Test Theory, Classical Test Theory

Frey, Felix

doi:10.1002/9781118901731.iecrm0247

Cited by 4 publications

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“…Classical Test Theory (CTT) is one of the most common approaches used to measure test scores changes (Frey, 2017). There are four major statistics reported in the framework of the CTT; "1) Item Difficulty, 2) Item-Test Correlation, 3) Reliability Coefficient, 4) Standard Error of Measurement (SEM)" (Zeng & Wyse, 2009).…”

Section: Methodology For the Reliability Analysis Of Test Scoresmentioning

confidence: 99%

İlkokul Öncesi Çocuklar İçin Artırılmış Gerçeklikli Dil Öğrenme Aracının Etkinliğinin İstatistiksel Analizi

Topsakal,

Topsakal

2023

ijlet

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Augmented Reality (AR) technology has been utilized in many fields, including language education. However, there is limited research on its application in early childhood education. This paper focuses on the statistical analysis of an AR application's efficacy for learning new vocabulary at the pre-elementary school level. We used a pre-test/post-test experimental design study to compare the effectiveness of learning foreign language vocabulary using traditional approaches (flashcards) and using an AR app. The data were collected from three daycares on students at three age groups, 4, 5, and 6 years old. The Mixed ANOVA Model is used to compare the control and treatment groups' means through pre-test and post-test scores. We also conducted a reliability analysis to see groups' pre-test and post-test scores are internally consistent. Cronbach's alpha scores were used as the reliability estimate. Moreover, the item-total correlation and item difficulty for each age group were calculated. The results show that Cronbach's alpha values for all age groups are between .80 and .95. (>.70). The item analysis results identified the easiest and the most difficult questions on the vocabulary test. The results of the Mixed ANOVA Model show the statistically significant difference between pretests and post-test results (time factor) for each age group (p=0.00, p<0.05 for each age group). We also show there is an interaction effect between pre/post-test scores and control/treatment groups for four-year-olds (p=0.00, p<0.005) and five-yearolds (p=0.045, p<0.05) in learning a foreign language when AR apps are utilized in the classroom.

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Section: Methodology For the Reliability Analysis Of Test Scoresmentioning

confidence: 99%

İlkokul Öncesi Çocuklar İçin Artırılmış Gerçeklikli Dil Öğrenme Aracının Etkinliğinin İstatistiksel Analizi

Topsakal,

Topsakal

2023

ijlet

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Test Theory and Classical Test Theory

Frey

2020

The International Encyclopedia of Media Psychology

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Test theory addresses statistical methods and criteria for the design, evaluation, and comparison of “tests,” that is, standardized procedures for measuring constructs of interest, typically encompassing several test items. These methods and criteria are based on assumptions about the composition of the measurements, the properties of these components, and the relationship between them. The most relevant test theoretical frameworks are classical test theory (CTT) and, as a representative of “modern” test theories, item response theory (IRT). The basic assumptions and procedures of the CTT were developed predominantly during the first half of the twentieth century. Within this framework, influential methods and criteria were developed for the evaluation of tests as a whole, for example, reliability, and for individual test items, for example, item difficulty and item discrimination. Since then, several serious limitations of CTT have been identified, such as the test and sample dependency of CTT's person and item parameters, incomparable scaling of person and item parameters, empirical interdependencies between parameters that should be independent theoretically, and the assumption of reliability as a uniform property of a test. Modern measurement models, like IRT, avoid or overcome these limitations of CTT. However, they do so only at the expense of more demanding assumptions, greater mathematical complexity, and more time and effort (including bigger sample sizes) needed for test construction and evaluation. Moreover, CTT‐ and IRT‐based evaluations often come to comparable conclusions regarding the quality and exclusion of items.

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SATs, sets and allegations of bias: The allocation of 11‐year‐old students to mathematics sets in some English schools in 2015. A response to Connolly et al., 2019

Gomm¹

2022

British Educational Res J

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This is a methodological critique of research by theBest Practice in Grouping Students (BPGS) project claiming teacher bias in allocating students to firstyear secondary school mathematics teaching sets (British Educational Research Journal, 45(4),(873)(874)(875)(876)(877)(878)(879). The research assumes that bias could be shown by non-random relationships between 'misallocations' to sets and memberships of gender, ethnic and/ or socio-economic subgroups. This paper questions the authors' prescriptions for correct set placements and demonstrates two ways in which non-random relationships between subgroup memberships and (alleged) misplacements could be generated despite all students with similar scores within a school being given the same chance of a set position deemed correct by the researchers. It suggests that the BPGS project results are largely artefacts of their evaluation approach. No claim is made herein that teacher bias plays no part in set allocation, merely that, if it does, the approach by Connolly et al. could not evidence it. A lesson from the critique is that, before drawing inferences from data aggregated from diverse

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Test Theory, Classical Test Theory

Cited by 4 publications

References 1 publication

İlkokul Öncesi Çocuklar İçin Artırılmış Gerçeklikli Dil Öğrenme Aracının Etkinliğinin İstatistiksel Analizi

İlkokul Öncesi Çocuklar İçin Artırılmış Gerçeklikli Dil Öğrenme Aracının Etkinliğinin İstatistiksel Analizi

Test Theory and Classical Test Theory

SATs, sets and allegations of bias: The allocation of 11‐year‐old students to mathematics sets in some English schools in 2015. A response to Connolly et al., 2019

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