摘要：The linear logistic test model (LLTM; Fischer, 1973) has been applied to a wide
variety of new tests. When the LLTM application involves item complexity
variables that are both theoretically interesting and empirically supported,
several advantages can result. These advantages include elaborating construct
validity at the item level, defining variables for test design, predicting
parameters of new items, item banking by sources of complexity and providing a
basis for item design and item generation. However, despite the many advantages
of applying LLTM to test items, it has been applied less often to understand the
sources of complexity for large-scale operational test items. Instead,
previously calibrated item parameters are modeled using regression techniques
because raw item response data often cannot be made available. In the current
study, both LLTM and regression modeling are applied to mathematical problem
solving items from a widely used test. The findings from the two methods are
compared and contrasted for their implications for continued development of
ability and achievement tests based on mathematical problem solving items.
关键词：Mathematical reasoning, LLTM, item design, mathematical problem solving
