Abstract: Factor analysis has often been used to simplify and combine (reduce) observed variables in the construction of psychological measurement instruments, and multiple regression has frequently been used in validity studies. One major challenge, however, is that all these variables have to satisfy the requirements for interval scales. Otherwise，new statistical procedures have to be developed.
In artificial intelligence, rough set can be used to reduce attributes and artificial neural networks can also be used to set up relations among the variables in a model. In this study, it was posited that these methods could also be similarly applied to psychological measurement.
Under the rough set theory, there are different discretization methods and algorithms to reduce attributes, it is necessary to find the most suitable way for psychological measurement and to see whether the neural network methodology is better than that of multiple regression in validity studies.
Responses data on the Competency Assessment Questionnaire of 718 civil servants who were not leaders working in a human resource section were obtained. The data were constructed into a rough set decision table with 21 condition attributes and 1 decision attribute. Seven methods, which included Boolean reasoning, Manual, Entropy/MDL, Equal frequency binning, Naive, Semi-naive and SOM, were used in the discretization. A total of 14 different reducts were obtained with the genetic algorithm and the Johnson algorithm. These were checked in validity studies with the probability neural networks method and the hierarchical regression method.
The results showed that reduct D21 was the best among the 14 reducts, which used the Manual method in discretization and the genetic algorithm in reduction. Its correct classification rate was 89.30. In contrast, the average correct classification rate with the probability neural networks was 82.92 which was still higher than the 71.93 of the hierarchical regression method.
The research showed the following findings. (i) In the construction of psychological measurement instruments, if the assumptions to apply factor analysis cannot be satisfied, the rough set methods can be used to reduce observed variables. (ii) The rough set provided several reducts. The best of them could be obtained through the neural networks in validity studies. (iii) In predictive validity studies, a higher classification rate could be obtained with the probability neural networks than with the hierarchical regression method. (iv) Theoretical analysis and empirical study showed that the rough set and neural networks could be applied in psychological measurement

Key words: psychological measurement, rough set, neural networks, competency