ISSN 0439-755X
CN 11-1911/B
主办:中国心理学会
   中国科学院心理研究所
出版:科学出版社

心理学报 ›› 2005, Vol. 37 ›› Issue (04): 555-560.

• • 上一篇    下一篇

基于人工神经网络的一种效度凭证求取方法

余嘉元   

  1. 南京师范大学心理学系,南京210097
  • 收稿日期:2004-04-28 修回日期:1900-01-01 出版日期:2005-07-30 发布日期:2005-07-30
  • 通讯作者: 余嘉元

THE STUDY ON ARTIFICIAL NEURAL NETWORKS BASED

Yu Jiayuan   

  1. Department of Psychology, Nanjing Normal University, Nanjing 210097, China
  • Received:2004-04-28 Revised:1900-01-01 Published:2005-07-30 Online:2005-07-30
  • Contact: Yu Jiayuan

摘要: 针对基于统计学的效度凭证求取方法所存在的问题,提出了基于人工神经网络的效度凭证求取方法。蒙特卡罗模拟实验和对实际数据的分析表明,当测验分数和效标分数为单变量且非线性关系时,或者测验分数和效标分数为多变量时,运用神经网络方法可以比统计学方法更好地求取心理测验效度的基于和其他变量关系的凭证。

关键词: 心理测量, 人工神经网络, 效度, 蒙特卡罗模拟方法

Abstract: Because there were some shortcomings that validity argument was obtained with statistics. The artificial neural networks based validity argument obtain method was introduced. 21 Monte Carlo simulation experiments were conducted, including the relationship between single independent variable and dependent variable were linear or nonlinear, the relationship between multi- independent variable and dependent variable were linear or nonlinear, the relationship between independent and dependent variable were random, the relationship between variables were affected by random variable with different degrees. Both standard normal distribution and uniform distribution for the independent variables were in each experiment. The actual psychological measurement data were analyzed with statistics and artificial neural networks. The results showed that the neural networks method was better than the statistical method for obtain the validity argument, when the relationship between the test scores and criterion scores was nonlinear or the test scores and criterion scores were multi-variables.

Key words: psychological measurement, artificial neural networks, validity, Monte Carlo simulation

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