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dc.contributor.author Salemi, Michael K. en_US
dc.contributor.author Tauchen, George en_US
dc.date.accessioned 2010-03-09T15:44:16Z
dc.date.available 2010-03-09T15:44:16Z
dc.date.issued 1980 en_US
dc.identifier.uri http://hdl.handle.net/10161/2088
dc.description.abstract This paper is broadly concerned with problems associated with the use of test score data to infer the relative strength of inputs to the production of learning. It should be of interest to those employing a typical strategy of economic education research: pretesting students, applying some special educational treatment to a subset of students, and posttesting the students. By this strategy the researcher enquires whether the treated group learned more or learned more efficiently. This paper specifically addresses several concerns raised by Thomas Johnson by laying out a set of structural equations and thereby modelling the probability of answering a test item correctly and by dealing in a novel way with the hypothesis that some test questions are more difficult than others. en_US
dc.format.extent 237587 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher The American Economic Review en_US
dc.subject learning en_US
dc.subject structural equations en_US
dc.title Guessing and the Error Structure of Learning Models en_US
dc.type Journal Article en_US
dc.department Economics

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