Sample size for Chi-Square test of Independence with G*Power

// Sample size for Chi-Square test of Independence with G*Power //

In the run-up to an empirical study or data collection for the Chi-Square test of Indepdence (sometimes only referred to as only Chi-Square Test), the necessary sample size must be determined, for example using G*Power. The minimum sample size depends on the assumed effect size (Cohen's ω), the alpha level, the statistical power and the degrees of freedom.

Here I show how to determine the recommended minimum sample size for the Chi-Square test of Indepdence with different characteristics of the input parameters mentioned using G*Power.

The degrees of freedom are calculated from the number of rows - 1 multiplied with the number of columns -1.
df = (r-1)*(c-1)

Example: You want to test the possession of a drivers license (yes/no) with the place of living (Urban, suburban and rural county).
df = (2-1)*(3-1) = 1*2 = 2 degrees of freedom.


Download link:
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https://www.psychologie.hhu.de/arbeit...


Doing a Chi-Square-test:
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R:    • Chi Square test of Independence in R ...  
SPSS:    • Chi Square test of Independence in SP...  


Introduction to G*Power:
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🎥    • G*Power: A (short) Beginner's Guide  


⏰ Timestamps:
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0:00 Introduction
0:10 Selecting the Chi-Square test of Indepdence
0:24 Select type of power analysis
0:33 Input parameter I: Cohen's ω
1:00 Input parameter II: alpha level (alpha error)
1:17 Input parameter III: power (1-beta error)
1:45 Input parameter IV: degrees of freedom
2:11 Calculation and overview


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