在一組問項有次序性(強弱性)的問卷中，若其議題涉隱私或過於敏感，則容易出現給予正向回答的比例偏低的情況；反之若是高度認同的議題，則容易造成給予正向回答的比例都偏高的情形，這兩種情況都屬於問項回答太過一致的問卷，若直接利用這種問卷來分析或許無法取得有用的資訊。 本研究主旨在於探討利用自動化問項篩選工具(AISP)，針對這種問項太過一致的問卷來篩選出符合Mokken尺度量表的問項時所可能衍生的問題。由於利用自動化問項篩選工具的一個缺失點為，篩選出的量表不一定是最好的量表，其中可能包含不適當的問項。因此我們將透過「違反篩選條件的問項個數和比例」的計算，來針對問項難易分佈偏左(正向回答比例低)、偏右(正向回答比例高)的問卷和問項分佈對稱的情況來進行探討。 模擬結果顯示，當問項難易分佈偏左或偏右時，皆會造成違反篩選條件的問項個數和比例逐漸遞增。此外，若問項設計的鑑別度太低，自動化問項篩選工具幾乎無法有效的篩選出適當的問項。 / In a questionnaire survey, if the items are related to politics or personal privacy, respondents tend to give a negative response to the items. On the other hand, if the items are highly recognized, most of them answer positively to the items. These two are examples that the responses to the items are too consistent. It is apparent that useful information is hard to come by through the use of such kind of survey data. The automated item selection procedure (AISP) is a tool to select items that form a Mokken scale from questionnaire. However, the items selected by AISP is not necessarily the optimal choice. Therefore, the aim in the study was to investigate whether extreme response items would create more problems when using AISP. Number of items which violates the selection rules was based to judge the possible impact. The simulation results showed that number of violations increased when the items were too consistent. Furthermore, items with low discrimination almost led to ineffective items selection.