Analyzing statistical mediation with multiple informants: A new approach with an application in clinical psychology

Lesther A. Papa, Kaylee Litson, Ginger Lockhart, Laurie Chassin, Christian Geiser

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Testing mediation models is critical for identifying potential variables that need to be targeted to effectively change one or more outcome variables. In addition, it is now common practice for clinicians to use multiple informant (MI) data in studies of statistical mediation. By coupling the use of MI data with statistical mediation analysis, clinical researchers can combine the benefits of both techniques. Integrating the information from MIs into a statistical mediation model creates various methodological and practical challenges. The authors review prior methodological approaches to MI mediation analysis in clinical research and propose a new latent variable approach that overcomes some limitations of prior approaches. An application of the new approach to mother, father, and child reports of impulsivity, frustration tolerance, and externalizing problems (N = 454) is presented. The results showed that frustration tolerance mediated the relationship between impulsivity and externalizing problems. The new approach allows for a more comprehensive and effective use of MI data when testing mediation models.

Original languageEnglish (US)
Article number1674
JournalFrontiers in Psychology
Volume6
Issue numberNOV
DOIs
StatePublished - 2015

Keywords

  • Indirect effects
  • Multimethod design
  • Multiple informants
  • Multiple raters
  • Statistical mediation

ASJC Scopus subject areas

  • Psychology(all)

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