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When using ecological momentary assessment data (EMA), missing data ispervasive as participant attrition is a common issue. Thus, any EMA study musthave a missing data plan. In this paper, we discuss missingness in time seriesanalysis and the appropriate way to handle missing data when the data ismodeled as a discrete time continuous measure state-space model. We found thatMissing Completely At Random, Missing At Random, and Time-dependent Missing AtRandom data have less bias and variability than Autoregressive Time-dependentMissing At Random and Missing Not At Random. The Kalman filter excelled athandling missing data. Contrary to the literature, we found that, with eitherdefault package settings or a lag-1 imputation model, multiple imputationstruggled to recover the parameters.

Missing Data in Discrete Time State-Space Modeling of Ecological Momentary Assessment Data: A Monte-Carlo Study of Imputation Methods