Marketing Chapter 5 Essay

12345 Words Mar 17th, 2012 50 Pages
Examples: Confirmatory Factor Analysis And Structural Equation Modeling

CHAPTER 5

EXAMPLES: CONFIRMATORY FACTOR ANALYSIS AND STRUCTURAL EQUATION MODELING
Confirmatory factor analysis (CFA) is used to study the relationships between a set of observed variables and a set of continuous latent variables. When the observed variables are categorical, CFA is also referred to as item response theory (IRT) analysis (Baker & Kim, 2004; du Toit, 2003). CFA with covariates (MIMIC) includes models where the relationship between factors and a set of covariates are studied to understand measurement invariance and population heterogeneity. These models can include direct effects, that is, the regression of a factor indicator on a covariate in order
The structural model describes three types of relationships in one set of multivariate regression equations: the relationships among factors, the relationships among observed variables, and the relationships between factors and observed variables that are not factor indicators. These relationships are described by a set of linear regression equations for the factors that are dependent variables and for continuous observed dependent variables, a set of censored normal or censored-inflated normal regression equations for censored observed dependent variables, a set of probit or logistic regression equations for binary or ordered categorical observed dependent variables, a set of multinomial logistic regression equations for unordered categorical observed dependent variables, and a set of Poisson or zero-inflated Poisson regression equations for count observed dependent variables. For logistic regression, ordered categorical variables are modeled using the proportional odds specification. Both maximum likelihood and weighted least squares estimators are available. All CFA, MIMIC and SEM models can be estimated using the following special features: • • • • • • • • • • Single or multiple group analysis Missing data Complex survey data Latent variable interactions and non-linear factor analysis using

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