摘要
目前较多使用者在用SPSS软件计算因子分析应用结果时,存在如下问题:变量没有正向(同向)化使变量方向识别出错,因子解释变量不清晰,选取的因子会遗漏变量较多信息,因子命名与正向(同向)化没有标准等,从而影响数据分析结果的客观性。文章列出和增加相应因子分析应用结果的算法与步骤,通过实际数据计算示范,并与旧算法结果比较,给出改进算法与步骤的有效性。
At present, many users have the following problems when calculating the application results of factor analysis with SPSS software: the variable without positive(homodromous) direction, which causes the variable direction identification to err, unclear factor interpreting variables, much omission of the variables information in the selected factors, no standard for factor naming and positive transformation and so on, thus affecting the objectivity of data analysis results. This paper lists and adds the algorithm and steps of the corresponding factor analysis application. The paper also presents actual data calculation demonstration, makes comparison with the results of the former algorithm, and proves the effectiveness of the improved algorithm and its steps.
作者
赵慧琴
朱建平
Zhao Huiqin;Zhu Jianping(Huashang College,Guangdong University of Finance&Economics,Guangzhou 511300,China;School of Management,Xiamen University,Xiamen Fujian 361005,China)
出处
《统计与决策》
CSSCI
北大核心
2019年第20期72-77,共6页
Statistics & Decision
基金
广东省高等教育“创新强校”专项资金资助项目(HS2018cxQx26)
关键词
SPSS
改进因子分析
因子命名和正向化
因子个数
SPSS
improved factor analysis
factor naming and forward transformation
number of factors