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The determination of sample size is a common task for many organizational researchers. Inappropriate, inadequate, or excessive sample sizes continue to influence the quality and accuracy of research. This manuscript describes the procedures for determining sample size for continuous and categorical variables using Cochran's (1977) formulas. A discussion and illustration of sample size formulas, including the formula for adjusting the sample size for smaller populations, is included. A table is provided that can be used to select the sample size for a research problem based on three alpha levels and a set error rate. Procedures for determining the appropriate sample size for multiple regression and factor analysis, and common issues in sample size determination are examined. Non-respondent sampling issues are addressed.
INTRODUCTION
A common goal of survey research is to collect data representative of a population. The researcher uses information gathered from the survey to generalize findings from a drawn sample back to a population, within the limits of random error. However, when critiquing business education research, Wunsch (1986) stated that "two of the most consistent flaws included (1) disregard for sampling error when determining sample size, and (2) disregard for response and nonresponse bias" (p. 31).
Within a quantitative survey design, determining sample size and dealing with nonresponse bias is essential. "One of the real advantages of quantitative methods is their ability to use smaller groups of people to make inferences about larger groups that would be prohibitively expensive to study" (Holton & Burnett, 1997, p. 71). The question then is, how large of a sample is required to infer research findings back to a population?
Standard textbook authors and researchers offer tested methods that allow studies to take full advantage of statistical measurements, which in turn give researchers the upper hand in determining the correct sample size. Sample size is one of the four inter-related features of a study design that can influence the detection of significant differences, relationships or interactions (Peers, 1996). Generally, these survey designs try to minimize both alpha error (finding a difference that does not actually exist in the population) and beta error (failing to find a difference that actually exists in the population) (Peers, 1996).
However, improvement is needed. Researchers are learning experimental statistics from highly...