09 SES 10 C, Methodological Issues in Tests and Assessments
This research builds on the investigations conducted in 2015 and is grounded in the intersecting theoretical frameworks of validity of assessments (Messick, 1989), and the use of Item Response Theory (IRT), to analyse and report student achievement tests. The area of interest is the psychometric impact of guessing in large scale assessments, and in particular cases where guessing is an acknowledged, and in some cases encouraged, in student response strategies for multiple choice tests, but not accounted for in the assessment analysis techniques.
Many of major large-scale assessments cited below use Item Response Theory as the underlying theoretical and conceptual framework to estimate student achievement. For instance, PISA and NAPLAN use the Rasch model (Rasch, 1960), which requires that the probability of a student correctly responding to the cognitive demands of any particular test question is a function of the difficulty of the question and the ability of the student in relation to the characteristic (or trait) being assessed. In contrast, TIMSS and PIRLS apply variants of the Item Response Theory model that attempt to take account of specific characteristics of the item-student interaction in regard to the discrimination of the items that comprise the test, and in some cases an attempt to account for guessing.
In large scale assessments that involve multiple choice items assessed using Item Response Theory, guessing is either unaccounted for (Rasch, 1960) or treated as a property of the item calibration model, (Birnbaum, 1968, Hambleton et al,1985,1991). This paper is underpinned by a multi-facetted approach that considers the issue of guessing in simulated data and then in case study data collected in the field. The purpose of the field study is to identify the extent to which the case study data replicate the theoretical outcomes generated by the simulated data. The case study data will also attempt to identify patterns of responses, and characteristics of analyses that may assist in the identification of guessing in a student’s response pattern and hence inform the development of a mechanism to validly account for any potential mis-information or statistical errors in reporting student performances impacted by guessing.
Given the lack of clarity regarding how, and to what extent guessing is accounted for in these various models this research will further investigate the impact of guessing on the estimation of item difficulty and its impact on the consequent estimation of student ability.
The research has three major data sources to investigate the problem:
- Simulated Guttman-like data in which guessed items are defined and specifically identified so that the item and person parameters can be calibrated without/and with accounting for defined guessing by independent analyses of the raw and conditioned data sets;
- Simulated Rasch-like data in which guessed items can be ‘identified’ by a comparison of the relative item location to the person ability estimate (Andrich, et al (2011) and then re-estimation of item parameters and person ability estimates conducted using the modified data set that accounts for ‘identified guessing’;
- Data collected by fieldwork in which students engage with two instruments. These instruments are designed to be curriculum content and grade appropriate. The first is presented in a foreign language and students are encouraged to guess from the contexts of the items. The second instrument is exactly the same items delivered in English so that students can present their responses in the familiar environment. It is assumed that, as the second instrument is targeted to the sample and in a familiar context and language, the outcomes of this interaction will provide a “true estimate” of the student ability in the domain of interest.
1. Andrich, D., Marais, I., & Humphry, S. (2011) Using a Theorem by Anderson and the Dichotomous Rasch Model to Assess the Presence of Random Guessing in Multiple Choice Items. Journal of Educational and Behavioural Statistics, 37:417. 2. Frary, A.B., Cross, L.H. & Lowry, S.R. (1977) Random Guessing, Correction for Guessing and Reliability of Multiple-Choice Test Scores. The Journal of Experimental Education. Vol. 46, No. 1 (Fall, 1977), pp. 11-15. 3. Lau, P. N. K., Lau, S. H., Hong, K. S., & Usop, H. (2011). Guessing, Partial Knowledge, and Misconceptions in Multiple-Choice Tests. Educational Technology & Society, 14 (4), 99–110. 4. Paek, I. (2015). An Investigation of the Impact of Guessing on Co-efficient a and Reliability. Applied Psychological Measurement 2015, Vol 39 (4) 264 - 277. 5. Waller, M.I. (1974) Removing the Effects of Random Guessing from Latent Trait Ability Estimates. Educational Testing Service, Princeton N.J. ETS-RB-74-32. 6. Zand Scholten. A. (2011) The Guttman-Rasch paradox in item response theory. Downloaded from UvA-DARE. University of Amsterdam; http://hdl.handle.net/11245/2.86877
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