Measurement of psychological disorders using cognitive diagnosis models.

By Jonathan Templin, Robert Henson. Psychological methods, 2006

634 citations recorded by the source corpus.

Subfields: Clinical Psychology, General Decision Sciences, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3b3859c9c25b6edc055b2029cb0c612d595165da

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Citations in the corpus

The Effects of Q-Matrix Misspecification on Parameter Estimates and Classification Accuracy in the DINA Model (2008, background): “…syndromes, it is useful to obtain very specific information about which combinations of syndromes are present in patients who have been broadly classified as either ‘‘healthy’’ or ‘‘clinically ill’’ to provide treatments that are suited to their specific needs (e.g., Templin & Henson, 2006).”
Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, neutral): “…for diﬀerent types of psychological processes underlying item response behavior, such as the Deterministic Inputs, Noisy “Or” gate (DINO) model (Templin and Henson, 2006) and reparametrized uni-ﬁed models (DiBello et al., 1995; Junker and Sijtsma, 2001), for which diﬀerent forms of IRFs are…”
Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables (2009, methodology): “MCMC estimation is has been growing in popularity and is a common choice in the literature to estimate cognitive diagnosis models (e.g., Hartz, 2002; Templin, Henson, Templin, & Roussos, in press; Templin & Henson, 2006 ).”
Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art (2008, methodology): “D ow nl oa de d by [ C ar ne gi e M el lo n U ni ve rs ity ] at 1 5: 14 2 8 Fe br ua ry 2 01 3 CHARACTERISTICS OF DIAGNOSTIC CLASSIFICATION MODELS 239 TABLE 1 A Taxonomy of DCM Observed Response Variables Latent Predictor Variables Dichotomous Polytomous Model Type Dichotomous RSM Noncompensatory AH”
A Cognitive Diagnostic Modeling of Attribute Mastery in Massachusetts, Minnesota, and the U.S. National Sample Using the TIMSS 2007 (2011, background): “For example, the deterministic input, noisy output, “or” gate model (DINO) introduced by Templin and Henson (2006) can be implemented.”
Cognitively Diagnostic Assessments and the Cognitive Diagnosis Model Framework (2014, methodology): “CDMs can be viewed as constrained latent class models that model responses as a function of discrete latent variables (Templin & Henson, 2006).”
An Application of M2 Statistic to Evaluate the Fit of Cognitive Diagnostic Models (2016, methodology): “For example, the Monte Carlo resampling technique (Templin & Henson, 2006) needs to obtain a stable empirical p value with multiple simulated data sets, which leads to an excessive amount of time.”
On the Estimation of Standard Errors in Cognitive Diagnosis Models (2018, methodology): “However, the method has also been suggested to identify the presence or absence of psychological disorders (de la Torre, van der Ark, & Rossi, 2015; Templin & Henson, 2006) or can be used for a detailed measurement of fluid intelligence using abstract reasoning tasks (Rupp, Templin, & Henson, 2010;…”
The Generalized DINA Model Framework. (2011, methodology): “The data for this illustration were a subset of the data originally described and used by Tatsuoka (1990), and more recently by Tatsuoka (2002, 2005), and de la Torre and Douglas (2004, 2008) .”
Estimating Classification Consistency and Accuracy for Cognitive Diagnostic Assessment. (2012, neutral): “…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): “As an example, Templin and Henson (2006) used pathological gambling.”
Diagnosing Teachers’ Understandings of Rational Numbers: Building a Multidimensional Test Within the Diagnostic Classification Framework (2014, background): “…as it is a more general and less familiar approach than many DCMs appearing in the literature that are maximally constrained versions of the LCDM (i.e., deterministic inputs noisy and gate [DINA] model, e.g., Haertel, 1989; deterministic inputs noisy or gate [DINO] model, Templin & Henson, 2006).”
Identifiability of restricted latent class models with binary responses (2016, methodology): “…(DiBello, Stout, and Roussos, 1995; Hartz, 2002), the DINO (Deterministic Input, Noisy ‘Or’ gate) and NIDO (Noisy Inputs, Deterministic ‘Or’ gate) (Templin and Henson, 2006), the rule space method (Tatsuoka, 1983, 2009), the attribute hierarchy method (Leighton, Gierl, and Hunka, 2004), the…”
Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models (2008, background): “Furthermore, the structural parameterization of Templin (2004) has been generalized from a single factor to multiple factors to fit a presumed model underlying dichotomous traits in the assessment of problem gamblers (Templin & Henson, 2006).”