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alternative analytical models, longitudinal study, time-varying variables



  1. Lin, Kuan-Chia
  2. Yan, Chiu-Fang
  3. Cheng, Su-Fen
  4. Gau, Meei-Ling


Background: Traditional statistics in longitudinal data analysis are likely to be insufficient in nursing studies, in which the time varying characteristics of explanatory variables and cumulative effects require additional consideration.


Objectives: The aims of this study were to introduce alternative longitudinal approaches for incorporating time-varying variables and cumulative effects, to discuss their strengths, and to highlight key issues that nursing researchers should recognize before and while undertaking such analyses.


Results: The three alternative models provide differing analytical outcomes based on the research focus. The baseline tracking model was used to estimate the stability effect of an intervention program, detecting risk factors early. The temporal sequence of potential cause and effect was incorporated further in the time-dependent model. The cumulative model was used to explore whether cumulative intervention effects existed.


Conclusion: Nurse researchers should incorporate alternative methods into the longitudinal data analysis tools they commonly use when facing explanatory variables with time variations or cumulative effects on the variable being measured.