Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach

By Adam Davey, J. Savla. 2009

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Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety (2013, methodology): “Prior work suggests that researchers must consider if missing data are missing at random versus missing completely at random, as this distinction will likewise affect statistical power and sample size requirements (Davey & Salva, 2009a; Dolan et al., 2005).”
The American Identity Measure: Development and Validation across Ethnic Group and Immigrant Generation (2012, methodology): “The chi-square value tests the null hypothesis of perfect fit to the data, which is rarely plausible in large samples or complex models (Davey & Savla, 2010).”