Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data. | Psychometrika;85(3): 775-811, 2020 09. | MEDLINE

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Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data.

Fang, Guanhua; Ying, Zhiliang.

Affiliation

Fang G; Columbia University, New York, USA. gf2340@columbia.edu.
Ying Z; Columbia University, New York, USA.

Psychometrika ; 85(3): 775-811, 2020 09.

Article in En | MEDLINE | ID: mdl-32929664

Subject(s)

Likelihood Functions; Psychometrics; Bayes Theorem; Humans; Markov Chains; Monte Carlo Method

Key words

co-occurrent pattern; identifiability; latent theme dictionary model; process data

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Psychometrics / Likelihood Functions Type of study: Diagnostic_studies / Health_economic_evaluation / Prognostic_studies Limits: Humans Language: En Journal: Psychometrika Year: 2020 Type: Article Affiliation country: United States

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Psychometrics / Likelihood Functions Type of study: Diagnostic_studies / Health_economic_evaluation / Prognostic_studies Limits: Humans Language: En Journal: Psychometrika Year: 2020 Type: Article Affiliation country: United States