In the reflective model, the attribute is seen as the common cause of observed scores (e.g. depression causes people feeling sad). In the formative model, observed scores define or determine the attribute (e.g. depression occurs when people feel sad a lot).
Reflective models are presented as measurement models. A latent variable is introduced to account for the covariance between other variables. In the reflective model, variables are regarded as exchangeable save for measurement parameters (e.g. reliability) and correlations between the variables are spurious in the reflective model. The correlation only exists because variables are related and might be the same thing.
Formative models differ from reflective models because the variables are not exchangeable. This is because variables are hypothesised to capture different aspects of the same construct. There is also no assumption about whether the variables should correlate.
There are three problems with the conceptualization of reflective and formative models:
The network model states that observable variables of latent variables should be seen as autonomous causal entities in a network of dynamical systems.
This bundle contains everything you need to know for the fifth interim exam for the course "Scientific & Statistical Reasoning" given at the University of Amsterdam. It contains both articles, book chapters and lectures. It consists of the following materials:
...This bundle contains all the summaries for the course "Scientific & Statistical Reasoning" given at the University of Amsterdam. It contains the following articles:
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