This study evaluates the sample size requirements for structural equation modeling (SEM) using Monte Carlo data simulation techniques. The researchers systematically varied key model properties, including the number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data, to investigate how these parameters affect sample size requirements in terms of statistical power, bias in parameter estimates, and overall solution propriety. The results reveal a wide range of sample size requirements (from 30 to 460 cases), meaningful patterns of association between parameters and sample size, and highlight the limitations of commonly cited rules-of-thumb. The study emphasizes the importance of considering statistical power, bias, and solution propriety when determining sample sizes for SEMs, and provides insights into the variability of sample size requirements across different models. Key lessons include the need to optimize model characteristics, address the perils of ignoring bias, errors, small effects, and missing data, and ensure the stability of sample size requirements.This study evaluates the sample size requirements for structural equation modeling (SEM) using Monte Carlo data simulation techniques. The researchers systematically varied key model properties, including the number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data, to investigate how these parameters affect sample size requirements in terms of statistical power, bias in parameter estimates, and overall solution propriety. The results reveal a wide range of sample size requirements (from 30 to 460 cases), meaningful patterns of association between parameters and sample size, and highlight the limitations of commonly cited rules-of-thumb. The study emphasizes the importance of considering statistical power, bias, and solution propriety when determining sample sizes for SEMs, and provides insights into the variability of sample size requirements across different models. Key lessons include the need to optimize model characteristics, address the perils of ignoring bias, errors, small effects, and missing data, and ensure the stability of sample size requirements.