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| Output of a Latent Change Score model with Growth Modeling elements and measurement error |
Can you beat my 10/2 ratio latent/observed variables? Here's a simple example of a model looking at changes in 1 observed outcome only, from Y1 to Y2. The Latent Change Score setup [see an excellent intro here] allows one to answer this question in a paired t-test kind of way, but can also accommodate growth modeling elements [intercept=initial level and slope=constant additive change factor]; plus, one can incorporate measurement error, i.e. strip the true measures of unreliability (assumed to be a 10% for example). This leads to the output above [this is an application using the data mentioned here under 'The paired t-test as a simple latent change score model']
*** By the way, in case you wonder about the meaning of each of those latent variables, here's a similar example of such a struggle to find the hidden side of reality, or what's behind the observed facts:
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