This book investigates the methodological challenges of conducting statistical power analysis when datasets contain missing observations, specifically within the framework of structural equation modeling. Adam Davey and Jyoti Savla provide a technical guide for researchers who must navigate the complexities of incomplete data in quantitative studies. The authors synthesize current statistical theory to offer practical strategies for estimating power, ensuring that researchers can maintain the validity of their models despite data attrition. By focusing on structural equation modeling, the text addresses the intersection of latent variable analysis and missing data imputation techniques.

Experts recognize this text as a specialized resource for advanced researchers and graduate students working with complex longitudinal or cross-sectional datasets. Readers frequently note the technical density of the prose, which assumes a foundational understanding of both structural equation modeling and statistical inference.