Multidimensional Item Response Theory

By M. Reckase. 2009

1,152 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/d57ad4cca35e781d17961e357f28c1029230e41d

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In Computer Networks and Communications

In Management Science and Operations Research

Citations in the corpus

Using Logistic Approximations of Marginal Trace Lines to Develop Short Assessments (2013, methodology): “The multidimensional GRM (Muraki & Carlson, 1993; Reckase, 2009) describes the probability of responding in category k or higher, where k = 0, 1, .”
What makes mathematical word problem solving challenging? Exploring the roles of word problem characteristics, text comprehension, and arithmetic skills (2019, background): “A widely recommended alternative to CTT is the item response theory (IRT) modelling (De Ayala 2009; Finch and French 2015; Reckase 2009), in which the difficulty level estimated with IRT refers to a probability of a correct response at a given level of participant ability (Finch and French 2015).”
Reporting of Subscores Using Multidimensional Item Response Theory (2010, methodology): “Multidimensional item response theory (MIRT) models (e.g., Reckase, 1997, 2007; Beguin and Glas, 2001) can be employed to report subscores.”
Generalized Network Psychometrics: Combining Network and Latent Variable Models (2016, methodology): “more, Epskamp et al. (in press) showed that the Ising model for ferromagnetism (Ising, 1925), which models magnetism as a network of particles, is equivalent to multidimensional item response theory (Reckase, 2009). In network psychometrics, psychometric data are modeled through directed or undirect”
Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications (2017, methodology): “…model (e.g. Anderson, 2003; Bartholomew et al., 2008), multidimensional item response theory model for binary responses that plays a key role in educational assessment (e.g. Reckase, 2009), and the Poisson model that is widely used to analyze multivariate count (e.g. Moustaki and Knott, 2000).”
Bifactor Models and Rotations: Exploring the Extent to which Multidimensional Data Yield Univocal Scale Scores (2010, background): “Reckase (2009) , for example, has written an entire book on the utility of multidimensional IRT models of the correlated-traits type in educational assessment contexts.”
A Tutorial on Interpreting Bifactor Model Scores (2013, methodology): “Wang (1985, as cited in Reckase, 2009, p. 126) used the term reference composite to refer to the predominant direction of measurement for the items on a test or subtest.”
An Overview and Practical Guide to IRT Measurement Equivalence Analysis (2015, methodology): “Although newer multidimensional IRT models have been developed (Reckase, 2009), these are beyond the scope of the current article and have been rarely employed in organizational research.”
Latent Variable Selection for Multidimensional Item Response Theory Models via $$L_{1}$$L1 Regularization (2016, neutral): “As summarized in Reckase (1997, 2009) and Embretson and Reise (2000), the multidimensional IRT models contain two or more parameters to describe the interaction between the latent traits and the responses to items.”
Practical Guide to Conducting an Item Response Theory Analysis (2014, methodology): “There are IRT models available that allow for relaxation of these assumptions (e.g., nonparametric IRT, see Sijtsma & Molenaar, 2002; multidimensional IRT, see Reckase, 2009).”
COMPARISON OF MULTIDIMENSIONAL ITEM RESPONSE MODELS: MULTIVARIATE NORMAL ABILITY DISTRIBUTIONS VERSUS MULTIVARIATE POLYTOMOUS ABILITY DISTRIBUTIONS (2008, background): “…penalty, 2PL model, general diagnostic model i Acknowledgments The authors thank Andreas Oranje, Sandip Sinharay, and Dan Eignor for their helpful comments. ii Multidimensional item response models are well-known in the psychometric literature but relatively little used in practice (Reckase, 2007).”
Process Overlap Theory: A Unified Account of the General Factor of Intelligence (2016, background): “There are two general kinds of multidimensional models: compensatory and noncompensatory models (for an introduction to MIRT, see Reckase, 2009).”
Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, neutral): “See Chapter 8, Reckase (2009) for a further discussion about these indeterminacies of IRT models.”
mirt: A Multidimensional Item Response Theory Package for the R Environment (2012, methodology): “However, with recent advances in estimation theory, coupled with the advances in computational power of personal computers, multidimensional IRT research is ﬁnally beginning to blossom as a feasible statistical analysis methodology (Edwards 2010; Reckase 2009; Wirth and Edwards 2007).”