Sample size determination for quantitative research

Continuous data is data where the responses fall on a continuum (e.g., likert-type scales), unlike categorical data (e.g., gender, occupation, etc.)

This article by James E. Bartlett, Joe W. Kotrlik and Chadwick C. Higgins describes the process for sample size determination. They also offer a useful table that compares continuous and categorical data calculations:

In the article the authors invite use of this table *if* the margin of error is appropriate for a researcher's study. If the researcher selects a different margin of error, the sizes need to be re-calculated.
A final note to researchers is to remember that the degree to which you can generalize is based on the sampling METHOD (not just the sampling numbers!) so be sure to familiarize yourself with all of that!

There are also a few rules of thumb you might consider, especially if the population size is not known (Hill, Robin, 1998, What sample size is "enough" in internet survey research? Interpersonal Computing and Technology: An Electronic Journal of the 21st Century, 6(3/4), 1-10). These include:

• Generally speaking, it's difficult to justify fewer than 10 cases, or more than 500
• In simple matched-pairs experimental designs, 10 cases can suffice, but more complicated experimental designs OR correlational research should have at least 30 cases. When these are broken down into categories (e.g., male/female) then multiple the minimum number of cases by categories
• For multiple regressions, samples should be at least 10 times the number of variable (so 5 variables means you should have at least 50 cases
• For purely descriptive research, the sample should be 10% of the population

To check sufficiency of data when the population is not known, you can perform a "split half analysis" in which you divide the data in two, and see if both halves generate the same conclusions.