How to Calculate Sample Size for Independent Samples T-Test in SPSS

In the run-up to an empirical study or data collection for the independent samples t-test (also two samples t-test), the necessary sample size must be determined.
You can either use G*Power ( • Two sample t-test - ca... ) or conveniently SPSS - if that is your analysis environment.
The minimum sample size depends on various parameters, you need to specify aforehand:
- the assumed effect size (Cohen's d),
- the alpha level,
- one- or two-sided testing,
- the statistical power and
- the allocation ratio (ratio of the number of observations for the groups).
The video also includes an overview for different effect sizes and power at the end.
⏰ Timestamps:
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0:00 Introduction
0:12 Select type of power analysis
0:22 Input parameter I: power (1-beta error)
0:51 Input parameter II: Allocation/group size ratio
1:00 Input parameter III: Effect size Cohen's d
1:42 Input parameter IV: One-tailed or two-tailed testing
1:55 Input parameter V: alpha level (alpha error)
2:11 Calculation and overview
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[@kevinjohanes3285]

I was having trouble finding the right formula for independent t test sample size formula. I used G power to calculate it, and although the numbers do pop up, the calculation steps is not transparent and the number is different from the one i manually calculated myself. Do you know any way to show which formula this software use?
Btw, which SPSS version is this? I use SPSS 25 but it doesnt have power analysis tab.

[@statorials]

Hello Kevin, this is SPSS 29. The power analysis section was added back in 2021 in SPSS 28.
You can find information on the used algorithms in the respective manual: www.ibm.com/docs/en/SSLVMB_29.0.0/pdf/IBM_SPSS_Statistics_Algorithms.pdf Either search for "POWER MEANS INDEPENDENT Algorithms" or directly navigate to 903 in Acrobat Reader.
Cheers, Björn.

[@kevinjohanes3285]

@@statorials your guidance is much appreciated. Thank you sir