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Estimating parallel form reliability from one administration of a criterion‐referenced test: A computer program for practitioners
Author(s) -
Saltstone Robert,
Stange Ken,
Chase Ted
Publication year - 1989
Publication title -
psychology in the schools
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.738
H-Index - 75
eISSN - 1520-6807
pISSN - 0033-3085
DOI - 10.1002/1520-6807(198907)26:3<249::aid-pits2310260305>3.0.co;2-1
Subject(s) - sophistication , reliability (semiconductor) , test (biology) , consistency (knowledge bases) , computer science , computer program , criterion referenced test , psychology , internal consistency , reliability engineering , statistics , data mining , psychometrics , mathematics education , mathematics , artificial intelligence , programming language , engineering , standardized test , paleontology , social science , power (physics) , physics , quantum mechanics , sociology , biology
Reliability of a criterion‐referenced test is often viewed as the consistency with which individuals who have taken two strictly parallel forms of a test are classified as being masters or nonmasters. However, in practice, it is rarely possible to retest students, especially with equivalent forms. For this reason, methods for making conservative approximations of alternate form (or test‐retest “without the effects of testing”) reliability have been developed. Because these methods are computationally tedious and require some psychometric sophistication, they have rarely been used by teachers and school psychologists. This paper (a) describes one method (Subkoviak's) for estimating alternate‐form reliability from one administration of a criterion‐referenced test and (b) describes a computer program developed by the authors that will handle tests containing hundreds of items for large numbers of examinees and allow any test user to apply the technique described. The program is a superior alternative to other methods of simplifying this estimation procedure that rely upon tables; a user can check classification consistency estimates for several prospective cut scores directly from a data file, without having to make prior calculations.