Ecological Momentary Assessment (EMA) Designs with Planned Missingness

Developing feasible study designs that minimize the number of participant responses while retaining acceptable statistical properties has been a challenge in psychological research, thus motivating the developments and use of planned missing designs in longitudinal panel studies. In this study we propose several planned missingness designs for experience sampling/ecological momentary assessment (EMA) studies and evaluate the statistical implications and trade-offs involved in reducing the number of data points collected per person. We consider change trajectories arising from the latent growth curve, multilevel, and time series contexts. A Monte Carlo simulation study revealed that factors such as the type of change trajectory and the placement of data points can greatly affect the estimation results even when the number of time points is held constant. Traditional growth curve models and an autoregressive time series model of order 1 worked well with most planned missingness designs, while a moving average time series model of order 1 required a more careful selection of the planned missingness scheme. Findings also revealed that most planned missingness designs were robust to identifying correctly specified models provided that the correct time intervals are used, thus providing enriched options for researchers and practitioners to collect fewer data points with negligible costs to statistical power.