passes quickly when I am working.”
- More related items improve reliability and can strengthen factor loadings.
4. Ensure Good Sample Size
- Factor loadings can be unstable with small samples.
- A rule of thumb: at least 5–10 respondents per item, with a minimum of ~100.
- More data = more stable factor solutions.
5. Check for Cross-Loadings
- If an item loads on more than one factor, it may confuse your model.
- Reword or remove such items to make factors more distinct.
- Use oblique rotation (e.g., Oblimin) if factors are correlated—it often gives cleaner loadings.
6. Strengthen Construct Validity
- Use established, validated scales from previous studies. Items that have worked before tend to load well.
- If you create your own items, run a pilot test and refine before large-scale data collection.
7. Use Model Modification in CFA (Carefully)
- In AMOS, LISREL, or other SEM software, modification indices can show where correlations between error terms might improve fit.
- But ⚠️ be cautious—don’t just chase better numbers. Modifications should always have theoretical justification, not just statistical.
In short
To improve factor loadings:
- Write better items (clear, simple, focused).
- Drop weak performers after analysis.
- Add more high-quality items for each construct.
- Use enough data to stabilize results.
- Check validity through pilot testing and theory.
Good factor loadings = stronger measurement model = more trustworthy results in your research.
