One of the most important things you can do when designing your study is to
conduct an a priori power analysis. Doing so will tell you
how many people that you will need in your sample size to detect the effect size or treatment effect in your study.
Without an
a priori calculation, you could frivolously
waste months or years of your life conducting a study only to find out that you only needed 100 in each group to achieve significance. Or, with the inverse, you conduct a study with only 50 patients and find out in a post hoc fashion that you would have needed 10,000 to prove your effect!
If you are using
Research Engineer and
G*Power to run your analyses, here are the things you will need:
1.
An evidence-based measure of effect from the literature is the first thing you should seek out. Find a study that is
theoretically, conceptually, or clinically similar to your own. Try to find a study that
uses the same outcome you plan to use in your study.
2. Use the
means, standard deviations, and proportions from these published studies as evidence-based measures of effect size to calculate how large of a sample size you will need. These values will be
reported in body of the results section or in tables within the manuscript. It shows more
empirical rigor on your part if you conduct an
a priori power analysis based on a well-known study in the field.
3. Plug these values into
G*Power using the steps published on the
sample size page to find out how many people you will need to collect for your study.
