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LATENT CLASS EQUATING WITH ANCHOR TEST DESIGN IN COGNITIVE DIAGNOSTIG MODELS
Oğuz Tahsin Başokçu, Arzu Canpolat

Last modified: 2017-07-28

Abstract


In this research, equation of two tests was carried out which were developed within the scope of TUBITAK Project, numbered 115K531, with reference to Cognitive Diagnostic Models (CDM). Angoff (1971) defines test equating as converting the system of units of one form to the system of units of the other form. In the study, with regard to anchor item parameters, formB latent classifications were equated to formA classification. The sample of the research consists of 992 6th grade examinees. Tests consist of 15 items, 8 of which were anchors. The formation of Q matrix was based on 7 fundamental mathematical capabilities (OECD, 2013, p. 30-31) which were used in PISA2012 framework. According to the expert opinion, 3 of the capabilities were considered to be related to the tests. Item statistics of the tests were determined via CTT and IRT models. Since the tests were given to examinees with similar attribute levels, horizontal equating was applied. The internal-anchor design was used as test linking design. DINA model was used in the analysis. DINA is a CDM model which consists of g (guess) and s (slip) parameters and item parameters including their standard errors and which generates alpha parameters of the posterior probabilities of the classes (de la Torre, 2009). Equating process was put into action in two stages. In the first stage, item and individual parameters of formA and formB were determined together with the response patterns of each examinee who took formB. 426 different response patterns were obtained from 491 examinees. In the second stage, data were generated using the anchor items in formA and item parameters of 7 items of formB. Anchor item parameters of formA were used as equating criteria for formB. FormB patterns were reclassified in line with formA parameters. Analysis results revealed that latent classes of formB examinees remained unchanged at a ratio of 88%. The changing clusters in general shifted towards higher attribute level.