Item Response Theory

By Christine E. DeMars. 2010

2,406 citations recorded by the source corpus.

This is a title in our Understanding Statistics series, which is designed to provide researchers with authoritative guides to understanding, presenting and critiquing analyses and associated inferences. Each volume in the series demonstrates how the relevant topic should be reported - including detail surrounding what can be said, and how it should be said, as well as drawing boundaries around what cannot appropriately be claimed or inferred. This volume addresses an important issue for the design of survey instruments, which is rarely taught in graduate programs beyond those specifically for statisticians. Item Response Theory is used to describe the application of mathematical models to data from questionnaires and tests as a basis for measuring abilities, attitudes, or other variables. It is used for statistical analysis and development of assessments, often for high stakes tests such as the Graduate Record Examination. The author is known for her clear, accessible writing; like all books in this series, this volume includes examples of both good and bad write-ups for methods sections of journal articles.

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Citations in the corpus

Understanding Dataset Difficulty with V-Usable Information (2021, neutral): “Item response theory (IRT; Embretson & Reise, 2013) allows the difficulty of instances to be learned via parameters in a probabilistic model meant to explain model performance (Lalor et al., 2018; Rodriguez et al., 2021).”
Testing Person Fit in Cognitive Diagnosis (2009, methodology): “In contrast, in IRT, an individual’s response can be modeled and thus the person fit of the model can be quantified and studied (Hulin et al., 1983).”
Financial Risk Tolerance (2017, unknown): “Readers interested in a nonmathematical primer on IRT models should review DeMars (2010).”
Uncovering Faking Samples in Applicant, Incumbent, and Experimental Data Sets: An Application of Mixed-Model Item Response Theory (2004, background): “Other psychometricians are more cautious about the Rasch model because they view the model’s assumptions as overly restrictive and unlikely to be met in applications (e.g., Drasgow & Hulin, 1990, p. 584).”
Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models (2014, methodology): “Similar models are used in educational testing scenarios , where rows are testers and columns are questions on a test (Baker, 1992).”
STATISTICAL LITERACY: A COMPLEX HIERARCHICAL CONSTRUCT (2003, methodology): “Rasch models are a set of measurement models coming under the general heading of Item Response Theory (Stocking, 1999) that have been widely used in surveys such as the Third International Mathematics and Science Survey (TIMSS) (Lokan, Ford, & Greenwood, 1997).”
ASSESSING SECOND-LANGUAGE ORAL PROFICIENCY FOR RESEARCH (2016, unknown): “See Ackerman, Gierl, and Walker ( 2003 ) and Thissen and Steinberg ( 2009 ), for examples.”
Leading to Stimulate Employees' Ideas: A Quantitative Review of Leader–Member Exchange, Employee Voice, Creativity, and Innovative Behavior (2017, methodology): “For example, employing item-response theory (Drasgow & Hulin, 1990) to inform item selection may also help increase the range of difficulty in items used so that tests can distinguish between individuals even in contexts where high levels of creativity and innovative behavior are the norm.”
Using plausible values in secondary analysis in large-scale assessments (2017, background): “A disadvantage with EAP is that in a given population, the variability of the EAP estimates of proficiency is typically less than the variability of θ because of shrinkage toward the mean for the EAP estimates (Kolen, 2006).”
The fusion model skills diagnosis system (2007, background): “Tetrachoric correlations model the relationship between two dichotomously measured variables, where it is assumed that a normally distributed latent variable generates each observed dichotomous variable (see, e.g., Hambleton & Swaminathan, 1985).”
The relative significance of syntactic knowledge and vocabulary breadth in the prediction of reading comprehension test performance (2007, methodology): “For this main study we adopted the conservative approach, which would be required in IRT (Henard, 2000), of excluding from the analysis those participants who received either a perfect or zero score on any of the ability measures.”
Generalizability Theory and Classical Test Theory (2010, unknown): “Expected Value Theories and IRT Given the popularity of item response theory (IRT) (see, for example, Lord, 1980 and Yen & Fitzpatrick, 2006), it seems obvious to consider some similarities and differences between IRT and the two expected value theories discussed in this article.”
Comparing the Fit of Item Response Theory and Factor Analysis Models (2011, methodology): “For an overview of IRT models and methods, see Hulin, Drasgow, and Parsons (1983), Hambleton and Swaminathan (1985), Hambleton, Swaminathan, and Rogers (1991), van der Linden and Hambleton (1997), and Embretson and Reise (2000).”
Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “…tools have been developed to better measure human behaviors, promoting IRT to become one of the dominant paradigms for measurement in education, psychology, and related problems; see Carlson and von Davier (2017), Embretson and Reise (2000), and van der Linden (2018) for the history of IRT.”
Knowledge Tracing: A Survey (2022, methodology): “IRT and factor analysis methods [30] assume prior learning ability information for each student to be available in addition to an exercise answering history, and this limits their usage in scenarios where such information is missing such as cold-start ones.”
A Conceptual and Methodological Framework for Psychometric Isomorphism (2014, methodology): “Illustration 1: Item Response Theory Modeling for Dichotomous Responses IRT is a commonly used statistical technique for measuring psychological constructs (Drasgow & Hulin, 1990).”
Local Dependence Diagnostics in IRT Modeling of Binary Data (2013, background): “Item response theory (IRT) refers to a set of models for discrete data that are widely used in educational, psychological, and medical assessment applications (Thissen & Steinberg, 2009).”
EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction (2019, background): “, a latent trait that describes the integrated knowledge state, from a logistic-like function [12].”