You can calculate the sample size using Python or MATLAB, especially if you have a complete dataset. This is particularly effective when you have a complete dataset, as it allows for more accurate sample size estimation based on your study design. In Python, you can use the statsmodels library to conduct power analysis for multilevel models and estimate the required sample size. In MATLAB, you can use the pwr or nlmefit functions to perform similar calculations based on your study design and expected effect sizes. How much data set you have?

GREETINGS

Power calculations – however made – require data assumptions. You might run the first block of your study with a moderate sample size (eg 20 Ps) – it would provide you with 1) reliability and other information that you may use for power calculations, and 2) give you part of your first block data (assuming no major issues found during the process right). Remember that your study is over a year – and there will be subject dropout losses during that time. You will need to proportionally increase the required sample size to accommodate that loss — which may not be built into the stat packages that you use for basic power calculations.)

Note also that if if inputting statistical values drived from an early base study (eg I suggested with the first block above ) they are random variables with estimable variances. Most power calculators do not account for this… the other are workarounds. [ We outline,a simplified power calculation algorithm, with an accommodation for this — with an illustration ] in a paper that should be available in the next month.]. In any case, the first block suggestion above could be pursued in the near term as well as a search for power software that accommodates, variation, and inputs..

Regards,

ALVAH

First step would be to identify the quantity you hypothesis on the effect-size you think minimally meaningful to spend effort on. Smaller effect-sizes require more effort meaning more dat is required. I you know the specific quantity you hypothesis on designing a simulation would give the biggest advantages and flexibility. Otherwise Gpower is certainly the most famous alternative.