Multiple Group Confirmatory Factor Analysis, part 1

Several techniques have been proposed to test measurement equivalence. Multiple group confirmatory factor analysis (MGCFA) [Jör71] is one of the most popular techniques to assess measurement equivalence [Bil03] [Byr89] [Ren98] [Ste98]. MGCFA is a quite straightforward extension from conventional confirmatory factor analysis (CFA). In CFA, observed items are considered to be indicators of an unobserved -or latent- concept. These indicators are imperfect, in the sense that, besides measuring the intended concepts, they are also affected by measurement error. Systems of equations are used to describe the relations between observed items and the latent concepts these items are supposed to measure. In our case, we have three items measuring one latent concept, ‘anti-immigration attitudes’. More in general, let us assume that we have p items measuring m latent variables. The observed items scores xi (i = 1,..,p) can then be written as linear functions of latent variables ξj (j = 1,...,m):

x = τ + Λξ + δ(1)

In expression (1), x refers to a p*1 vector containing the observed item scores. This vector is modelled as the sum of three components. Λξ is the product of a p*m matrix containing the factor loadings (Λξ) and an m*1 vector with the latent variable scores (ξ). The factor loadings can be seen as the slopes of a regression of xi on ξj. τ is a p*1 vector with the intercepts of the functions. These intercepts refer to the expected value of the observed items when the latent variable score is equal to zero. Finally, δ is a p*1 vector containing stochastic error terms that are assumed to follow a multivariate normal distribution and to have the expected value 0.

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