When and how to use confirmatory composite analysis (CCA) in second language research

Researchers in second language (L2) and education domain use different statistical methods to assess their constructs of interest. Many L2 constructs emerge from elements/parts, i.e., the elements define and form the construct and not the other way around. These constructs are referred to as emergent variables (also called components, formative constructs, and composite constructs). Because emergent variables are composed of elements/parts, they should be assessed through confirmatory composite analysis (CCA). Elements of emergent variables represent unique facets of the construct. Thus, such constructs cannot be properly assessed by confirmatory factor analysis (CFA) because CFA and its underlying common factor model regard these elements to be similar and interchangeable. Conversely, the elements of an emergent variable uniquely define and form the construct, i.e., they are not similar or interchangeable. Thus, CCA is the preferred approach to empirically validate emergent variables such as language skills L2 students’ behavioral engagement and language learning strategies. CCA is based on the composite model, which captures the characteristics of emergent variables more accurately. Aside from the difference in the underlying model, CCA consists of the same steps as CFA, i.e., model specification, model identification, model estimation, and model assessment. In this paper, we explain these steps. and present an illustrative example using publicly available data. In doing so, we show how CCA can be conducted using graphical software packages such as Amos, and we provide the code necessary to conduct CCA in the R package lavaan.