Assessing psychological variables on mobile devices: an introduction to the experience sampling app ESM-Quest

Abstract

The use of the Experience Sampling Method (ESM), which involves repeated assessments in people's daily lives, has increased in popularity in psychology and associated disciplines in recent years. A rather challenging aspect of ESM is its technical implementation. In this paper, after briefly introducing the history of ESM and the main reasons for its current popularity, we outline the ESM-Quest experience sampling app which is currently being developed at the University of Vienna. ESM-Quest runs on different operating systems, specifically on mobile devices such as smartphones and tablets running either iOS or Android. An internet connection is not necessary during the assessment. Compared to most other ESM apps, ESM-Quest allows event-based random sampling, which is very helpful when assessments need to be collected within specific situations. Currently, ESM-Quest is being utilized at the University of Vienna and will be made available for research groups worldwide upon request. We introduce the technical aspects of ESM-Quest and provide examples of analyses on ESM data collected through this app, such as examining fluctuations in constructs within individuals. Finally, we outline potential next steps in ESM research.

Keywords: ESM-Quest; assessment; experience sampling method; psychological variables; real-time data; state; trait.

Figure 1

Screenshot of the ESM-Quest configuration options.

Figure 2

Screenshots of the mobile client of ESM-Quest.

Figure 3

Fluctuations within participants across time. The figures display how control (A), value (B), and enjoyment (C) fluctuate within three participants across the duration of data collection. The left panel (Study 1) displays three participants (yellow, orange, and red lines) who activated the app 16 times during learning situations. As event sampling was used and participants activated the app individually, the distance between activation sessions might differ between participants. The right panel displays three randomly selected participants and their responses across seven days of assessment. For all panels, the jittered points depict single assessments nested within the respective clustering units: learning situations (Study 1) and mental situations across days (Study 2). The lines connect the situational/daily averages of the respective individuals.

Figure 4

Distribution of individuals’ variance of state responses. The figure illustrates the range of intraindividual variance for the variables control, value, and enjoyment across participants, for the student sample of Study 1 (upper panel) and the adult lifespan sample of Study 2 (lower panel). For example, in Study 1 (upper panel), five participants experienced no variance in control over time (i.e., their control ratings were on the same level of intensity across all measurement points).

Figure 5

Variance decomposition in inter- and intraindividual variance components. The student sample (Study 1) is displayed in the left panel, the adult lifespan sample (Study 2) is displayed in the right panel. For the adult lifespan sample, intraindividual variance is displayed separately for daily variance (nested in individuals) and situational variance (nested in both individuals and days).

Figure 6

Distribution of aggregated state (background, in color) and trait (front, gray lines) variables (Study 1). Aggregated state variables are displayed in color, trait variables are displayed in gray and transparent points.