ABSTRACT
Chapter 9 presents Confirmatory Factor Analysis (CFA), which aims at confirming if a factor solution is robust enough; that is, instead of looking for dimensions that group observable variables, it seeks to confirm if the relationships between the observable variables and their respective factors are robust. Using CFA, it is possible to measure many important latent variables such as loyalty, satisfaction, perceived value, switching intention, and brand image, etc. As an example, what observable variables form loyalty and which one has the greatest importance: repurchase behavior, positive attitude, positive word-of-mouth, or share of wallet? The chapter presents the steps to construct the model, to perform the analysis, and to interpret the results. Furthermore, it includes the approaches to increase model fit. The technique is illustrated with theoretical description, followed by an example with the AMOS commands and the results tables with comments. The chapter also includes exercises, such as a road map to perform the analysis, an interpretative exercise with results tables, and a market context to guide a research design.
