Optimizing Measurement Reliability in Within-Person Research: Guidelines for Research Design and R Shiny Web Application Tools
Sponsor
This research was supported by the grant T03OH008435 awarded to Portland State University, funded by the Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH, CDC, or HHS. This research is also supported by the National Science Foundation under Grant awarded to Wei Wang (No. 16406229).
Published In
Journal of Business and Psychology
Document Type
Citation
Publication Date
3-14-2022
Abstract
Within-person research has become increasingly popular over recent years in the field of organizational studies for its unique theoretical and methodological advantages for studying dynamic intrapersonal processes (e.g., Dalal et al., Journal of Management 40:1396–1436, 2014; McCormick et al., Journal of Management 46:321–350, 2020). Despite the advancements, there remain serious challenges for many organizational researchers to fully appreciate and appropriately implement within-person research—more specifically, to correctly conceptualize and compute the within-person measurement reliability, as well as navigate key within-person research design factors (e.g., number of measurement occasions, T; number of participants, N; and scale length, I) to optimize within-person reliability. By conducting a comprehensive Monte Carlo simulation with 3240 data conditions, we offer a practical guideline table showing the expected within-person reliability as a function of key design factors. In addition, we provide three easy-to-use, free R Shiny web applications for within-person researchers to conveniently (a) compute expected within-person reliability based on their customized research design, (b) compute observed validity based on the expected reliability and hypothesized within-person validity, and (c) compute observed within-person (as well as between-person) reliability from collected within-person research datasets. We hope these much-needed evidence-based guidelines and practical tools will help enhance within-person research in organizational studies.
Rights
© 2022 Springer
Locate the Document
DOI
10.1007/s10869-022-09803-5
Persistent Identifier
https://archives.pdx.edu/ds/psu/37369
Citation Details
Yang, L. Q., Wang, W., Huang, P. H., & Nguyen, A. (2022). Optimizing Measurement Reliability in Within-Person Research: Guidelines for Research Design and R Shiny Web Application Tools. Journal of Business and Psychology, 1-16.