In multiple regression texts, some authors (e.g., Pedhazur, 1997, p. 207) suggest subject to variable ratios of 15:1 or 30:1 when generalization is critical. But there are few explicit guidelines such as this for EFA (Baggaley, 1983). Two different approaches have been taken: suggesting a minimum total sample size, or examining the ratio of parameters such as subjects to variables, as in multiple regression.

Comfrey and Lee (1992) suggest that “the adequacy of sample size might be evaluated very roughly on the following scale: 50 – very poor; 100 – poor; 200 – fair; 300 – good; 500 – very good; 1000 or more – excellent” (p. 217). Guadagnoli & Velicer (1988) review several studies that conclude that absolute minimum sample sizes, rather than subject to item ratios, are more relevant. These studies range in their recommendations from an N of 50 (Barrett & Kline, 1981) to 400 (Aleamoni, 1976). In our mind, some of these recommendations are ridiculous, as they could result in analyses estimating far more parameters than available subjects.

The case for ratios. There are few scholars writing from the multiple regression camp who would argue that total N is a superior guideline to the ratio of subjects to variables. Yet, authors focusing on EFA occasionally vehemently defend this position. It is interesting precisely because the general goal for both analyses is similar: to take individual variables and create optimally weighted linear composites that will generalize to other samples or to the population. Although the mathematics and procedures differ in the details, the essence and the pitfalls are the same. Both EFA and multiple regression risk overfitting of the estimates to the data (Bobko & Schemmer, 1984), and both suffer from lack of generalizability when sample size is too small.

Absolute sample sizes seem simplistic given the range of complexity factor analyses can exhibit—each scale differs in the number of factors or components, the number of items on each factor, the magnitude of the item to factor correlations, and the correlation between factors, for example. This has led some authors to focus on the ratio of subjects to items or, more recently, the ratio of subjects to parameters (as each item will have a loading for each factor or component extracted). This is similar to what authors do with regression, rather than absolute sample size, when discussing guidelines concerning EFA.

Gorsuch (1983, p. 332) and Hatcher (1994, p. 73) recommend a minimum subject to item ratio of at least 5:1 in EFA, but they also describe stringent guidelines for when this ratio is acceptable, and they both note that higher ratios are generally better. There is a widely cited rule of thumb from Nunnally (1978, p. 421) that the subject to item ratio for exploratory factor analysis should be at least 10:1, but that recommendation was not supported by empirical research. Authors such as Stevens (2002) have provided recommendations ranging from 5 to 20 participants per scale item, with Jöreskog & Sörbom (1996) encouraging at least 10 participants per parameter estimated.

There is no one ratio that will work in all cases; the number of items per factor and communalities and item loading magnitudes can make any particular ratio overkill or hopelessly insufficient (MacCallum, Widaman, Preacher, & Hong, 2001).