The next section presents the parameter estimates. Consult Hu and Bentler (1999) for fuller details on interpretation. We look for a non-significant \(\chi^2\) test, a RMSEA less than 0.05, CFI/TLI above 0.90 to 0.95, and SRMR less than 0.08. We are then presented with model fit information.
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Output after this warning message may still say convergence was achieved, but should not ever be reported.
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If a positive-definite or other warning message was returned from the sem function, it is a signal that the model is not identified or suitable for the data. Note that there were no warning messages returned from the sem function. # Estimate Std.Err z-value P(>|z|) Std.lv Std.all The lavaan syntax to run the model is the following: model |z|) Std.lv Std.all The model expects that democracy in 1965 will be associated with democracy in 1960 as well as industrialization in 1960. We will also add a latent variable measuring industrialization in 1960 ( \(\xi_1\)). The model will keep both latent variables from the measurement model, which represented democracy measured in 1960 ( \(\eta_1\)) and democracy measured in 1965 ( \(\eta_2\)). Here we are going to move from fitting a measurement model to actually testing structural relationships between variables. The post on CFA using the lavaan package in R described the steps towards fitting and testing the measurement model for the two measures of democracy. The data are then loaded into the environment using the data() function. library(sem) # Warning: package 'sem' was built under R version 3.6.2 library(lavaan)Īll required packages are loaded first with the library() commands.
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The data can be accessed from the built-in Bollen dataset in the sem package.