Advancing the understanding of the resident pro-tourism behavior scale: An integration of item response theory and classical test theory

2022 ◽  
Vol 141 ◽  
pp. 113-125
Author(s):  
Jing Liu ◽  
Hua Lin ◽  
Bing Hu ◽  
Zixiong Zhou ◽  
Elizabeth Agyeiwaah ◽  
...  
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Classical Test Theory ◽  
Test Theory ◽  
Response Theory ◽  
Classical Test ◽  
Behavior Scale

scholarly journals Depressive and anxiety symptoms in refugees: Insights from classical test theory, item response theory and network analysis

2020 ◽  
Author(s):  
Pascal Schlechter ◽  
Paul O. Wilkinson ◽  
Judith Knausenberger ◽  
Katharina Wanninger ◽  
Svea Kamp ◽  
...  
Keyword(s):  
Item Response Theory ◽  
Network Analysis ◽  
Item Response ◽  
Classical Test Theory ◽  
Anxiety Symptoms ◽  
Test Theory ◽  
Response Theory ◽  
Classical Test

Item response theory

2015 ◽  
Author(s):  
David L. Streiner ◽  
Geoffrey R. Norman ◽  
John Cairney
Keyword(s):  
Item Response Theory ◽  
Psychometric Properties ◽  
Item Response ◽  
Traditional Approach ◽  
Classical Test Theory ◽  
Test Theory ◽  
Response Theory ◽  
Advantages And Disadvantages ◽  
The Past ◽  
Classical Test

Over the past few decades, there has been a revolution in the approach to scale development. Called item response theory (IRT), this approach challenges the notion that scales must be long in order to be reliable, and that psychometric properties of a scale derived from one group of people cannot be applied to different groups. This chapter provides an introduction to IRT, and discusses how it can be used to develop scales and to shorten existing scales that have been developed using the more traditional approach of classical test theory. IRT also can result in scales that have interval-level properties, unlike those derived from classical test theory. Further, it allows people to be compared to one another, even though they may have completed different items, allowing for computer-adapted testing. The chapter concludes by discussing the advantages and disadvantages of IRT.

Differentiating Among High-Achieving Learners: A Comparison of Classical Test Theory and Item Response Theory on Above-Level Testing

2020 ◽  
Vol 64 (3) ◽  
pp. 219-237
Author(s):  
Brandon LeBeau ◽  
Susan G. Assouline ◽  
Duhita Mahatmya ◽  
Ann Lupkowski-Shoplik
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Classical Test Theory ◽  
Reliability And Validity ◽  
Test Theory ◽  
Response Theory ◽  
Classical Test ◽  
High Achieving Students ◽  
High Achieving ◽  
The Impact

This study investigated the application of item response theory (IRT) to expand the range of ability estimates for gifted (hereinafter referred to as high-achieving) students’ performance on an above-level test. Using a sample of fourth- to sixth-grade high-achieving students ( N = 1,893), we conducted a study to compare estimates from two measurement theories, classical test theory (CTT) and IRT. CTT and IRT make different assumptions about the analysis that impact the reliability and validity of the scores obtained from the test. IRT can also differentiate students based on the student’s grade or within a grade by using the unique string of correct and incorrect answers the student makes while taking the test. This differentiation may have implications for identifying or classifying students who are ready for advanced coursework. An exploration of the differentiation for Math, Reading, and Science tests and the impact the different measurement frameworks can have on classification of students are explored. Implications for academic talent identification with the talent search model and development of academic talent are discussed.

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