Factor Loading Threshold Calculator

Work out factor loading threshold instantly with clear inputs, formula shown and shareable results.

About Factor Loading Threshold Calculator

The loading needed for statistical significance falls as sample size rises: about 0.40 at n = 200 but only 0.30 at n = 350, following Hair's guidelines. A loading of 0.62 therefore passes comfortably at n = 200 and explains 38.4% of the item's variance, since communality is the squared loading.

Formulas

Significance thresholds

0.30 at n=350, 0.40 at n=200, 0.55 at n=100

Communality

variance explained = loading squared

Worked example

With the default values below, Factor Loading Threshold Calculator returns Loading Required For Significance of 0.400. Change any input above and every figure here is recalculated in your browser.

Inputs used

Sample sizeminimum 10: 200
Observed factor loadingaccepts -1–1: 0.62

Results

Loading Required For Significance: 0.400
Variance Explained By The Factor: 0.3844
Significance Verdict: Loading is significant at this sample size
Margin Above The Threshold: 0.220

Step by step

1Values used
2Significance thresholds
3Communality
4Loading Required For Significance= 0.400
5Variance Explained By The Factor= 0.3844
6Significance Verdict= Loading is significant at this sample size
7Margin Above The Threshold= 0.220

Assumptions and limits

The result is derived only from the 2 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax or compounding frequency into account.
Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Why does the threshold depend on sample size?

Because significance testing compares the loading against its standard error, which shrinks as the square root of n.

Is a significant loading a useful one?

Not necessarily. A 0.30 loading explains only 9% of variance, so practical significance usually demands 0.50 or more regardless of n.