Hello Jawad,

As a rule of thumb, a minimum of 300 participants have been recommended (Yong & Pearce, 2013).

Another study ranked the size: "100 as poor, 200 as fair, 300 as good, 500 as very good, and 1000 or more as excellent" ( Williams, Onsman & Brown, 1996). Alternatively, cases to variable ratio of 1:5 is considered minimum.

In any case, even if you have less than the above-suggested sample size, factor analysis always return sample size adequacy tests e.g. Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy value (0.5 is the minimum).

Good luck!

Ref:

Gie Yong, A. & Pearce, S. A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis. Tutor. Quant. Methods Psychol. 9, 79–94 (2013).

Williams, B., Onsman, A. & Brown, T. Exploratory factor analysis: A five-step guide for novices. J. Emerg. Prim. Heal. Care 19, 42–50 (1996).

Various versions of this question have been asked here several times, including:

https://www.researchgate.net/post/How_to_calculate_power_for_path_models_in_SEM2

and

https://www.researchgate.net/post/How_can_we_use_Gpower_sample_size_software_for_calculating_our_sample_in_structural_equation_modeling

Hello Jawad,

As a rule of thumb, a minimum of 300 participants have been recommended (Yong & Pearce, 2013).

Another study ranked the size: "100 as poor, 200 as fair, 300 as good, 500 as very good, and 1000 or more as excellent" ( Williams, Onsman & Brown, 1996). Alternatively, cases to variable ratio of 1:5 is considered minimum.

In any case, even if you have less than the above-suggested sample size, factor analysis always return sample size adequacy tests e.g. Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy value (0.5 is the minimum).

Good luck!

Ref:

Gie Yong, A. & Pearce, S. A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis. Tutor. Quant. Methods Psychol. 9, 79–94 (2013).

Williams, B., Onsman, A. & Brown, T. Exploratory factor analysis: A five-step guide for novices. J. Emerg. Prim. Heal. Care 19, 42–50 (1996).

5:1 case to parameter ratio is doubtful

10:1 case to parameter ratio is realistic

20:1 case to parameter ratio is desirable

There are rule of Thumbs as mentioned above by Mohammed M. Alhaji however a more better approach will be based on the items that you have in your questionnaire and then follow the rules provided by J.F Hair. People usually tend to follow just 5 responses towards each item but a comprehensive result will be attained when there are 20 responses per item. Still it all depends on the population and sample that you have and obviously time restrictions.

Hope it helps.

Arshad Haroon

Although there are several suggestions for the minimum sample size in the factor analytic literature, following a rule of thumb might be misleading in some cases. A large dataset does not guarantee accurate factor solutions. I would suggest EFA/CFA researchers to check the communalities. When the communality values are high (larger than .60), even a relatively small sample size would be enough. In short, instead of blindly following certain cut-off levels/rule of thumbs, researchers should first get to know their dataset by performing a detailed data screening.

Here are some useful papers that researchers can refer to when conducting EFA/CFA:

Loewen, S., & Gonulal, T. (2015). Exploratory factor analysis and principal components analysis. In Plonsky, L. (Ed), Advancing quantitative methods in second language research. New York: Routledge.

Phakiti A. (2018) Confirmatory Factor Analysis and Structural Equation Modeling. In: Phakiti A., De Costa P., Plonsky L., Starfield S. (eds) The Palgrave Handbook of Applied Linguistics Research Methodology. Palgrave Macmillan, London

Plonsky, L., & Gonulal, T. (2015). Methodological synthesis in quantitative L2 research: A review of reviews and a case study of exploratory factor analysis. Language Learning, 65, (S1), 9-36.

Bandalos D. L., & Boehm-Kaufman M. R. (2009). Four common misconceptions in exploratory factor analysis. In C. E. Lance & R. J. Vandenberg (Ed.), Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences. New York: Routledge.

One can delete certain items depending on the reliability scores. It is not important to include all the statements in EFA as the main purpose is to identify the factors that impact the decision. The communalities and the total extraction if high , small sample size can be used for preliminary investigation.

KMO Value for EFA and HOELTER value for CFA represent the effectiveness of the sample size. For details see the books of Hair et. al. and Barbara Bryne

For EFA 100 +