Making the black box transparent: A template and tutorial for (pre-)registration of studies using Experience Sampling Methods (ESM)

A growing interest in understanding complex and dynamic psychological processes as they occur in everyday life has led to an increase in studies using Ambulatory Assessment techniques, including the Experience Sampling Method (ESM) and Ecological Momentary Assessment (EMA). There are, however, numerous “forking paths” and researcher degrees of freedom, even beyond those typically encountered with other research methodologies. Whilst a number of researchers working with ESM techniques are actively engaged in efforts to increase the methodological rigor and transparency of such research, currently, there is little routine implementation of open science practices in ESM research. In the current paper, we discuss the ways in which ESM research is especially vulnerable to threats to transparency, reproducibility and replicability. We propose that greater use of (pre-)registration, a cornerstone of open science, may address some of these threats to the transparency of ESM research. (Pre-)registration of ESM research is not without challenges, including model selection, accounting for potential model convergence issues and the use of pre-existing datasets. As these may prove to be significant barriers to (pre-)registration for ESM researchers, we also discuss ways of overcoming these challenges and of documenting them in a (pre-)registration. A further challenge is that current general templates do not adequately capture the unique features of ESM. Here we present a (pre-)registration template for ESM research, adapted from the original Pre-Registration Challenge (Mellor et al., 2019) and pre-registration of pre-existing data (van den Akker et al., 2020) templates, and provide examples of how to complete this.