在測驗品質的維護中，差異試題功能（Differential Item Function，DIF）在測驗
公平性與效度上扮演著不可或缺的角色。在過去的文獻中，有關DIF 檢核的研究
已經發展得十分完備。然而在找出DIF 試題之餘，相關領域的學者們亦期待能提
供測驗編製者（領域專家）更多有用的資訊，以提供DIF 試題的修改方向。因此，
DIF 成因的探討也逐漸受到重視。DIF 成因的探討包含了質性與量化兩個不同的取
向。以質性取向來說，專家審查（expert review）為主要探討的方式。在量化取向
上，主要方式為將潛在的DIF 成因納入統計模式中，並對其進行檢定。然而，當
DIF 成因為未知時，則不適合使用上述量化取向之方式探討。因此在本研究中，本
研究提出一套DIF 成因檢核程序，結合DIF 檢核、結構方程模式（Structural Equation
Modeling，SEM）下的多重指標與多重原因模式（Multiple Indicators and Multiple
Causes model，MIMIC model）分析、與探索性因素分析（Exploratory Factor Analysis，
EFA）與專家審查等方法，探討差異試題功能的成因。其次，以兩個模擬研究來探
討影響DIF 成因檢核程序在DIF 成因數量估計上的命中率（Hit Rate，HR）與DIF
試題分類至所屬DIF 成因的逐題正確性（Per Element Accurate，PEA）的變項。最
後，以2011 年TIMSS 八年級數學科之資料來演示DIF 成因檢核程序，提供應用
探索性因素分析探討DIF 成因的例子。
研究結果顯示，在各個可能影響 DIF 成因檢核程序之變項上，大部份情境皆
提供不錯之HR；且如能正確界定DIF 成因之數量，即能有效找出DIF 試題所屬之
DIF 成因。值得注意的是當不同DIF 成因之相關為0.6 時，DIF 成因的數量則有低
估的傾向。除此之外，DIF 檢核的結果亦會造成影響：如正確檢核出DIF 試題的比
例越高、錯誤將乾淨試題（DIF-free）檢核為DIF 試題的比例越低，則HR 與PEA
則越高。透過真實資料的分析，此程序將兩道數學科試題的DIF 以人口金字塔概
念進行解釋，提供了一個DIF 成因的解釋方式。綜合上述結果，本研究建議如在
進行DIF 檢核時找出一組DIF 試題，並無明確收集到可能之DIF 成因時，可利用
DIF 成因檢核程序進一步探討造成DIF 之原因，以期能對DIF 試題，提供更深入
的探討。

Differential Item Function (DIF) plays an important role in testing quality in terms
of fairness and validity. In the literature, methods of DIF assessment have been well
developed. The further researches would focus on the issues about the sources of DIF.
Recently, several qualitative and quantitative approaches for defining DIF sources were
proposed. In the qualitative approach, expert review is one of the most popular method.
In the quantitative approach, the potential DIF source is incorporated into the statistical
model, such as Logistic Regression model, Mixture model, or Mediated MIMIC model,
and is assessed in the statistical model simultaneously. However, this approach is limited
due to the requirement of pre-specified DIF sources which have to be collected in advance.
Therefore, a more flexible method for defining the structure of DIF sources without any
previous information is necessary. The purposes of this study are (a) developing a
procedure to define the sources of DIF (b) investigating independent variables which may
affect the efficacy of this procedure, and (c) demonstrating the procedure on an empirical
data and explaining the sources of DIF. Two simulation studies and an empirical data
analysis were conducted. The performance of the proposed procedure on each
independent variable in both simulation studies was evaluated with Hit Rate (HR) and
Per-Element Accurate (PEA). The results indicated that this procedure provided almost
perfect HR and PEA in most conditions. In addition, the performance of DIF assessment
would positively affect the HR and PEA of defining DIF source. In the analysis of 2011
TIMSS math, the procedure defined the population pyramid content as the DIF source in
2 DIF items. In summary, the procedure can define the DIF source without advance data
collection which previous quantitative approaches did. This study recommends
researchers to apply this procedure to define the DIF source when DIF items are identified
but the DIF source is not obvious.