Moving Beyond Main Effects: A Data Analytic Strategy for Testing Complex Theories of Clinical Phenomena

Alissa Russell, Gerald J. Haeffel, Benjamin L. Hankin, Scott E. Maxwell, Robert A. Perera

Research output: Contribution to journalArticlepeer-review

Abstract

The goal of this study was to advance a data analytic strategy for testing sophisticated models of clinical phenomena. We illustrate this method by testing a prominent and highly specific cognitive model of depression (Abramson et al., 1989). Specifically, we used multilevel modeling (MLM) to test the entire sequence featured in the hopelessness theory. The study used a daily diary design with a sample of undergraduates (n = 210). To our knowledge, this is the first study to use multilevel modeling with multiple waves of data to test a model with two mediators and a moderator. Results of analyses provided strong support for the MLM strategy and offer a concrete example for how to test complex theories of clinical phenomena.

Original languageEnglish (US)
Pages (from-to)385-397
Number of pages13
JournalClinical Psychology: Science and Practice
Volume21
Issue number4
DOIs
StatePublished - Dec 1 2014
Externally publishedYes

Keywords

  • Clinical phenomena
  • Cognitive vulnerability
  • Data analysis
  • Hopelessness theory
  • Multilevel modeling

ASJC Scopus subject areas

  • Clinical Psychology

Fingerprint Dive into the research topics of 'Moving Beyond Main Effects: A Data Analytic Strategy for Testing Complex Theories of Clinical Phenomena'. Together they form a unique fingerprint.

Cite this