If all data is continuous, the default estimator in the lavaan package is maximum likelihood (estimator = "ML"). Alternative estimators available in lavaan are:

Many estimators have ‘robust’ variants, meaning that they provide robust standard errors and a scaled test statistic. For example, for the maximum likelihood estimator, lavaan provides the following robust variants:

For the DWLS and ULS estimators, lavaan also provides ‘robust’ variants: WLSM, WLSMVS, WLSMV, ULSM, ULSMVS, ULSMV. Note that for the robust WLS variants, we use the diagonal of the weight matrix for estimation, but we use the full weight matrix to correct the standard errors and to compute the test statistic.

ML estimation: Wishart versus Normal

If maximum likelihood estimation is used ("ML" or any of its robusts variants), the default behavior of lavaan is to base the analysis on the so-called biased sample covariance matrix, where the elements are divided by $n$ instead of $n-1$. This is done internally, and should not be done by the user. In addition, the chi-square statistic is computed by multiplying the minimum function value with a factor $n$ (instead of $n-1$). This is similar to the Mplus program. If you prefer to use an unbiased covariance, and $n-1$ as the multiplier to compute the chi-square statistic, you need to specify the likelihood = "wishart" argument when calling the fitting functions. For example:

fit <- cfa(HS.model, 
           data = HolzingerSwineford1939, 
           likelihood = "wishart")
lavaan (0.6-1) converged normally after  35 iterations

  Number of observations                           301

  Estimator                                         ML
  Model Fit Test Statistic                      85.022
  Degrees of freedom                                24
  P-value (Chi-square)                           0.000

The value of the test statistic will be closer to the value reported by programs like EQS, LISREL or AMOS, since they all use the ‘Wishart’ approach when using the maximum likelihood estimator. The program Mplus, on the other hand, uses the ‘normal’ approach to maximum likelihood estimation.

Missing values

If the data contain missing values, the default behavior is listwise deletion. If the missing mechanism is MCAR (missing completely at random) or MAR (missing at random), the lavaan package provides case-wise (or ‘full information’) maximum likelihood estimation. You can turn this feature on, by using the argument missing = "ML" when calling the fitting function. An unrestricted (h1) model will automatically be estimated, so that all common fit indices are available.

Standard errors

Standard errors are (by default) based on the expected information matrix. The only exception is when data are missing and full information ML is used (via missing = "ML"). In this case, the observed information matrix is used to compute the standard errors. The user can change this behavior by using the information argument, which can be set to "expected" or "observed".

If the estimator is simply "ML", you can request robust standard errors by using the se argument, which can be set to "robust.sem", "robust.huber.white" , "first.order" or "bootstrap".
Or simply to "none" if you don’t need them. This will not affect the test statistic. In fact, you can choose the test statistic independently by using the test argument, which can be set to "standard", "Satorra-Bentler", "Yuan-Bentler" or "bootstrap".


There are two ways for using the bootstrap in lavaan. Either you can set se = "bootstrap" or test = "bootstrap" when fitting the model (and you will get bootstrap standard errors, and/or a bootstrap based p-value respectively), or you can you the bootstrapLavaan() function, which needs an already fitted lavaan object. The latter function can be used to ‘bootstrap’ any statistic (or vector of statistics) that you can extract from a fitted lavaan object.