The analysis of time series data has been common in many areas of research for decades, but for psychological research the large-scale collection of longitudinal data is a recent development. Through technological advances, we are now able to repeatedly measure psychological variables such as emotions repeatedly over time – a process called ecological momentary assessment (EMA). Due to different aspects of these EMA-data, the application of standard time series models to these data is not straightforward.
EMA-data are usually collected at relatively few, say 25-50, time points. Common autoregression-parameter estimators have been developed based upon asymptotic arguments, which clearly don’t work for such short series. In the first half of my presentation, I will focus on the consequences of this for the estimation of parameters in the model, both for single case studies as for multilevel/hierarchical designs.
In the second half of the talk, I will discuss the Bayesian dynamic model (BDM). This Bayesian extension of state space models offers a versatile class of modelling approaches very useful for typical psychological data. I will outline the benefits of this class of models and showcase it using two examples from psychological practice. The first example stems from emotion research, in the second we compare different treatments for patients suffering from anxiety attacks.