Ex Ante Power Analysis for Multiple Regression and Structural Equation Modeling (SEM) is a statistical technique used to determine the sample size required for a study to achieve a desired level of statistical power.

Statistical power refers to the probability of detecting a true effect or relationship between variables in a study. It is influenced by several factors, including the sample size, effect size, alpha level (significance level), and the statistical analysis technique employed.

Ex Ante Power Analysis is conducted prior to conducting the actual study to estimate the sample size needed to achieve a desired level of power. It involves specifying the effect size (the magnitude of the relationship between variables) expected in the study, the significance level (commonly set at 0.05), and the desired power (typically 0.80 or 0.90).

In the case of multiple regression analysis, Ex Ante Power Analysis takes into account the number of predictors (independent variables) and the expected effect size for each predictor. It calculates the necessary sample size to ensure sufficient power to detect the specified effects.

For SEM, Ex Ante Power Analysis considers the complexity of the model, including the number of observed and latent variables, the expected relationships between them, and the expected effect sizes. It estimates the required sample size to adequately test the hypothesized model.

The aim of conducting Ex Ante Power Analysis is to ensure that the study is adequately powered, which means it has a high probability of detecting the effects or relationships under investigation. Insufficient power can lead to Type II errors, where real effects are missed or deemed insignificant due to small sample sizes.

By conducting Ex Ante Power Analysis, researchers can determine an appropriate sample size, reducing the risk of underpowered studies and increasing the likelihood of obtaining meaningful results.

Zubaida Abdul Sattar Thanks a lot for sharing detailed information.