A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practicality.

By Sarah M. Hartz. 2002

371 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/31843907beb32ef44f25780a96a340bdb8e4e2ab

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Citations in the corpus

An Application of M2 Statistic to Evaluate the Fit of Cognitive Diagnostic Models (2016, methodology): “…we used M2 and RMSEA2 statistics to investigate the performance of CDMs among the deterministic inputs, noisy ‘‘and’’ gate model (DINA; de la Torre, 2009; Junker & Sijtsma, 2001), the compensatory reparameterized unified model (C-RUM; Hartz, 2002), and LCDM regarding fraction subtraction data.”
Cognitive Diagnosis Approaches to Language Assessment: An Overview (2009, methodology): “These include the tree-based regression method (Sheehan, 1997), the hybrid model of latent class analysis (Yamamoto & Gitomer, 1993), the cognitive design system (Embretson, 1998), the attribute hierarchy method (Leighton, Gierl, & Hunka, 2004), the Bayesian inference network (Mislevy, Steinberg, & ”
Identifiability of restricted latent class models with binary responses (2016, methodology): “Note that the model is equivalent to the log-link model For the reduced RUM in (2.7) , it is easy to see that assumptions (2.2) and (2.3) are satisﬁed by the deﬁnition.”
The Impact of Model Misspecification on Parameter Estimation and Item‐Fit Assessment in Log‐Linear Diagnostic Classification Models (2012, unknown): “Hartz (2002) and Henson and Templin (2009) investigated the effects of Q-matrix misspecification for a relatively complex noncompensatory model, while de la Torre (2008) developed a method for identifying and correcting for misspecified Q-matrix entries for a simpler noncompensatory model.”
How assessing reading comprehension with multiple-choice questions shapes the construct: a cognitive processing perspective (2006, methodology): “…psychometric research should prove to be useful and further inquiries along the lines of Buck et al. (1997) with the rule-space methodology or Hartz (2002) with the fusion model could open up important pathways to construct validation of reading comprehension tests with MC items (see also…”
Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art (2008, methodology): “…Ξ that contains the correlations between the underlying latent continuous skill variables can be used to estimate the latent threshold parameters (Hartz, 2002; Templin and Henson, 2006, Henson & Templin, 2007). log( ) ( ) ( ) ( ) ( )I c k ck l ck l lk K kl ck cl l k •h = + + + == = ∑∑l l a l a…”
The Effects of Q-Matrix Misspecification on Parameter Estimates and Classification Accuracy in the DINA Model (2008, background): “For example, the same Q-matrix setup could be used with other multiple classification models such as the NIDA or RUM models.”
Estimating Classification Consistency and Accuracy for Cognitive Diagnostic Assessment. (2012, unknown): “…diagnostic models or diagnostic classification models in the measurement literature, have been devised (e.g., de la Torre & Douglas, 2004; Embretson, 1984; Gierl, Cui, & Hunka, 2008; Hartz, 2002; Junker & Sijtsma, 2001; Mislevy, Steinberg, & Almond, 2003; Templin & Henson, 2006; von Davier, 2007).”
Skills Diagnosis Using IRT-Based Latent Class Models (2007, methodology): “Hartz (2002) applied the RUM to the 60-item PSAT, with the goal being to inform students about the skills they should study prior to taking the SAT.”
The fusion model skills diagnosis system (2007, methodology): “Numerical techniques for drawing the correlations in the MCMC algorithm were used to maintain positive definiteness, as is required of the correlation matrix (see Hartz & Roussos, in press, for details).”
Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models (2008, methodology): “A model that differentiates between all examinee classes and allows differing item response probabilities for items with the same Q-matrix entries is the reduced reparameterized unified model (RUM; Hartz, 2002). In an attempt to reparameterize the unified model parsimoniously (DiBello, Stout, & Rous”