Which of the following research designs usually has the smallest sample size?

Many times those conducting surveys are told that larger samples are always preferable to smaller ones.  This is not always necessarily the case.  An array of factors, including degree of variability in the population, the degree of accuracy desired, and the analysis the results will be subject to, should be considered when deciding upon a sample size.

Degree of accuracy desired: Related to the subject of Power Analysis (which is beyond the scope of this site), this method requires the researcher to consider the acceptable margin of error and the confidence interval for their study.  The online resource from Raosoft and Raven Analytics uses this principle.

Degree of variability (homogeneity/heterogeneity) in the population: As the degree of variability in the population increases, so too should the size of the sample increase.  The ability of the researcher to take this into account is dependent upon knowledge of the population parameters.

Number of different variables (subgroups) to be examined:  As the number of subgroups to be examined increases, so too should the size of the sample increase.  For example, should a researcher wish to examine the differences between ethnicities for a given phenomenon, the sample must be large enough to allow for valid comparison between each ethnic group.

Sampling ratio (sample size to population size): Generally speaking, the smaller the population, the larger the sampling ratio needed.  For populations under 1,000, a minimum ratio of 30 percent (300 individuals) is advisable to ensure representativeness of the sample.  For larger populations, such as a population of 10,000, a comparatively small minimum ratio of 10 percent (1,000) of individuals is required to ensure representativeness of the sample.

Response rate and oversampling: Are all the individuals in your sample likely to complete your questionnaire?  If not, oversampling (sampling more individuals than would otherwise be necessary) may be required.  Here the goal is to ensure that a given minimum raw count of respondents is met.  While this is straightforward for a project using Simple Random Sampling, this can become increasingly complex as the number of variables to be examined grows, since the researcher must ensure that each critical subgroup attains the required response rate.

Statistical analysis desired:  Specific minimum sample sizes are required for some statistical procedures, particularly those involving the investigation of multiple variables.

Other Online Resources

Sample Size Calculator (Raosoft)

Further Reading

Nardi, P.M. (2003). Doing survey research: A guide to quantitative methods. Boston, MA: Allyn and Bacon.
Neuman, W. L. (2007). Basics of social research: Qualitative and quantitative approaches (2nd ed.). Boston, MA: Allyn and Bacon.
Suskie, L.A. (1996). Questionnaire survey research: What works (2nd ed.). Tallahassee, FL: Association for Institutional Research

What sampling method is best for small population?

What research designs does not require large sample size?

Which sampling method is normally followed when the sample size is very small?

In which of the following sampling technique smallest sample size can serve the purpose?