Departament de Psicologia
Universitat Rovira i Virgili
 
Inici | Utilitats | Seneca
ISeneca

Many rules-of-thumb has been proposed based on the experience of researchers in order to decide the size of the sample. However, simulation studies suggest that the actual size of a sample to be factor analyzed depends on the characteristics of the data at hand. SENECA carries on a simulation study based on your own data, so that the size of the sample proposed takes into account the characteristics of your data.

To calculate SENECA, you must provide a small initial sample. Please note that this must be a representative sample of the population and that the larger this initial sample is, the more accurate the advice given by SENECA will be. You must decide:

- If the factor analysis to be computed is linear (i.e., Pearson correlation matrix is analyzed) or non-linear (i.e., polychoric correlation matrix is analyzed). Polychoric correlations are usually less stables, and a large sample is usually needed in order to analyze them.

- The level of precision in the analysis. It means to decide a value for the index CRMSR: the lower the value, the more accurate the simulation study will be. We advise to use at least a value between .05 and .01.

- The number of replications to be computed. We advise at least 100. The larger the number of replications, the more accurate the simulation will be.

- If the study is purely exploratory (i.e., the number of factors is totaly unknown) or it has a confirmatory aim (i.e., the number of factors is at least suspected). If a number of factors is suspected, to provide the suspected number of factors to extract adds information to the simulation study and the sample advised is usually smaller.

Download Seneca Factor Sample Size v1.0.0.0

In this video tutorial you can see how to compute SENECA with FACTOR.


You can learn more about SENECA in the paper:

Lorenzo-Seva, U., & Ferrando, P. J. (2024). Determining Sample Size Requirements in EFA Solutions: A Simple Empirical Proposal. Multivariate Behavioral Research, 59(5), 899-912. doi:10.1080/00273171.2024.2342324.

Universitat Rovira i Virgili