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). Whilst a number of researchers working with these techniques are currently actively engaged in efforts to increase the methodological rigor and transparency of such research, at present there is little routine implementation of open science practices in ESM research.
In our **pre-print**, 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, our pre-print also discusses ways of overcoming these challenges and of documenting them in a (pre-)registration.
Here we also present:
1. **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. Much of the original content has been retained. We have added ESM research-specific sections and also adapted examples for the other sections, so that they are more relevant for ESM research.
2. **Two examples of how to complete the ESM (pre-)registration template**, including a pre-data collection pre-registration (Example 1) and a post-data collection "post-registration" (Example 2).
3. **R code** for calculating the number of participants required, using a simulation approach, and for illustrating the effect of serial dependency in the estimation accuracy of the mean level of stationary autoregressive processes.
