[R] Results of CFA with Lavaan

[R Help]

Yes, that is the difference.  For the last SEM I built I fixed the
factor variances to 1, and I think that's what I want to do for the
CFA I'm doing now.  Does that make sense for a CFA?

I'll try figuring out how to do that with lavaan later, but my model
takes so long to fit that I can't try it right now.

Thanks,
Sam

On Wed, Jun 8, 2011 at 5:58 PM, John Fox <jfox at mcmaster.ca> wrote:
> Dear Sam,
>
> In each case, the first observed variable is treated as a "reference
> indicator" with its coefficient fixed to 1 to establish the metric of the
> corresponding factor and therefore to identify the model. If you didn't do
> the same thing (or something equivalent, such as fixing the factor variances
> to 1) in specifying the model to sem::sem(), that might account for the
> problems you encountered.
>
> Best,
>  John
>
> --------------------------------
> John Fox
> Senator William McMaster
>  Professor of Social Statistics
> Department of Sociology
> McMaster University
> Hamilton, Ontario, Canada
> http://socserv.mcmaster.ca/jfox
>
>
>
>> -----Original Message-----
>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
>> On Behalf Of R Help
>> Sent: June-08-11 4:15 PM
>> To: r-help
>> Subject: [R] Results of CFA with Lavaan
>>
>> I've just found the lavaan package, and I really appreciate it, as it
>> seems to succeed with models that were failing in sem::sem.  I need some
>> clarification, however, in the output, and I was hoping the list could
>> help me.
>>
>> I'll go with the standard example from the help documentation, as my
>> problem is much larger but no more complicated than that.
>>
>> My question is, why is there one latent estimate that is set to 1 with
>> no SD for each factor?  Is that normal?  When I've managed to get
>> sem::sem to fit a model this has not been the case.
>>
>> Thanks,
>> Sam Stewart
>>
>> HS.model <- ' visual  =~ x1 + x2 + x3
>>               textual =~ x4 + x5 + x6
>>               speed   =~ x7 + x8 + x9 '
>> fit <- sem(HS.model, data=HolzingerSwineford1939) summary(fit,
>> fit.measures=TRUE) Lavaan (0.4-8) converged normally after 35 iterations
>>
>>   Number of observations                           301
>>
>>   Estimator                                         ML
>>   Minimum Function Chi-square                   85.306
>>   Degrees of freedom                                24
>>   P-value                                        0.000
>>
>> Chi-square test baseline model:
>>
>>   Minimum Function Chi-square                  918.852
>>   Degrees of freedom                                36
>>   P-value                                        0.000
>>
>> Full model versus baseline model:
>>
>>   Comparative Fit Index (CFI)                    0.931
>>   Tucker-Lewis Index (TLI)                       0.896
>>
>> Loglikelihood and Information Criteria:
>>
>>   Loglikelihood user model (H0)              -3737.745
>>   Loglikelihood unrestricted model (H1)      -3695.092
>>
>>   Number of free parameters                         21
>>   Akaike (AIC)                                7517.490
>>   Bayesian (BIC)                              7595.339
>>   Sample-size adjusted Bayesian (BIC)         7528.739
>>
>> Root Mean Square Error of Approximation:
>>
>>   RMSEA                                          0.092
>>   90 Percent Confidence Interval          0.071  0.114
>>   P-value RMSEA <= 0.05                          0.001
>>
>> Standardized Root Mean Square Residual:
>>
>>   SRMR                                           0.065
>>
>> Parameter estimates:
>>
>>   Information                                 Expected
>>   Standard Errors                             Standard
>>
>>
>>                    Estimate  Std.err  Z-value  P(>|z|) Latent variables:
>>   visual =~
>>     x1                1.000
>>     x2                0.554    0.100    5.554    0.000
>>     x3                0.729    0.109    6.685    0.000
>>   textual =~
>>     x4                1.000
>>     x5                1.113    0.065   17.014    0.000
>>     x6                0.926    0.055   16.703    0.000
>>   speed =~
>>     x7                1.000
>>     x8                1.180    0.165    7.152    0.000
>>     x9                1.082    0.151    7.155    0.000
>>
>> Covariances:
>>   visual ~~
>>     textual           0.408    0.074    5.552    0.000
>>     speed             0.262    0.056    4.660    0.000
>>   textual ~~
>>     speed             0.173    0.049    3.518    0.000
>>
>> Variances:
>>     x1                0.549    0.114    4.833    0.000
>>     x2                1.134    0.102   11.146    0.000
>>     x3                0.844    0.091 9.317 0.000
>>     x4                0.371    0.048    7.778    0.000
>>     x5                0.446    0.058    7.642    0.000
>>     x6                0.356    0.043    8.277    0.000
>>     x7                0.799    0.081    9.823    0.000
>>     x8                0.488    0.074    6.573    0.000
>>     x9                0.566    0.071    8.003    0.000
>>     visual            0.809    0.145    5.564    0.000
>>     textual           0.979    0.112    8.737    0.000
>>     speed             0.384    0.086    4.451    0.000
>>
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