Recent Developments in Item Response Theory with Implications for Teacher Certification

1987 ◽  
Vol 14 ◽  
pp. 239 ◽  
Author(s):  
Robert J. Mislevy
2019 ◽  
Vol 38 (6) ◽  
pp. 692-705 ◽  
Author(s):  
Boris Forthmann ◽  
Rüdiger Grotjahn ◽  
Philipp Doebler ◽  
Purya Baghaei

As measures of general language proficiency, C-tests are ubiquitous in language testing. Speeded C-tests are quite recent developments in the field and are deemed to be more discriminatory and provide more accurate diagnostic information than power C-tests especially with high-ability participants. Item response theory modeling of speeded C-tests has not been discussed in the literature, and current approaches for power C-tests based on ordinal models either violate the model assumptions or are relatively complex to be reliably fitted with small samples. Count data models are viable alternatives with less restrictive assumptions and lower complexity. In the current study, we compare count data models with commonly applied ordinal models for modeling a speeded C-test. It was found that a flexible count data model fits equally well in absolute and relative terms as compared with ordinal models. Implications and feasibility of count data models for the psychometric modeling of C-tests are discussed.


Author(s):  
Jonas W.B. Lang ◽  
Louis Tay

Item response theory (IRT) is a modeling approach that links responses to test items with underlying latent constructs through formalized statistical models. This article focuses on how IRT can be used to advance science and practice in organizations. We describe established applications of IRT as a scale development tool and new applications of IRT as a research and theory testing tool that enables organizational researchers to improve their understanding of workers and organizations. We focus on IRT models and their application in four key research and practice areas: testing, questionnaire responding, construct validation, and measurement equivalence of scores. In so doing, we highlight how novel developments in IRT such as explanatory IRT, multidimensional IRT, random item models, and more complex models of response processes such as ideal point models and tree models can potentially advance existing science and practice in these areas. As a starting point for readers interested in learning IRT and applying recent developments in IRT in their research, we provide concrete examples with data and R code. Expected final online publication date for the Annual Review of Organizational Psychology and Organizational Behavior, Volume 8 is January 21, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.


2001 ◽  
Vol 46 (6) ◽  
pp. 629-632
Author(s):  
Robert J. Mislevy

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