Basic Protocol: Smartphone Sensing Panel Study

Schoedel, Ramona; Oldemeier, Michelle

doi:10.23668/psycharchives.2901

Cited by 3 publications

(2 citation statements)

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“…We analyzed data collected in the SSPS, an interdisciplinary research project conducted by Ludwig-Maximilians-Universität München (LMU Munich) in cooperation with the Leibniz Institute for Psychology (Schoedel & Oldemeier, 2020). The SSPS obtained data via three modalities, namely online surveys, ES, and smartphone sensing, from a quota sample representative of the German population 1 .…”

Section: Methodsmentioning

confidence: 99%

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Schoedel¹,

Kunz²,

Bergmann³

et al. 2023

Journal of Personality and Social Psychology

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Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people’s thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this opportunity, the present study demonstrates how smartphones can help establish associations between the psychological perception and physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective cues obtained via smartphone sensing. More precisely, we extracted a total of 1,356 granular cues from different sensing modalities to account for the complexity of real-world situations. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, Sociality (DIAMONDS), finding significant out-of-sample predictions for the five dimensions reflecting the situations’ Duty, Intellect, Mating, pOsitivity, and Sociality. In a series of follow-up analyses, we further explored the data patterns captured by our models, revealing, for example, that those cues related to time and location were particularly informative of the respective situation characteristics. We conclude by interpreting the mapping between cues and characteristics in real-world situations and discussing how smartphone-based situational snapshots may push the boundaries of psychological research on situations.

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Section: Methodsmentioning

confidence: 99%

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Schoedel¹,

Kunz²,

Bergmann³

et al. 2023

Journal of Personality and Social Psychology

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“…We report how we determined our sample size, all data exclusions, manipulations, and all measures of this study and we follow JARS (Appelbaum et al, 2018). Additional details on the procedures and measures used in the larger research project are available in the study protocol provided by Schoedel and Oldemeier (2020). Furthermore, we provide in-depth descriptions of all data preprocessing and data analyses steps in our Open Science Framework (OSF) repository under https://osf.io/b7krz/?view_only=471f8c0007ff4e6da48c6180b4a1c45f.…”

Section: Transparency and Opennessmentioning

confidence: 99%

Snapshots of Daily Life: Situations Investigated Through the Lens of Smartphone Sensing

Schoedel¹,

Kunz²,

Bergmann³

et al. 2022

Preprint

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Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people’s thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this development, the present study demonstrates how smartphones can help establish associations between the psychological perception and the physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective situation cues obtained via smartphone sensing. To account for the complexity of real-world situations, we extracted a total of 1,356 granular situation cues from different sensing modalities. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight DIAMONDS, finding significant out-of-sample predictions for the five dimensions capturing the situations’ Duty, Intellect, Mating, pOsitivity, and Sociality. Analyses of (grouped) feature importance revealed that these predictions relied on complex constellations of cues representing various situational information about the Persons/Interactions and Objects present, the Events/Activities happening, and the current Location and Time. Furthermore, a nomological network analysis provided evidence for the construct validity of our cue-based DIAMONDS predictions. We conclude by discussing how smartphone-based situational snapshots, in general, and our prediction models, in particular, advance psychological research on situations.

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Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies

Reiter,

Schoedel

2023

Behav Res

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Given the increasing number of studies in various disciplines using experience sampling methods, it is important to examine compliance biases because related patterns of missing data could affect the validity of research findings. In the present study, a sample of 592 participants and more than 25,000 observations were used to examine whether participants responded to each specific questionnaire within an experience sampling framework. More than 400 variables from the three categories of person, behavior, and context, collected multi-methodologically via traditional surveys, experience sampling, and mobile sensing, served as predictors. When comparing different linear (logistic and elastic net regression) and non-linear (random forest) machine learning models, we found indication for compliance bias: response behavior was successfully predicted. Follow-up analyses revealed that study-related past behavior, such as previous average experience sampling questionnaire response rate, was most informative for predicting compliance, followed by physical context variables, such as being at home or at work. Based on our findings, we discuss implications for the design of experience sampling studies in applied research and future directions in methodological research addressing experience sampling methodology and missing data.

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Basic Protocol: Smartphone Sensing Panel Study

Cited by 3 publications

References 0 publications

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Snapshots of Daily Life: Situations Investigated Through the Lens of Smartphone Sensing

Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies

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