Bartlett's test of sphericity tests the hypothesis that your correlation matrix is an identity matrix, which would indicate that your variables are unrelated and therefore unsuitable for structure detection.

Small values (less than 0.05) of the significance level indicate that a factor analysis may be useful with your data.

The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates the proportion of variance in your variables that might be caused by underlying factors.

High values (close to 1.0) generally indicate that a factor analysis may be useful with your data. If the value is less than 0.50, the results of the factor analysis probably won't be very useful.

应该没有问题，适合效度分析。

在这个对话框中选择descriptive，里面选择，kmo的值接近于1，适合做因子分析，Bartlett球度统计量越大越好，其伴随概率<0.05，说明数据适合做因子分析。
以前的文献中写说,KMO在0.9以上，非常合适做因子分析；在0.8-0.9之间，很适合；在0.7-0.8之间，适合；在0.6-0.7之间，尚可；在0.5-0.6之间，表示很差；在0.5以下应该放弃。

可以的。主要还是看KMO和sig的值。