The need to write-up a power analysis occurs a few times when conducting a research project: in the research proposal, ethics application, conference presentations, and the Method section of a thesis / empirical report.

**Why Do I Need to Do a Power Analysis? **

You need to conduct an a priori power analysis (*a priori *meaning it is conducted *before* you do your research) to calculate the minimum number of participants needed to test your study hypotheses / detect a significant effect (if one exists). Ethics committees care about power analysis because they don't want to see you unnecessarily recruiting (and potentially putting at __risk__) e.g., 500 participants, when you only needed 200 participants to detect an effect.

The most popular software for conducting power analysis is __G*Power__.

**What do I need to Include in a G*Power Analysis Write-Up? **

When you write-up the results of an a priori G*Power analysis (i.e., the number of participants required to detect an effect) you need to report three parameters that you input into G*Power:

alpha (usually set to Î± = .05),

power (usually set to .80)

effect size (this can vary - see below)

The **expected effect size** (i.e., the strength of the effect) that you input into G*Power is generally derived from:

A pilot study or published study that has looked at your variables; or

If there is no pilot data and no similar studies (i.e., your study is new), then you can use Cohen's (1988) general guidelines for detecting a â€˜small"â€™, "â€˜medium"â€™, or "â€˜largeâ€™ "effects (these are reported in the

__G*Power manual____)__.

From my experience, some universities/supervisors are BIG on power analysis; others, not so much. If they are BIG on power analysis, they may want you to go down path 1 and search the literature for a study similar to yours that has published an effect size that you can then input into G*Power. Because previous research is not likely to be identical to your study design, this search can be a challenge. In contrast, other supervisors will just recommend you make an educated guess of the expected effect (e.g., small, medium or large) based on Cohen's guidelines (this is often a medium effect - but make sure to discuss this with your supervisor!).

**Example G*Power Write-ups **

**1. Where the effect size is from a pilot/published study: **

"An a priori power analysis was conducted using G*Power version 3.1.9.7 (Faul et al., 2007) for sample size estimation, based on data from [pilot study/published study] (year) (*N* = XX), which compared X to Y. The effect size in [pilot study/published study]'s study was #, considered to be [extremely large/large/medium/small] using Cohen's (1988) criteria. With a significance criterion of Î± = .05 and power = .80, the minimum sample size needed with this effect size is *N* = # for [insert statistical test you are using to test your hypothesis]. Thus, the obtained sample size of *N* = # is more than adequate to test the study hypothesis."

**2. Where a medium effect is expected:**

â€œAn a priori power analysis was conducted using G*Power version 3.1.9.7 (Faul et al., 2007) to determine the minimum sample size required to test the study hypothesis. Results indicated the required sample size to achieve 80% power for detecting a medium effect, at a significance criterion of Î± = .05, was *N* = # for [insert statistical test you are using to test your hypothesis]. Thus, the obtained sample size of *N *= # is adequate to test the study hypothesis."

*Additional Tips:*

*Additional Tips:*

If you have multiple hypotheses that each require different data analysis strategies (e.g., Hypothesis 1 is to be tested using correlation; Hypothesis 2 is to be tested using a multiple regression), you may need to perform a separate power analysis for each hypothesis. I recommend reporting the results of each power analysis and then selecting the larger sample size needed from among them as a basis of recruitment.

For analysis that compares groups, be sure to include the number of participants required per group (e.g., "G*Power suggests we would need # participants per group (

*N*= #) in an independent samples t-test").In your research proposal / ethics application you may want to increase your proposed sample size to account for potential attrition. Try to include a reference to justify this increased sample size. E.g., "Accounting for a potential attrition rate of 20% based on previous research [e.g., previous research that has used this intervention / investigated this topic] (see reference), an additional # participants will be recruited"].

I have included further G*Power resources below.

Happy Researching! ðŸŽ‰

## G*Power Resources:

Download G*Power __here__

â€‹

â€‹

The correct reference for G*Power is:

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. *Behavior Research Methods, 39, *175â€“191. https://doi.org/10.3758/BF03193146

**Please remember: **If the content of this article conflicts with what you have been taught at your university... please follow the advice of your university! Always seek the advice of your supervisor!

*****If you want more strategies and tips for writing your thesis, you can enrol in my on-demand workshop, ***Learn How to Write a Kick Ass Thesis: Part 1 - Setting Yourself up For Success in Your Literature Review and Introduction********

## Comments