The one of the main purposes of this study was to devisea Korean version of a standardized personality test or school children, based on the California Test of Personality originally developed by L. P. Thorpe and others. The second purpose was to analyze the factorial structure of the test in order to see the possibility of developing a new simplified test. Even though the urgent need for a personality test which can be used for lower grade levels in elementary school was constantly voiced by school teachers and clinicians working with problem children, there has not appeared such a suitable personality test to meet their needs. In order to develop a Korean version of the California Test of Personality (CTP), the test items were translated and some new items were devised to substitute for possible drop outs of items in preliminary try-out of the test. The test was standardized through national sample on the bas is of selected items showing high discriminating power for each of the 12 subscales. And then a statistical method of factor analysis was applied to clarify the factorial structure of the test on subscale level. All of the above analyses were carried out by the computer installed at the computer center, the Korean Institute of Science and Technology. The principal axissolution of factor analysis, one of three main techniques of factor solution, was applied to the data with orthogonal rotation after extraction of major factors The results generally indicated that each of the 12 subscales has relatively a large portion of unique factor which do not share common variances with other scales. Therefore it can be concluded that the result of factor analysis on scale level was not useful in terms of parsimony principle in simplifying the test. Even though the test can be condensed on the basis of 4 major factors, the test will lose its unique portion of information provided by each subscale. It is recommended for further research that factor analysis technique should be applied on item level. It will give a better opportunity to identify a larger portion of common variances explained by small number of common factors.