Calculate The Sample Size Needed for a Research Study (2024)

Calculate sample size for ten different statistical tests using G*Power

Sample size plays an integral role in statistical power and the ability of researchers to make precise and accurate inferences. In order to calculate sample size, researchers have to know what type of effect size they are attempting to detect. Oftentimes, researchers have NO IDEA what their proposed effect sizeconstitutes in regards to magnitude and variance.

The best choice for most researchers is to seek out published papers in the area of empirical interest that answer theoretically, conceptually, or physiologically similar research questions and use the reported values associated with the statistical results. This is known as using an evidence-based measure of effect size to plan an a priori sample size calculation. Sample size calculations using evidence-based measures of effect show more empirical rigor on the researchers' part and adds internal validity to the study.

Researchers should seek out thehighest level of evidenceat their disposal.Systematic reviewsandsynopses of synthesesproducethe mostprecise and accurate evidence-based measures of effect size.Randomized controlled trialsshould be considered if no systematic reviews or syntheses exist in the empirical area.Observational studiesshould only beconsidered ifhigher levels of evidencedo not exist in the current literature.

Inclusion and exclusion criteria and sample size

One of the first things that researchers do when writing their research questions and designing a study is to objectively define the

. In order to define a population from which a sample size will be taken, researchers use inclusion criteria (characteristics that members of the population of interest must possess to be included in the study) and exclusion criteria (characteristics that members of the population should not possess for purposes of sampling). Click on a button below to learn more about establishing these criteria for a population.

Inclusion Criteria

Exclusion Criteria

Sampling methods and sample size

There are two primary sampling methods that can be used in applied research:

and

. Probability sampling is used in experimental designs where all members of a given population have an equal chance of being selected for participation in a trial, also known as

. Non-probability sampling is used in observational designs where the outcomes of interest have already occurred or when random selection is not feasible. Click on the Sampling Methods button below to learn more.

Sampling Methods

G*Power and sample size calculations

After finding the highest level of evidence and critically appraising its results in the context of the current empirical or clinical setting, the evidence-based measures of effectcan be utilized in both a priori and post hoc power analyses. The open-source statistical power application, G*Power, is a towering contribution to the field of applied science.* G*Power provides researchers the ability to conduct many types of power analyses and provides a user-friendly interface. The methods for conducting sample size calculations for ten different statistical tests are presented below.

Click here to download G*Power:http://www.gpower.hhu.de/en.html

The Sample Sizedecision tree provides the methods for conducting and interpreting 10 sample size calculations using G*Power. Click on a button below to continue.

Methods for calculating sample size in G*Power

Chi-Square

Mann-Whitney U

Independent-Samples t-test

One-Way ANOVA

McNemar's Test, Relative Risk

Wilcoxon

Repeated-Measures t-test

Repeated-Measures ANOVA

Point Biserial

Pearson's r

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