4784 - Leveraging Meta-Analytically Synthesized Data to Provide Large-Sample Tests of Established and Novel Integrated Theoretical Models in Health Behavior Research

Session: S.01 - ADVANCES AND NOVEL APPLICATIONS OF INTEGRATED MODELS FOR CHANGING BEHAVIOUR
AUTHORS:
Hagger Martin (Department of Psychological Sciences, University of California, Merced; Health Sciences Research Institute, University of California, Merced; Faculty of Sport and Health Sciences, University of Jyväskylä; School of Applied Psychology, Griffith University) , Hamilton Kyra (School of Applied Psychology, Griffith University; Health Sciences Research Institute, University of California, Merced; Faculty of Sport and Health Sciences, University of Jyväskylä ~ Jyväskylä ~ Finland)
Abstract text:
Testing the predictive validity of theories in health behavior research is highly salient to the development of an evidence base of potentially modifiable health behavior determinants and the processes by which they relate to health behavior. Such evidence may signal potential targets for efficacious behavior change interventions to promote health. Such tests typically involve evaluating the fit of models specifying theory-stipulated nomological networks among psychological constructs and outcomes with observed data in health behavior contexts. Analytic techniques combining meta-analysis and multivariate analyses such as structural equation modeling affords researchers opportunity to conduct large-sample tests of such theoretical networks using synthesized data across multiple studies. Importantly, the techniques also provide opportunity to test predictions of unique theoretical perspectives, including integrated theoretical models, that draw constructs and process-related predictions from multiple perspectives to arrive at comprehensive accounts of health behavior. Such models, for example, incorporate multiple processes beyond those specified in typical social cognition and motivational theories, such as volitional and implicit processes. In this paper, we provide a non-technical overview of this approach and outline its advantages including: leveraging of the large sample size from multiple data sources to provide robust point and variability estimates of theory effects with high statistical power; capacity to provide overall evaluations of existing and new theoretical models across extant research and guideline ranges for theory effects in subsequent tests; and ability to test mechanistic effects, namely, mediation effects and effects of study- and sample-level moderator variables on theory effects. To illustrate our points, we outline a series of examples that illustrate the application of this approach to test existing and novel integrated theoretical models in health behavior research. We conclude by summarizing some of the limitations of this approach and recommend future innovations that may advance theoretical knowledge in the field.