变量之间提取的公因子方差越大，则表明被公因子解释的能力更强，而提取的公

Extraction larger common factor between the variance of the variables, it indicates the ability to be explained more common factors, and the degree of extraction of variables known factor variance factors are proposed to explain the higher than 70%, thus, good extraction results, the raw data less information loss. Typically for variance contribution rate of not less than 75%, the extraction component factor explained 75% of the total information information. For Eigenvalue factor greater than 1, based on the data analysis software SPSS, finally obtained three factors, as follows:

The greater the variance of the common factor extracted between variables, the greater the ability to be interpreted by the male factor, while the variable factor proposed by the extracted common factor variance is interpreted to a higher than 70%, so the extraction effect is good and the information of the original data loss is less. In general, for a variance contribution rate of no less than 75%, the factor extraction component interpretation information accounts for 75% of the total information. For factors with a feature root greater than 1, data analysis is based on SPSS software, resulting in three factors, as shown in the following table:

The larger the variance of common factors extracted between variables, the stronger the ability to be interpreted by common factors, and the degree of variable factors proposed by the extracted common factor variance to be interpreted is higher than 70%. Therefore, the extraction effect is good, and the information of original data loss is less. Generally speaking, for variance contribution rate of no less than 75%, factor extraction component interpretation information accounts for 75% of the total information. For factors with feature root greater than 1, data analysis is conducted based on SPSS software, and three factors are finally obtained, as shown in the table below:<br>