  Cognitive diagnosis models have been popularly used in fields such as
education, psychology, and social sciences. While parametric likelihood
estimation is a prevailing method for fitting cognitive diagnosis models,
nonparametric methodologies are attracting increasing attention due to their
ease of implementation and robustness, particularly when sample sizes are
relatively small. However, existing clustering consistency results of the
nonparametric estimation methods often rely on certain restrictive conditions,
which may not be easily satisfied in practice. In this article, the clustering
consistency of the general nonparametric classification method is reestablished
under weaker and more practical conditions.
