Please help me. Can I use a vcv matrix of factor scores as input to lavaan?

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Christopher Galgo

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Jul 14, 2019, 10:33:29 AM7/14/19
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I have a covariance matrix from factor scores and I want to use that as input for my analysis. 

Firstly, I tested my model in lavaan and I received a lavaan warning saying : some esimated ov variances are negative / covariance matrix of latent variables is not positive definite; so what I did is I tried to do teh steps outlined in Roseel on Factor Score Regression. I did factor score analysis first and produced a vcv matrix out of the factor score loadings. To be honest after this I just dont know how to proceed in R and I do not know if this will produce anything but I hope someone can help me how to move forward. I was wondering if anyone can help me if this still produce nothing on how does one do Croon's correction in R? I have limited statistical background and I am trying to understand what they mean by "TRUE" latent variables? 

Moreover, I was wondering if not having the same amount of factor affects anything in my model? Thank you. 

So this was the covariance matrix I produced. 
              bc1                        bc2                 dv1               dv2              fc1                  fc2.     
bc1    0.887552662 0.004782191  0.06123076  0.02554806 0.218898581  0.13123927
bc2    0.004782191 0.994807132  0.28404452  0.08107698 0.162241929  0.28466757
dv1    0.061230755 0.284044520  0.77432820  0.14407436 0.232051684  0.07219528
dv2    0.025548063 0.081076979  0.14407436  0.62335210 0.013623666  0.20646870
fc1    0.218898581 0.162241929  0.23205168  0.01362367 0.756387914  0.04228952
fc2    0.131239270 0.284667570  0.07219528  0.20646870 0.042289517  0.98754576
info1  0.081799350 0.358589395  0.30719286  0.35342787 0.043581747  0.34527086
sc1    0.143758352 0.114136844  0.14158094 -0.05666471 0.263336420 -0.12509480
so1    0.002854240 0.289255740  0.33211637  0.20766151 0.003479363  0.28322543
vs1    0.234104713 0.086719244 -0.04805958 -0.03642953 0.041590666  0.09575327
vs2   -0.071045262 0.324794629  0.30728513  0.19251651 0.119356966  0.23614446
vs3    0.309499653 0.176341682 -0.01385311  0.20985285 0.135037334  0.16708045

             info1                    sc1              so1                   vs1                    vs2                vs3
bc1    0.08179935  0.14375835  0.002854240  0.2341047127 -0.0710452620  0.309499653
bc2    0.35858939  0.11413684  0.289255740  0.0867192443  0.3247946289  0.176341682
dv1    0.30719286  0.14158094  0.332116373 -0.0480595837  0.3072851332 -0.013853110
dv2    0.35342787 -0.05666471  0.207661513 -0.0364295293  0.1925165138  0.209852848
fc1    0.04358175  0.26333642  0.003479363  0.0415906660  0.1193569658  0.135037334
fc2    0.34527086 -0.12509480  0.283225433  0.0957532652  0.2361444610  0.167080450
info1  0.91411023 -0.09915263  0.496686186  0.0509772815  0.2853273889  0.073079203
sc1   -0.09915263  0.99501013 -0.029381342  0.2296454833  0.0375761612  0.048804598
so1    0.49668619 -0.02938134  0.804089874 -0.0402545871  0.1527907735  0.140742117
vs1    0.05097728  0.22964548 -0.040254587  0.9641480486 -0.0001142417  0.011572463
vs2    0.28532739  0.03757616  0.152790774 -0.0001142417  0.9950109643 -0.001316469
vs3    0.07307920  0.04880460  0.140742117  0.0115724628 -0.0013164690  0.793047306


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