Beyond Black and White? True and False? The Hidden Information of "Neutral" Options in Surveys

黑白與對錯之外？問卷中間選項的隱藏資訊

10.6338/JDA.201108_6(4).0006

林定香(Ting-Hsiang Lin)

知識量表 ； 試題反應理論 ； 中間選項 ； 名義反應模式 ； Knowledge Scale ； Item Response Theory ； Neutral Response Options ； Nominal Response Mode

Journal of Data Analysis

6卷4期（2011 / 08 / 01）

91 - 110

英文

本研究於利用試題反應理論探討問卷中反應選項的回答型態的訊息。我們利用基因知識量表來說明中間選項的隱藏資訊。量表題目皆有正確答案，但皆以態度量表的形式呈現。結果顯示，中間選項可能代表不同的母體，因為它們選項反應曲線顯示出各種不同知識程度的受試者對中間選項不同的回答型態。低知識程度的受試者比高知識程度的受試者有較高機率回答「不知道」。相反地，隨著知識程度的增加，受試者有較高機率選澤「不理解題意」，而中知識程度的受試者有較高機率選澤「不確定」。知識量表的中間選項提供受試者在對錯之外一個另類選擇。如何分析有賴於量表所欲測量的意涵。研究者應該在將所有中問選項合併為一之前先檢查資料，並應謹慎解釋結果。

The objective of this study is to examine the response options of survey in terms of their answer patterns based on item response theory (IRT). We particularly examined and compared the ”neutral” (or ”middle”) options, including ”unsure”, ”I don't understand the meaning of the statement”, and ”don't know” options. The study use a genetics knowledge scale as an example. The questions have correct answers just like a test but the questions were written in an attitude format. We used IRT software Multilog and normal response model was fitted to the data.The results showed the ”neutral” options may represent distinct populations since their option characteristics curves show different patterns at variously fixed knowledge level. When comparing the three ”neutral” options, it indicates respondents of lower knowledge level have a higher probability of answering ”don't know” than respondents with a higher knowledge level. Conversely, with the increase of the knowledge level, respondents are more likely to answer ”I don't understand the meaning of the statement”. The respondents of middle knowledge level have a higher probability of answering ”unsure” than respondents of the two ends.In general, these options are often used as middle responses and provide respondents alternatives besides true and false on a knowledge scale. How these options should be analyzed depends much on what the scale intend to measures. Researchers should verify their data before combining them into one group and use extra caution when interpreting the results.

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