由優良護理長選拔分析評審給分公平性之研究

The Best Head Nurse Awarded First Prize--Using Rasch Analysis

醫院內許多競技比賽的評審計分，是將評審的分數平均或加總而予排名 成績。本研究克服傳統統計分析的限制，利用現代測驗理論的Rasch 分析 （1960） ，以客觀等距分數分析比賽的成績，並揭示評定指標及評審資料的單 一向度，期以先行除列不符Rasch 模式資料預期的資料，以使競技比賽更為公 平合理。 本研究以某醫學中心2006 年護士節的優良護理長選拔資料，四位督導針 對七項指標，對初選入圍的10位護理長，進行為期一個月的考評活動。利用 Facets 軟體進行9 至O 強迫排名給分的評定量尺模式分析， （1 ）選入符合單一 向度的指標及評審， （2） 功能檢測不符向度分佈資料的影響對象， （3） 分析 刪除不符向度資料的前後排名變化， （4） 評量優勝個人的潛能特質， （5） 檢 視調查分析的整體結果。 結果顯示， （1）指標7 （隨時接受上級指派任務並積極執行）及評審3 的 資料分佈不符單一向度而除列不計， （2） 護理長5 在管理，護理長10 在教育 訓練上表現較佳，護理長4 在溝通協調方面尚需加強，對評審3 要加強對溝通 評分意義與規則之瞭解， （3） 線性迴歸分析出領導及教育訓練是未來角逐年 度優良護理長的關鍵項目。

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 unfair in a contest selected the best head nurse, to examine whether items or judges meet the requirement of unidimensional construct and then to estimate the abilities of examinees after removing unexpected items and unfair judges. An annual contest selected the best head nurse 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 performance items. 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 head nurse, and (5) summarize results of the achievement assessments. 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 list due to misfitting Rasch model's expectation, (2) performances were excellently 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, and (3) items related to leadership and education were statisti-cally significantly differentiated by linear regression to be suggested further emphases placed upon for those would-be candidates pursuing the title of the best head nurse in the next year.