我的問題內容大致如下:
... My question is about how to deal with different numbers of response categories in a given polytomous data set. In my questionnaire, there are six items scored on a 3-level scale (0, 1, 2 points) and two items are scored on a 4-level scale (0, 1, 2, 3points). I would like to confirm if it is still all right to use Mokken scale analysis to analyze my data. Should I collapse the response categories, e.g. 4-level scale into 3-level scale to make the same format?
There was inconclusive evidence on multiple studies for whether or not it is okay to use different numbers of response categories in a give set. ....
Dr. van der Ark的回覆如下:
"The problem with different numbers of response categories is that that is unclear what the scale score means. Scores 0, 1, and 2 in the first set of items are not equivalent to the scores 0, 1, and 2 in the second set of items because in the latter set there is also a category 3.
So I would advise against having scales consisting of items with different numbers of categories, especially for investigating IIO where the interest lies in ordering of items by the mean item score. You may collapse the response categories if you believe that is meaningful; that is, if you believe that by collapsing the scores in the first set of items the scores in both sets of items are comparable."
就Dr. van der Ark的回覆,是建議使用collapsing the scores,但對於我們來說,
項目選項的meaning可能就會不見,因為目前的選項對於item來說有其意義 ; 但從另一個
角度來看,如果說軟體無法(或本意並無)可計算不同response categories,那目前出來
的結果該如何解釋呢?!! 思考中~~
項目選項的meaning可能就會不見,因為目前的選項對於item來說有其意義 ; 但從另一個
角度來看,如果說軟體無法(或本意並無)可計算不同response categories,那目前出來
的結果該如何解釋呢?!! 思考中~~
主要您嘗試,就有機會跟大師交流!!
回覆刪除這樣看起來Dr.van der Ark還是覺得採用相同的scoring catecories較好,對於得分較容易解釋。
回覆刪除有4分量尺的項目,除了因為個案覺得較容易評量外,是否還有其他原因要分為0-1-2-3呢?