Below you will find complete descriptions and links to 4 different analytics calculators for computing sample sizes for different types of analytics studies.

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Compute the sample size required for a hierarchical multiple regression study. The calculator computes the minimum required sample size for a significance test of the addition of a set of predictor variables B to the model, over and above another set of predictor variables A, given the expected effect size, probability level, and power level. Knowing the correct sample size is very useful for ensuring that hierarchical regression-based analytics studies have enough power to detect the expected effect.

Compute the minimum required sample size for your multiple regression study, given your desired p-value, the number of predictor variables in your model, the expected effect size, and your desired statistical power level. Knowing if your sample is large enough to detect an expected or hypothesized effect is critical to using multiple regression correctly in analytics.

Compute the minimum sample size for a structural equation model (SEM) study involving latent variables, given the expected effect size, the desired p-value, the desired statistical power level, and the number of observed and latent variables. The calculator will compute the minimum sample size required in light of the structural complexity of the model, as well as the minimum sample size required to detect the specified effect. Knowing the proper sample size is critical in analytics studies that rely on structural equation modeling (SEM).

Compute the minimum required total sample size and per-group sample size for a one-tailed or two-tailed t-test study, given the p-value, the expected effect size, and the statistical power level. Knowing if your sample is large enough to detect an expected or hypothesized effect is critical when conducting analytics studies that rely on t-tests.