Factor loading is like a quality check for your survey items. A high loading (usually ≥ 0.5, ideally ≥ 0.7) means your question really captures the concept you are studying. A low loading indicates that some items may be confusing, irrelevant, or not of interest.

Here’s how to fix it.

1. Perfect Your Survey Items

• Use clear and simple words. Ambiguity reduces correlation with the intended construct.
  • ❌ “I felt the support of the organization in a variety of contexts.”
  • ✅ “My organization supports me in my daily work.”
• Stay focus. Every item must be measured just one idea.
  • ❌ “I am satisfied with my salary and career opportunities.”
  • ✅ Split into two items: “I am satisfied with my salary.” And “I am satisfied with the career opportunities.”

2. Remove Weak Items

• After running a Exploratory Factor Analysis (EFA) or CFAview loading:
  • Items <0.4 often have to be discarded.
  • Items 0.4–0.5 may be retained if they are theoretically important.
• Removing weak items can increase the average load and increase the clarity of your factors.

3. Increase the Number of Related Items

• Add more items that measure the same construct in slightly different ways.
• Example: If “Work Engagement” has a low loading, not only “I feel involved in my work,” add items like:
  • “I am enthusiastic about my work.”
  • “Time flies when I’m working.”
• More related items increase reliability and can strengthen factor loadings.

4. Ensure a Good Sample Size

• Factor loadings can be unstable with small samples.
• The rule of thumb: at least 5–10 respondents per itemwith a minimum of ~100.
• More data = more stable factor solution.

5. Check Cross Loading

• If an item loads on more than one factor, this may confuse your model.
• Rewrite or delete the item to make the factors clearer.
• Use oblique rotation (e.g., Oblimin) if the factors are correlated—this often provides a cleaner load.

6. Strengthen Construct Validity

• Use established and validated scales from previous research. Items that have worked before tend to load well.
• If you created the item yourself, run a trials and refine it before large-scale data collection.

7. Use Model Modification in CFA (Carefully)

• In AMOS, LISREL, or other SEM software, modification indices can indicate where correlation between error terms can improve the fit.
• But ⚠️ be careful—don’t just chase better numbers. Modifications must always be present theoretical justificationnot just statistics.

In short

To increase 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 the results.
• Check validity through trials and theory.

Good factor loadings = stronger measurement model = more reliable results of your research.