Many contests were annually held in hospitals, using summed scores directly to rank performances of examinees. In contrast to classical test theory, Rasch(1960) analysis with the software Facets(Linacre,2006) was implemented in this study, detecting judges fair or not in a contest selected the best nurse head, to examine whether items or judges meet the requirement of unidimensionality and then to estimate the abilities of examinees after removing unexpected items and unfair judges.
An annual contest selected the best nurse head was held at an academic medical center in spring 2006. Four nurse superintendents as judges appraised 10 candidates with force ranking scores, coding 9 for the most excellent and 0 the poorest, across 7 indicators. Five approaches were proceeded to (1) examine data fitting the Rasch model, (2) evaluate impacts while removing unexpected data, (3) distinguish changes before and after using the fitted data, (4) detect underlying latent characteristics of the awarded nurse head, and (5)summary results of this achievement contest.
The results showed (1) the item 7(whether fully execute superintendents’ occasional tasks and commands) and the judge 3 should be removed from the assessment lists due to misfitting Rasch model’s expectation. (2) the most excellent performance were shown to candidate 5 and 10 in management and education respectively, poorest to candidate 4 in communication and coordination, and data distortion scoring onto judge 3. (3)Leadership and education, statistically significantly differentiated by linear regression, were suggested to be much more emphasized for would-be candidates pursuing the title of the best nurse head in the next year.