Estimating Measurement Quality in Digital Trace Data and Surveys Using the MultiTrait MultiMethod Model

Alexandru Cernat*, Florian Keusch, Ruben L. Bach, Paulina K. Pankowska

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Digital trace data are receiving increased attention as a potential way to capture human behavior. Nevertheless, this type of data is far from perfect and may not always provide better data compared to traditional social surveys. In this study we estimate measurement quality of survey and digital trace data on smartphone usage with a MultiTrait MultiMethod (MTMM) model. The experimental design included five topics relating to the use of smartphones (traits) measured with five methods: three different survey scales (a 5- and a 7-point frequency scale and an open-ended question on duration) and two measures from digital trace data (frequency and duration). We show that surveys and digital trace data measures have very low correlation with each other. We also show that all measures are far from perfect and, while digital trace data appears to have often better quality compared to surveys, that is not always the case.

Original languageEnglish
JournalSocial Science Computer Review
Early online date21 May 2024
DOIs
Publication statusE-pub ahead of print - 21 May 2024

Keywords

  • data quality
  • digital trace data
  • latent variables
  • measurement error
  • survey data

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