The use of suitable statistical models can help us to improve the understanding of species-habitat relationships. To identify the current most popular statistical methods, we surveyed the papers published in 10 SCI (Science Citation Index) journals aimed at habitat selection and journals of mainland China over the last 10 years (2003-2012). Of 30 methods used in 177 papers published in SCI journals, Logistic regression, Resource selection function, Compositional analysis, Generalized linear model, Multivariate analysis of variance, Euclidean distance-based approach, Generalized linear mixed model, Ecological-niche factor analysis, Individual-based modeling and Canonical correspondence analysis were most widely used ones. The Generalized linear model, Logistic regression, Multivariate analysis of variance and Euclidean distance-based approach were four methods very flexile when dealing with ecology data, however, the results might be lack of ecologically meanings. Resource selection function and Ecological-niche factor analysis provide us the concept which can lead to unified theory for the analysis and interpretation of habitat selection data. The Individual-based approach is a bottom-up approach which will never lead to theories at the system level. We surveyed 232 papers from mainland China, and found 19 methods were used, and the Principal component analysis, Mann-Whitney U test, Student's t test, Chi-square test, Discriminant analysis, Analysis of Variance, Vanderploeg and Scavia's first selection index, Vanderploeg and Scavia's second selection index, Logistic regression, Kruskal-Wallis H test and multiple regression analysis were most widely used.