Different kinds of questionnaires are usually applied in a field of social sciences. The basic interest of these studies is often to reveal the underlying construct which is measured by different set of specific questions. Usually the construct of measurement instrument is examined using latent variable model e.g. exploratory or confirmatory factor analysis, (multidimensional) item response models (Bock, Gibbons & Muraki, 1988), latent class or latent profile analysis (Goodman, 1974), depending on the measurement level of observed variables and the assumptions of underlying model. Although the latent variable models are well known and usually part of basic methodological curriculum, many still struggle with estimation problems e.g. Haywood cases and non-identifiability. To identify the cause of these problems one must carefully examine the huge number of different result matrixes, which can be rather difficult in most commercial software. At the same time different models can lead to similar results and its often difficult to choose which model is best for specific problem or data. In this presentation Ill show how Survo-R environment (Sund, Vehkalahti & Mustonen, 2014) might help researcher to solve different computational issues, which usually arise when one is trying to analyze real word data leading to a better understanding of underlying model. Survo-R is a powerful editor of an R-programing language (R Core Team, 2013) which is of the most widely used statistical environment.