| Title |
Using Hamiltonian Monte Carlo to estimate the log-linear cognitive diagnosis model via Stan
|
|---|---|
| Published in |
Behavior Research Methods, June 2018
|
| DOI | 10.3758/s13428-018-1069-9 |
| Pubmed ID | |
| Authors |
Zhehan Jiang, Richard Carter |
| Abstract |
The Bayesian literature has shown that the Hamiltonian Monte Carlo (HMC) algorithm is powerful and efficient for statistical model estimation, especially for complicated models. Stan, a software program built upon HMC, has been introduced as a means of psychometric modeling estimation. However, there are no systemic guidelines for implementing Stan with the log-linear cognitive diagnosis model (LCDM), which is the saturated version of many cognitive diagnostic model (CDM) variants. This article bridges the gap between Stan application and Bayesian LCDM estimation: Both the modeling procedures and Stan code are demonstrated in detail, such that this strategy can be extended to other CDMs straightforwardly. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 19% |
| Researcher | 4 | 13% |
| Student > Master | 4 | 13% |
| Professor > Associate Professor | 3 | 10% |
| Student > Doctoral Student | 3 | 10% |
| Other | 6 | 19% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 8 | 26% |
| Computer Science | 4 | 13% |
| Mathematics | 3 | 10% |
| Social Sciences | 3 | 10% |
| Medicine and Dentistry | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 9 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2019.
All research outputs
#4,788,315
of 25,653,515 outputs
Outputs from Behavior Research Methods
#604
of 2,564 outputs
Outputs of similar age
#84,785
of 342,339 outputs
Outputs of similar age from Behavior Research Methods
#20
of 38 outputs
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