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Longitudinal Analysis in Occupational Health Psychology: A Review and Tutorial of Three Longitudinal Modeling Techniques
Author(s) -
Liu Yihao,
Mo Shenjiang,
Song Yifan,
Wang Mo
Publication year - 2016
Publication title -
applied psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.497
H-Index - 88
eISSN - 1464-0597
pISSN - 0269-994X
DOI - 10.1111/apps.12055
Subject(s) - psychology , longitudinal data , longitudinal study , applied psychology , data science , management science , computer science , engineering , medicine , data mining , pathology
There is an increasing call for the collection of longitudinal data and the use of longitudinal analysis in occupational health psychology research. Some useful and popular longitudinal analysis techniques include the cross‐lagged model, the latent growth model, and the latent change score model. However, previous reviews and discussions on these modeling techniques are quite generic and often overlook the connections among these techniques. Therefore, in the current article, we first reviewed the three modeling techniques as well as their existing applications in occupational health psychology research. We then present a detailed tutorial regarding how to utilise these techniques to analyze a simulated dataset. Finally, we compare the three techniques and discuss their utility for addressing different research questions in occupational health psychology.