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

Autor: Slipetz, Lindley R., Falk, Ami, Henry, Teague R.
Rok vydání: 2023
Předmět:
Druh dokumentu: Working Paper
Popis: When using ecological momentary assessment data (EMA), missing data is pervasive as participant attrition is a common issue. Thus, any EMA study must have a missing data plan. In this paper, we discuss missingness in time series analysis and the appropriate way to handle missing data when the data is modeled as a discrete time continuous measure state-space model. We found that Missing Completely At Random, Missing At Random, and Time-dependent Missing At Random data have less bias and variability than Autoregressive Time-dependent Missing At Random and Missing Not At Random. The Kalman filter excelled at handling missing data. Contrary to the literature, we found that, with either default package settings or a lag-1 imputation model, multiple imputation struggled to recover the parameters.
Databáze: arXiv