Making the Black Box Transparent: A Template and Tutorial for Registration of Studies Using Experience-Sampling Methods. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Kirtley, Olivia J.; Lafit, Ginette; Achterhof, Robin; Hiekkaranta, Anu P.; Myin-Germeys, Inez
• Is Part Of: Advances in methods and practices in psychological science. Volume 4:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: preregistration -- reproducibility -- open science -- transparency -- experience sampling -- intensive longitudinal data; Psychology -- Periodicals; Psychology -- Research -- Periodicals; 150
• DOI: 10.1177/2515245920924686
• Languages: English
• ISSNs: 2515-2459

Abstract

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. These methods, however, tend to involve numerous forking paths and researcher degrees of freedom, even beyond those typically encountered with other research methodologies. Although a number of researchers working with ESM techniques are actively engaged in efforts to increase the methodological rigor and transparency of research that uses them, currently there is little routine implementation of open-science practices in ESM research. In this article, we discuss the ways in which ESM research is especially vulnerable to threats to transparency, reproducibility, and replicability. We propose that greater use of study registration, a cornerstone of open science, may address some of these threats to the transparency of ESM research. Registration of ESM research is not without challenges, including model selection, accounting for potential model-convergence issues, and the use of preexisting data sets. As these may prove to be significant barriers for ESM researchers, we also discuss ways of overcoming these challenges and of documenting them in a registration. A further challenge is that current general preregistration templates do not adequately capture the unique features of ESM. We present a registration template for ESM research and also discuss registration of studies using preexisting data.