On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix

By L. DeCarlo. 2011

217 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5c341db2a2f0e210a603af13c23132af8e4bd39d

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. Chia-Yi Chiu
2. Carolin Strobl
3. Achim Zeileis
4. Wei Tian
5. Yanlou Liu
6. Tao Xin
7. Michel Philipp
8. Jimmy de la Torre

In Experimental and Cognitive Psychology

1. Chia-Yi Chiu
2. Gongjun Xu
3. Wei Tian
4. Yanlou Liu
5. Tao Xin
6. Achim Zeileis
7. Michel Philipp
8. Carolin Strobl

In Management Science and Operations Research

1. Chia-Yi Chiu
2. Gongjun Xu
3. Jonathan Templin
4. Wei Tian
5. Yanlou Liu
6. Tao Xin
7. Achim Zeileis
8. Michel Philipp

Citations in the corpus

• Diagnosing Teachers’ Understandings of Rational Numbers: Building a Multidimensional Test Within the Diagnostic Classification Framework (2014, methodology): “For example, although the Tatsuoka fraction subtraction data have been used in over 10 methodological DCM publications in the last 8 years, DeCarlo (2011) demonstrated that a flaw in the Q-matrix leads to misclassification, an incompatibility that has yet to be resolved.”
• On the Estimation of Standard Errors in Cognitive Diagnosis Models (2018, background): “DeCarlo (2011) suggested PM estimation to overcome these problems. Whether PM estimation leads to more accurate parameter and standard error estimates than the traditional ML approach in CDMs was not the scope of this work but something that can be investigated in future research. Moreover, the norm”
• Identifiability of restricted latent class models with binary responses (2016, background): “Psychometricians have long been aware of the identifiability issue of the Q-matrix based latent class models (DiBello et al., 1995; Tatsuoka, 2009; DeCarlo, 2011; Maris and Bechger, 2009).”
• Statistical Refinement of the Q-Matrix in Cognitive Diagnosis (2013, methodology): “In partial agreement with DeCarlo (2011), the first result indicated that possession of the third attribute was not required to respond correctly to Item 4.”
• An Application of M2 Statistic to Evaluate the Fit of Cognitive Diagnostic Models (2016, methodology): “This data set has been a commonly used example in cognitive diagnosis literature (e.g., DeCarlo, 2010; de la Torre, 2009, 2011; de la Torre & Douglas, 2008; Henson et al., 2009).”