Covariance-Based Structural Equation Modeling in the<i>Journal of Advertising</i>: Review and Recommendations

TLDR

This article reviews the use of covariance‑based structural equation modeling in the Journal of Advertising from its first application in 1972 to 2015. The authors examined 111 JA articles, summarizing key methodological issues in confirmatory factor analysis, causal modeling, multiple‑group analysis, reporting, guidelines, and terminology variations. The review shows that SEM has advanced conceptual, empirical, and methodological aspects of advertising research and offers researchers a concise guide to best practices and core distinctions of covariance‑based analysis.

Abstract

In this article, we review applications of covariance-based structural equation modeling (SEM) in the Journal of Advertising (JA) starting with the first issue in 1972. We identify 111 articles from the earliest application of SEM in 1983 through 2015, and discuss important methodological issues related to the following aspects: confirmatory factor analysis (CFA), causal modeling, multiple group analysis, reporting, and guidelines for interpretation of results. Moreover, we summarize some issues related to varying terminology associated with different SEM methods. Findings indicate that the use of SEM in the JA contributes greatly to conceptual, empirical, and methodological advances in advertising research. The assessment contributes to the literature by offering advertising researchers a summary guide to best practices and a reminder of the basics that distinguish the powerful and unique approach involving structural analysis of covariances.