The forward search is an effective and efficient approach when analyzing non-survey data to detect a group of influential observations which affect regression estimates greatly if they were removed from the model fitting. It has the advantages of avoiding masked effects among the outliers, as well as automatically identifying influential points. Compared to multiple-case deletion diagnostic statistics, this method reduces computational burden, especially when the dataset is very large. In this research we adapted the forward search to linear regression diagnostics for some types of complex survey data. While keeping the existing advantages of this method, we incorporate sample weights and the effects of stratification. A case study is conducted to illustrate the advantages of the adapted method.