Computation of the Response Similarity Index M4 in R under the Dichotomous and Nominal Item Response Models
| Autor: | Cengiz Zopluoglu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
response similarity
Index (economics) Computer science response similarity M4 test fraud item response theory test security item response theory Eğitim Bilimsel Disiplinler Ocean Engineering Function (mathematics) Measure (mathematics) lcsh:Education (General) test security Similarity (network science) m4 test fraud Item response theory Statistics Probability distribution Multinomial distribution lcsh:L lcsh:L7-991 Education Scientific Disciplines lcsh:Education Test data |
| Zdroj: | Volume: 6, Issue: 5 1-19 International Journal of Assessment Tools in Education International Journal of Assessment Tools in Education, Vol 6, Iss 5, Pp 1-19 (2019) |
| ISSN: | 2148-7456 |
| DOI: | 10.21449/ijate.527299 |
| Popis: | Unusual response similarityamong test takers may occur in testing data and be an indicator of potentialtest fraud (e.g., examinees copy responses from other examinees, send textmessages or pre-arranged signals among themselves for the correct response, itempre-knowledge). One index to measure the degree of similarity between tworesponse vectors is M4 proposed by Maynes(2014). M4 index is based on a generalized trinomialdistribution and it is computationally very demanding. There is currently noaccessible tool for practitioners who may want to use M4 in their research andpractice. The current paper introduces the M4 index and its computationaldetails for the dichotomous and nominal item response models, provides an Rfunction to compute the probability distribution for the generalized trinomialdistribution, and then demonstrates the computation of the M4 index under thedichotomous and nominal item response models using R. Unusual response similarity among test takers may occur in testing data and be an indicator of potential test fraud (e.g., examinees copy responses from other examinees, send text messages or pre-arranged signals among themselves for the correct response, item pre-knowledge). One index to measure the degree of similarity between two response vectors is M4 proposed by Maynes (2014). M4 index is based on a generalized trinomial distribution and it is computationally very demanding. There is currently no accessible tool for practitioners who may want to use M4 in their research and practice. The current paper introduces the M4 index and its computational details for the dichotomous and nominal item response models, provides an R function to compute the probability distribution for the generalized trinomial distribution, and then demonstrates the computation of the M4 index under the dichotomous and nominal item response models using R. |
| Databáze: | OpenAIRE |
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