Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial
| dc.date.accessioned | 2024-09-11T08:53:08Z | |
| dc.date.available | 2024-09-11T08:53:08Z | |
| dc.date.issued | 2024-07-11 | |
| dc.identifier | doi:10.17170/kobra-2024091110809 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/16033 | |
| dc.description.sponsorship | Gefördert im Rahmen des Projekts DEAL | |
| dc.language.iso | eng | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | App-based data collection | eng |
| dc.subject | Monte Carlo simulations | eng |
| dc.subject | dropout | eng |
| dc.subject | longitudinal data | eng |
| dc.subject | planned missingness | eng |
| dc.subject.ddc | 150 | |
| dc.title | Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial | eng |
| dc.type | Aufsatz | |
| dcterms.abstract | Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows. Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Kueppers, Sebastian | |
| dcterms.creator | Rau, Richard | |
| dcterms.creator | Scharf, Florian | |
| dc.relation.doi | doi:10.1080/00273171.2024.2335411 | |
| dc.subject.swd | Monte-Carlo-Simulation | ger |
| dc.subject.swd | App <Programm> | ger |
| dc.subject.swd | Datenerhebung | ger |
| dc.type.version | publishedVersion | |
| dcterms.source.identifier | eissn:1532-7906 | |
| dcterms.source.issue | Issue 4 | |
| dcterms.source.journal | Multivariate Behavioral Research | eng |
| dcterms.source.pageinfo | 879-893 | |
| dcterms.source.volume | Volume 59 | |
| kup.iskup | false |
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