Students typically struggle with sample size
justification, in part because there are 2 types. One type is based on
the population and the second based on a power analysis. Sample size
based on a population is generally not used in dissertations. It not used
in dissertations because the requirement would too exhaustive to stratify the
population in terms of geography and the size requirement would be too
great.
The sample size based on a power analysis is used
in dissertation and is a required section in your method chapter (and is needed
for IRB or URR). A power analysis essentially says that the researcher
has a 80% chance of finding differences or relationships among the variables if
they actually do exist. Sample size based on a power analysis uses the
type of statistical analysis you are using such as an ANCOVA, multiple
regression, Pearson correlation, etc), the alpha (typically .05), and a
small, medium or large effect size. Effect size has both theoretical and
practical considerations. At a theoretical level, the researcher needs to
review other studies that examined the same type of constructs or the same
instruments, then see what effect size was found. If the effect size is
not presented, it can be calculated from the means and standard deviations,
one-way ANOVAs, frequency counts, correlations, mean gain scores,
unstandardized regression coefficients, full sample standard deviations,
chi-squares, phi-coefficients, cell frequencies, t-tests, or proportions.
The practical aspect of justifying the sample size is money and time needed to
collect data. For example, if you’re running a multiple regression with 3
predictor variables AND the effect size is small, you’ll need an
N=547! This is in comparison to a regression at a medium effect
size with a desired N=76 or a large effect size with an N=34.
Sample size can be calculated by using a free G*Power analysis program or you can purchase a
sample size write-up with references from our website by signing on for our Basic Membership for $29.00.
