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