The purpose of this study was to develop a tool to assess the powerlessness and to measure the powerlessness of elderly. From the result of pre-test, twenty seven items were finally selected to survey the elderly's powerlessness. The questionnaires on the elderly's powerlessness were drafted so that such tool may be evaluated in accordance with the four point Likert Scale. The number of subjects is 1,150 with ages of 60 years or more and who live in a large city, a small and middle-sized city and a rural area. The subjects of the test and retest were 85 elderlies. Collected data were analyzed by utilizing SAS program with Cronbach's alpa and Pearson's correlation, factor analysis method and known group techniques, descriptive statics, t-test and ANOVA. The results from this study were summarized below: 1. When the factor analysis method was applied for validity, the tool for powerlessness of elderly was separated into 5 factors: loss of self-confidence to deal with physical, emotional, social aspects of life; expulsion by others from meaningful human relate; perception that life is meaningless and time passes quickly; falling behind young people; being rejected by other people, having no influence on others. An application of the known group technique showed a significant difference with the result of the degree of elderly's powerlessness tools developed by the two groups, with illness and without. 2. In testing reliability, it was found that coefficient of test-retest was .9435(P<.0001) when the test-retest method was used as a test of stability, and that the alpha coefficient of internal consistency was .9141 over all items within the tool of elderly's powerlessness. 3. In powerlessness of elderly, total mean is 2.8493. And in factor of powerlessness, the highest factor is falling behind young people (M=3.1713), the lowest factor is loss of self- confidence to deal with physical, emotional, social aspects of life(M=2.6080). 4. The results from the test for the degree of powerlessness, according to the subject's demographic variables, showed that there were significant differences between age, sex, marital status, educational level, religion, possession of house, job, monthly pocket money, health status, illness and a place of residence.