Throughout empirical research, not all measurements planned are taken in actual practice. It is important to reflect on the nature and implications of such incompleteness, or missingness, and to accommodate it properly in the modeling process. When referring to the

missing-value process, we will use terminology of Little and Rubin (2002, Chapter 6; see also Chapter 17 of this volume). A non-response process is said to be missing completely at random (MCAR) if the missingness is independent of both unobserved and observed data and missing at random (MAR) if, conditional on the observed data, the missingness is independent of the unobserved measurements. A process that is neither MCAR nor MAR is termed non-random (NMAR).