Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

By Robert Henson, Jonathan Templin, John T. Willse. 2009

579 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/c41f6619e48e496e4f64d481ab0788a59ed6621e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. L. DeCarlo
2. Wei Tian
3. Yanlou Liu
4. Tao Xin
5. Gongjun Xu
6. Jimmy de la Torre
7. Chun Wang
8. Alan Huebner

In Computer Networks and Communications

1. L. DeCarlo
2. Chun Wang
3. Alan Huebner
4. Jimmy de la Torre
5. Gongjun Xu
6. Wei Tian
7. Yanlou Liu
8. Tao Xin

Citations in the corpus

• On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix (2011, methodology): “The DINA model has been fit within a fully Bayesian framework using Markov chain Monte Carlo (MCMC) methods (e.g., de la Torre & Douglas, 2004, 2008; Henson et al., 2009; Junker & Sijtsma, 2001).”
• The Generalized DINA Model Framework. (2011, methodology): “DINA and DINO models can also be derived from the logit and log CDMs, albeit on different scales without the appropriate transformations, by applying the same constraints to λ and ν .F or example, Henson, Templin, and Willse (2009) showed that the DINA and DINO models can be obtained from the satu”
• A Note on Comparing Examinee Classification Methods for Cognitive Diagnosis Models (2011, background): “See Henson, Templin, and Willse (2009) for a thorough explanation of these concepts.”
• Diagnosing Teachers’ Understandings of Rational Numbers: Building a Multidimensional Test Within the Diagnostic Classification Framework (2014, methodology): “We designed the DTMR Fractions test from the perspective of a general DCM, the log-linear cognitive diagnosis model (LCDM; Henson et al., 2009).”
• An Application of M2 Statistic to Evaluate the Fit of Cognitive Diagnostic Models (2016, methodology): “We use the item numbering originally used by Henson et al. (2009).”
• A Cognitive Diagnostic Modeling of Attribute Mastery in Massachusetts, Minnesota, and the U.S. National Sample Using the TIMSS 2007 (2011, unknown): “For example, the generalized DINA ( de la Torre, in press ) model, the general diagnostic model ( von Davier, 2005 ), and the loglinear cognitive diagnostic model ( Burke & Henson, 2008 ; Henson, Templin, & Willse, 2009 ) demonstrate this trend in the literature.”
• Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “All these DCMs can be viewed as special cases of a general diagnostic classification model (de la Torre, 2011; Henson et al., 2009; von Davier, 2008), where the IRF takes the general form containing all the inter-imsart-sts ver.”
• Identifiability of restricted latent class models with binary responses (2016, methodology): “Identifiability is the prerequisite for most common statistical inferences, especially parameter estimation, and its study dates back to Koopmans (1950) and Koopmans and Reiersøl (1950); see also McHugh (1956); Rothenberg (1971); Goodman (1974); Gabrielsen (1978) for further developments.”
• Cognitive Diagnosis Approaches to Language Assessment: An Overview (2009, background): “First, CDPMs can be viewed as a special case of latent class models where each class represents a group of learners with a specific mastery/nonmastery profile for a set of attributes specified in the Q-matrix ( Henson, Templin, & Willse, 2007 ; von Davier, 2005 ).”
• Analysis of Clinical Data From a Cognitive Diagnosis Modeling Framework (2018, background): “It should be noted that general CDMs based on different link functions (e.g., G-DINA model and log-linear CDM;Henson et al., 2009) are equivalent to each other in their saturated forms, and, thus, provide identical model-data fit.”
• The Impact of Model Misspecification on Parameter Estimation and Item‐Fit Assessment in Log‐Linear Diagnostic Classification Models (2012, background): “The log-linear model framework is a general model specification and estimation framework that allows practitioners to specify core diagnostic classification models from the literature (Henson et al., 2009; Rupp et al., 2010).”