Ergul DEMIR
Ankara University, Educational Science Faculty, TURKEY.
DOI: 10.14689/ejer.2018.75.3

ABSTRACT

Purpose: The answer-copying tendency has the potential to detect suspicious answer patterns for prior distributions of statistical detection techniques. The aim of this study is to develop a valid and reliable measurement tool as a scale in order to observe the tendency of university students’ copying of answers. Also, it is aimed to provide evidence with more comprehensive validity and reliability studies than the previously available researches.

Research Methods: This is a scale development study. The “Answer-Copying Tendency Scale in University Students (ACTS)” was developed under the “Classical Test Theory”. Other theories were also considered, especially “Item Response Theory”. After preliminary studies and item writing, a trial application with 711 students and main applications with 909 students was conducted. Structural validity, item and test descriptive statistics, item discriminations, inconsistency and test–retest reliability, classification accuracy, and item bias with differential item functioning were examined.

Findings: The ACTS composed of 2 factor and 20 items. Total scores and item scores distributions are normal. Item discriminations are very high and over 0.40. α inconsistency coefficients are over 0.88 and test–retest reliability coefficient is 0.804. It provides highly correct classifications according to the students’ answer-copying positions. There is no significant and serious DIF on items.

Implications for Research and Practice: Unlike similar examples, it was studied on the large groups and used more comprehensive techniques to obtain evidences. Results show that the validity and reliability levels of the ACTS are very high. The ACTS can be used to understand the nature of the answer-copying. Also, and more importantly, it is thought that the ACTS can be used to detect suspicious answer patterns for prior distributions.

Keywords: Answer-copying dishonesty scale development copy-detection.