Generalized Linear Latent Variables Models (GLLVM) enable the modeling of relationships between manifest and latent variables, where the manifest variables are distributed according to a distribution of the exponential family (e.g. binomial or normal) and to the multinomial distribution (for ordinal manifest variables). These models are widely used in social sciences. To test the appropriateness of a particular model, one needs to define a Goodness-of-fit test statistic (GFI). In the normal case, one can use a likelihood ratio test or a modified version proposed by citeN{SaBe:01} (S&B GFI) that compares the sample covariance matrix to the estimated covariance matrix induced by the model. In the binary case, Pearson-type test statistics can be used if the number of observations is sufficiently large. In the other cases, including the case of mixed types of manifest variables, there exists GFI based on a comparison between a pseudo sample covariance and the model covariance of the manifest variables. These types of GFI are based on latent variable models that suppose that the manifest variables are themselves induced by underlying normal variables (underlying variable approach). The pseudo sample covariance matrices are then made of polychoric, tetrachoric or polyserial correlations. In this article, we propose an alternative GFI that is more generally applicable. It is based on some distance comparison between the latent scores and the original data. This GFI takes into account the nature of each manifest variable and can in principle be applied in various situations and in particular with models with ordinal, and both discrete and continuous manifest variables. To compute the value associated to our GFI, we propose a consistent resampling technique that can be viewed as a modified parametric bootstrap. A simulation study shows that our GFI has good performance in terms of empirical level and empirical power across different models with different types of manifest variables.