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Do the relative merits of path analysts and structure equation modeling outweight their limitiations?
Marketing research departments are often interested in quantifying the relative importance of factors afFecting issues such as brand equity, brand loyalty and customer satisfaction. Two widely used methods in this regard are path analysis (incorporating ordinary least-squares regression) and structural equation modeling (SEM) (typically incorporating maximum likelihood estimation). The latter is considered to be the more "advanced" of the two. But is it?
EXECUTIVE SUMMARY
This article examines path analysis and structural equation modeling when used to model marketing constructs such as brand value and loyalty. The author examines whether these two methods produce similar results when applied to an identical data set and compares and contrasts the relative merits of each. Because of both methods' potential shortcomings, the author says researchers may benefit from alternative research designs that more effectively control for multicolloinearity and other factors that can affect the reliability of path analysis and structural equation model findings.
This article addresses that question by pitting conventional path analysis against SEM and comparing and contrasting the results from both when analyzing the same data set. Additionally, I discuss the relative strengths and weaknesses of each approach and make recommendations to the practitioner. To simplify the discussion, the term oath analysis will always refer to regression-based path analysis, and SEM to LISREL-type structural equation modeling incorporating maximum likelihood estimation procedures.
OVERVIEW
Both methods are forms of causal modeling that examine relationships between and among one or more dependent variables and two or more predictor or independent variables. Examples of dependent variables might be measures of customer satisfaction, loyalty toward a brand or company, brand equity, or perceived value. Independent variables frequently focus on issues relating to brand or company performance such as perceived product quality, service quality, and price competitiveness. Both methods help decision makers understand the relationships between the independent and dependent variables.
Neither path analysis nor SEM are methods for discovering causative relationships. Rather, they are a means by which theoretical relationships can be tested. In applied research, one often sees path analysis employed to test relatively simple relationships such as displayed in Exhibit 1. In contrast, SEM is sometimes referred to as latent-variable analysis because these models...