Assessing and accounting for measurement in intensive longitudinal studies: current practices, considerations, and avenues for improvement

Author:

Vogelsmeier Leonie V. D. E.ORCID,Jongerling JoranORCID,Maassen EstherORCID

Abstract

Abstract Purpose Intensive longitudinal studies, in which participants complete questionnaires multiple times a day over an extended period, are increasingly popular in the social sciences in general and quality-of-life research in particular. The intensive longitudinal methods allow for studying the dynamics of constructs (e.g., how much patient-reported outcomes vary across time). These methods promise higher ecological validity and lower recall bias than traditional methods that question participants only once, since the high frequency means that participants complete questionnaires in their everyday lives and do not have to retrospectively report about a large time interval. However, to ensure the validity of the results obtained from analyzing the intensive longitudinal data (ILD), greater awareness and understanding of appropriate measurement practices are needed. Method We surveyed 42 researchers experienced with ILD regarding their measurement practices and reasons for suboptimal practices. Results Results showed that researchers typically do not use measures validated specifically for ILD. Participants assessing the psychometric properties and invariance of measures in their current studies was even less common, as was accounting for these properties when analyzing dynamics. This was mainly because participants did not have the necessary knowledge to conduct these assessments or were unaware of their importance for drawing valid inferences. Open science practices, in contrast, appear reasonably well ingrained in ILD studies. Conclusion Measurement practices in ILD still need improvement in some key areas; we provide recommendations in order to create a solid foundation for measuring and analyzing psychological constructs.

Publisher

Springer Science and Business Media LLC

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