What is the minimum sample size for principal component analysis?

In the context of PCA and FA, some workers have proposed rules of thumb for minimum sample size in relation to number of variables or correlation structure. Gorsuch (1983) recommended at least 100 samples. Hatcher (1994) recommended that the sample size should be larger than five times the number of variables (p).

Which of the following is the main difference between principal components analysis and principal axis factoring?

Despite all these similarities, there is a fundamental difference between them: PCA is a linear combination of variables; Factor Analysis is a measurement model of a latent variable.

Does sample size matter in factor analysis?

Minimum Sample Size Recommendations for Conducting Factor Analyses. There is no shortage of recommendations regarding the appropriate sample size to use when conducting a factor analysis. Suggested minimums for sample size include from 3 to 20 times the number of variables and absolute ranges from 100 to over 1,000.

What is a good sample size for bivariate data analysis?

A good maximum sample size is usually 10\% as long as it does not exceed 1000. A good maximum sample size is usually around 10\% of the population, as long as this does not exceed 1000. For example, in a population of 5000, 10\% would be 500. In a population of 200,000, 10\% would be 20,000.

Are factor analysis and PCA the same?

The mathematics of factor analysis and principal component analysis (PCA) are different. Factor analysis explicitly assumes the existence of latent factors underlying the observed data. PCA instead seeks to identify variables that are composites of the observed variables.

When should one use PCA and ICA?

Although the two approaches may seem related, they perform different tasks. Specifically, PCA is often used to compress information i.e. dimensionality reduction. While ICA aims to separate information by transforming the input space into a maximally independent basis.

What does the size of a sample in factor analysis has an impact on?

They argue that, as the sample size increases, sampling error is reduced, factor analysis solutions become more stable and more reliably produce the factorial structure of the population (MacCallum et al 1999).