Enter code here...
item~~.001*item
lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -1.437742e-16) is smaller than zero. This may be a symptom that
the model is not identified.lavaan WARNING: some estimated ov variances are negative
[1] 3.5744820051 0.4209028657 0.2839836555 0.2477959891 0.1991937449 0.0898171920 0.0134621281 0.0005180068
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Hi Jeremey,
… the problem was that the error variance of the item was negative and its standardized loading was greater than 1 (1.022, not much, and it is possible that std item’s loading can be greater than 1). The idea was to thus fix the variance to a small number (.001). With ML estimation, the standardized factor loading is less than 1 (.99). But with WLSMV, no changes were observed in the std. loading who has the exact same value before fixing the variance. Any clues why?
Best,
João
With ML estimation, the standardized factor loading is less than 1 (.99). But with WLSMV, no changes were observed in the std. loading who has the exact same value before fixing the variance. Any clues why?
standardizedSolution(fit)
lavaan WARNING: the optimizer warns that a solution has NOT been found!
After changing the parameterization to "theta" I get:
lavaan WARNING: the optimizer warns that a solution has NOT been found!
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Sure, altough I must inform that my sample is small (N = 187)
model <- ' F1=~ item1 + item2 + item3F2=~ item4 + item5 + item6
F3=~ item7 + item8
F4=~ item9 + item10
F5=~ item11 + item12
F6=~ item13 + item14
F7=~ item15 + item16
Factor_2L =~ F1+F2+F3F4+F5+F6+F7'
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