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      On Nomological Validity and Auxiliary Assumptions: The Importance of Simultaneously Testing Effects in Social Cognitive Theories Applied to Health Behavior and Some Guidelines

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          Abstract

          Tests of social cognitive theories provide informative data on the factors that relate to health behavior, and the processes and mechanisms involved. In the present article, we contend that tests of social cognitive theories should adhere to the principles of nomological validity, defined as the degree to which predictions in a formal theoretical network are confirmed. We highlight the importance of nomological validity tests to ensure theory predictions can be disconfirmed through observation. We argue that researchers should be explicit on the conditions that lead to theory disconfirmation, and identify any auxiliary assumptions on which theory effects may be conditional. We contend that few researchers formally test the nomological validity of theories, or outline conditions that lead to model rejection and the auxiliary assumptions that may explain findings that run counter to hypotheses, raising potential for ‘falsification evasion.’ We present a brief analysis of studies ( k = 122) testing four key social cognitive theories in health behavior to illustrate deficiencies in reporting theory tests and evaluations of nomological validity. Our analysis revealed that few articles report explicit statements suggesting that their findings support or reject the hypotheses of the theories tested, even when findings point to rejection. We illustrate the importance of explicit a priori specification of fundamental theory hypotheses and associated auxiliary assumptions, and identification of the conditions which would lead to rejection of theory predictions. We also demonstrate the value of confirmatory analytic techniques, meta-analytic structural equation modeling, and Bayesian analyses in providing robust converging evidence for nomological validity. We provide a set of guidelines for researchers on how to adopt and apply the nomological validity approach to testing health behavior models.

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          This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years. We focus first on the variety of research designs and substantive issues to which SEM can be applied productively. We then discuss a number of methodological problems and issues of concern that characterize some of this literature. Although it is clear that SEM is a powerful tool that is being used to great benefit in psychological research, it is also clear that the applied SEM literature is characterized by some chronic problems and that this literature can be considerably improved by greater attention to these issues.
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              Bayesian structural equation modeling: a more flexible representation of substantive theory.

              This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximum-likelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford's (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                03 November 2017
                2017
                : 8
                : 1933
                Affiliations
                [1] 1Health Psychology and Behavioural Medicine Research Group, School of Psychology, Faculty of Health Sciences, Curtin University , Perth, WA, Australia
                [2] 2School of Physiotherapy and Exercise Science, Faculty of Health Sciences, Curtin University , Perth, WA, Australia
                Author notes

                Edited by: Tim Bogg, Wayne State University, United States

                Reviewed by: Thomas L. Webb, University of Sheffield, United Kingdom; Jill Ann Jacobson, Queen’s University, Canada; Mark Conner, University of Leeds, United Kingdom

                *Correspondence: Martin S. Hagger, martin.hagger@ 123456curtin.edu.au

                This article was submitted to Personality and Social Psychology, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2017.01933
                5675876
                29163307
                ed63d6f9-fac0-45b0-892c-5ed65513942b
                Copyright © 2017 Hagger, Gucciardi and Chatzisarantis.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 July 2017
                : 19 October 2017
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 79, Pages: 14, Words: 0
                Funding
                Funded by: Tekes 10.13039/501100003406
                Award ID: FiDiPro
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                nomological validity,predictive validity,falsifiability,path analysis,meta-analysis,replication,auxiliary assumptions

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