Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach

By Deniz Senturk-Doganaksoy. Technometrics, 2006

516 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ab29e74b7f64dc765f2814d0c32f7d574ad09771

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Citations in the corpus

Connecting Cognitive Theory and Assessment: Measuring Individual Differences in Reading Comprehension (2012, methodology): “The development of sophisticated item-response techniques is likely to be helpful in clearing these hurdles (e.g., De Boeck & Wilson, 2004).”
Operacionalização de adaptação transcultural de instrumentos de aferição usados em epidemiologia (2007, background): “* Cadernos de Saúde Pública (20), Revista de Saúde Pública (15) e Revista Brasileira de Epidemiologia (1).”
Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “The latent regression models can also be regarded as a special explanatory IRT model (De Boeck and Wilson, 2004); see Section 2.6 for a brief discussion of explanatory IRT models.”
A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. (2014, background): “De Boeck and Wilson (2004), in providing their anatomy of explanatory models, identify the three building blocks that can be used in the construction of models whose aim is to explain observed variance.”
Designing Cognitive Complexity in Mathematical Problem-Solving Items (2010, methodology): “The coefficients of the LLTM and the 2PL-constrained model were estimated as nonlinear mixed models (see DeBoeck & Wilson, 2004) using marginal maximum likelihood.”
IRTrees: Tree-Based Item Response Models of the GLMM Family (2012, background): “Various models for ordinal data have been ﬁt to these data (De Boeck and Wilson 2004).”
Likelihood inference for a class of latent Markov models under linear hypotheses on the transition probabilities (2006, methodology): “we can formulate a LM version of the Rasch (1961) model, the most well-known Item Response Theory (IRT) model (see, among others, Hambleton and Swaminathan, 1985, and De Boeck and Wilson, 2004). This model finds its natural application in educational assessment, when a group of examinees has to be a”
Bias in Factor Score Regression and a Simple Solution (2011, methodology): “The generalized linear and nonlinear methodologies described in De Boeck and Wilson (2004) provide a partial approach because they allow the prediction of IRT item parameters and person parameters by external variables.”
Applied Psychometrics: The Steps of Scale Development and Standardization Process (2018, background): “IRT is often presented as a superior alternative to CTT (see De Boeck & Wilson, 2004; Embretson & Reise, 2010; Nering & Ostini, 2010; Reise & DOI: 10.4236/psych.2018.911145 2551 Psychology Revicki, 2015 quoted by DeVellis, 2017).”
Exploring Local Item Dependence Using a Random-Effects Facet Model (2005, background): “The MRCMLM (see Equation (23)), being a member of the exponential family of distributions, can be viewed as a generalized linear mixed model (De Boeck & Wilson, 2004; McCullagh & Nelder, 1989; McCulloch & Searle, 2001; Nelder & Wedderburn, 1972; Rijmen, Tuerlinckx, De Boeck, & Kuppens, 2003).”
The estimation of item response models with the lmer function from the lme4 package in R (2011, methodology): “For SAS PROC NLMIXED, suchlike descriptions can be found in De Boeck and Wilson (2004) and in Sheu et al.”
Optimizing Instructional Policies (2013, methodology): “In the spirit of item-response theory (Boeck & Wilson, 2004), the model could be extended further to include factors that represent the difficulty of individual test questions and interactions between subject ability and question difficulty.”
The Rasch Testlet Model (2005, methodology): “The MRCMLM, being a member of the exponential family of distributions, can be viewed as a generalized linear mixed model (De Boeck & Wilson, 2004; McCullagh & Nelder, 1989; McCulloch & Searle, 2001; Nelder & Wedderburn, 1972; Rijmen, Tuerlinckx, De Boeck, & Kuppens, 2003).”
Formulating the Rasch Differential Item Functioning Model Under the Marginal Maximum Likelihood Estimation Context and Its Comparison With Mantel–Haenszel Procedure in Short Test and Small Sample Conditions (2011, unknown): “See also, for example, Kamata (2001) and De Boeck and Wilson (2004) on how to use a mixed model framework to estimate IRT models.”
Copula Functions for Residual Dependency (2007, methodology): “Another advantage of the copula approach is that the effect can still be interpreted using the (log) odds ratio, as is the case for Rasch models with latent regression of θp (see De Boeck & Wilson, 2004, for more examples).”
Special education students’ use of indirect addition in solving subtraction problems up to 100—A proof of the didactical potential of an ignored procedure (2012, result): “This explains why the GEE approach and the multilevel approach lead to different results (see also Molenberghs & Verbeke, 2004).”