1. Descriptive statistical analysis: This method is used to summarize and describe the main characteristics of data, such as mean, median and standard deviation. It can help researchers understand the basic situation of the data and provide a basis for subsequent inferential statistical analysis.
2. Inference statistical analysis: This method is used to infer the overall characteristics from the sample data. The commonly used statistical methods of inference include t-test, variance analysis, chi-square test and correlation analysis. These methods can help researchers determine the relationship between two or more variables and their influence on dependent variables.
3. Regression analysis: This method is used to study the relationship between one or more independent variables and dependent variables. The commonly used regression analysis methods include linear regression, multiple regression and logistic regression. These methods can help researchers build prediction models and evaluate the influence of independent variables on dependent variables.
4. Cluster analysis: This method is used to group similar objects together. Commonly used clustering analysis methods include hierarchical clustering, K-means clustering and systematic clustering. These methods can help researchers discover structures and patterns in data.
5. Principal Component Analysis (PCA): This method is used to reduce the dimension of data while retaining the most important information in the data. PCA can help researchers reduce the complexity of data and identify key features in data more easily.
6. Factor analysis: This method is used to study the potential relationship between multiple observed variables. Commonly used factor analysis methods include exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). These methods can help researchers discover the potential structure in data and explain the relationship between observed variables.