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

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### Christopher Galgo

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

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?Â

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

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

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