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Because the 16 constructs were developed a priori, a confirmatory factor analysis (CFA) was used across the 104-scale items in order to verify that each scale item (i.e., manifest indicator) loaded on the latent construct that it was developed to measure.
We used latent class cluster analysis (LCCA) to explain associations between observed manifest indicator variables (clinical observations) through hypothesized underlying unobserved latent variables.
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First researchers wanting to use latent indices instead of manifest indicators need to develop a coherent theoretical framework for the construct they intend to measure.
In Austen's day there were a number of such manifest indicators.
Hence, statistical models that are based on a perfect normal distribution of the manifest indicators may not apply to CSE.
LCA is a method of identifying latent clusters based on probabilistic test models (Lazarsfeld and Henry 1968; McCutcheon 1987) using manifest indicators (Geiser 2013).
We applied item parcel scores as manifest indicators for the two latent variables reflecting the professional knowledge in biochemistry and cell biology.
Therefore, we used subscale or parcel scores as manifest indicators to measure latent variables (e.g., instead of 92, there are just six factor loadings; see Fig. 2; cf. Little et al. 2002).
The internal structural model not only used the latent construct of FWPs but also used a second latent construct to represent organizational attachment in place of the manifest indicators organizational commitment and turnover intention.
For each latent construct, two manifest indicators were taken into account.
In the measurement models, the relation between a composite is defined by one or several manifest indicators that are observed directly in the data.
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