Using Restricted Latent Class Models to Map the Skill Structure of Achievement Items

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Abstract
This paper presents a new method for using certain restricted latent class models, referred to as binary skills models, to determine the skills required by a set o f test items. The method is applied to reading achievement data from a nationally representative sample o f fourth‐grade students and offers useful perspectives on test structure and examinee ability, distinct from those provided by other methods o f analysis. Models fitted to small, overlapping sets o f items are integrated into a common skill map, and the nature o f each skill is then inferred from the characteristics o f the items for which it is required. The reading comprehension items examined conform closely to a unidimensional scale with six discrete skill levels that range from an inability to comprehend or match isolated words in a reading passage to the abilities required to integrate passage content with general knowledge and to recognize the main ideas o f the most difficult passages on the test.