ABSTRACT
In this book, we have tried to provide a step by step guide to planning, implementing, and interpreting a power analysis when the researcher expects missing data, focusing on a structural equation modeling perspective. The broad set of tools that we have outlined can be applied to measurement models, structural models, or any combination thereof and work equally well whether data are missing completely at random (MCAR) or missing at random (MAR). Although beyond the scope of this volume because it also introduces issues related to bias in parameter estimates, the methods we describe can also quite easily be extended to situations where data are missing not at random (MNAR) by generating a missing data mechanism that is not completely included in the analytic model itself.
