An Exponential Decay Model for Mediation

Matthew S. Fritz

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Mediation analysis is often used to investigate mechanisms of change in prevention research. Results finding mediation are strengthened when longitudinal data are used because of the need for temporal precedence. Current longitudinal mediation models have focused mainly on linear change, but many variables in prevention change nonlinearly across time. The most common solution to nonlinearity is to add a quadratic term to the linear model, but this can lead to the use of the quadratic function to explain all nonlinearity, regardless of theory and the characteristics of the variables in the model. The current study describes the problems that arise when quadratic functions are used to describe all nonlinearity and how the use of nonlinear functions, such as exponential decay, address many of these problems. In addition, nonlinear models provide several advantages over polynomial models including usefulness of parameters, parsimony, and generalizability. The effects of using nonlinear functions for mediation analysis are then discussed and a nonlinear growth curve model for mediation is presented. An empirical example using data from a randomized intervention study is then provided to illustrate the estimation and interpretation of the model. Implications, limitations, and future directions are also discussed.

Original languageEnglish (US)
Pages (from-to)611-622
Number of pages12
JournalPrevention Science
Issue number5
StatePublished - Oct 1 2014


  • Exponential decay
  • Longitudinal
  • Mediation

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health


Dive into the research topics of 'An Exponential Decay Model for Mediation'. Together they form a unique fingerprint.

Cite this