Is your sample size sufficient for your quantitative analysis? This is a question that a lot of doctoral researchers ask. I guess it depends on the type of analysis used. My PhD research looked at blog readers and I used Structural Equation Modelling (SEM) to analyse my data. I used the following to justify my sample size.
- For factor analysis, according to Hair et al., (2010), a sample should preferably be more than 100 for factor analysis to proceed
- However, according to Tabachnick and Fidell (2007 p. 613), it should be higher than 300 cases, the number that is considered “comfortable”.
If you are doing SEM:
- A ratio of ten responses per free parameters is required to obtain trustworthy estimates (Bentler and Chou, 1987).
- Others suggest a rule of thumb of ten subjects per item in scale development, is prudent (Flynn and Pearcy 2001).
- However, if data is found to violate multivariate normality assumptions, the number of respondents per estimated parameter increases to 15 (Bentler and Chou 1987; Hair, et al. 2006).
BENTLER, P. M. & CHOU, C. P. (1987) Practical issues in structural modeling. Sociological Methods & Research, 16, 78.
FLYNN, L. & PEARCY, D. 2001. Four subtle sins in scale development: Some suggestions for strengthening the current paradigm. International Journal of Market Research 43: (4) 409-433.
HAIR, J. F., BLACK, W. C., BABIN, B. J. & ANDERSON, R. E. 2010. Multivariate Data Analysis: A Global Perspective, New Jersey, Pearson Prentice Hall.
TABACHNICK, B. G. & FIDELL, L. S. 2007. Using Multivariate Statistics (5th Ed.) New York, HarperCollins.