Matches in SemOpenAlex for { <https://semopenalex.org/work/W2782130816> ?p ?o ?g. }
- W2782130816 endingPage "25" @default.
- W2782130816 startingPage "12" @default.
- W2782130816 abstract "In a fully Bayesian framework, a novel slice–Gibbs algorithm is developed to estimate a multilevel item response theory (IRT) model. The advantage of this algorithm is that it can recover parameters well based on various types of prior distributions of the item parameters, including informative and non-informative priors. In contrast to the traditional Metropolis–Hastings (M–H) within Gibbs algorithm, the slice–Gibbs algorithm is faster and more efficient, due to its drawing the sample with acceptance probability as one, rather than tuning the proposal distributions to achieve the reasonable acceptance probabilities, especially for the logistic model without conjugate distribution. In addition, based on the Markov chain Monte Carlo (MCMC) output, two model assessment methods are investigated concerning the goodness of fit between models. The information criterion method on the basis of marginal likelihood is proposed to assess the different structural multilevel models, and the cross-validation method is used to evaluate the overall multilevel IRT models. The feasibility and effectiveness of the slice–Gibbs algorithm are investigated in simulation studies. An application using a real data involving students’ mathematics test achievements is reported." @default.
- W2782130816 created "2018-01-12" @default.
- W2782130816 creator A5014261970 @default.
- W2782130816 creator A5080380640 @default.
- W2782130816 creator A5086720597 @default.
- W2782130816 date "2018-02-01" @default.
- W2782130816 modified "2023-10-18" @default.
- W2782130816 title "Slice–Gibbs sampling algorithm for estimating the parameters of a multilevel item response model" @default.
- W2782130816 cites W1965804664 @default.
- W2782130816 cites W1966576448 @default.
- W2782130816 cites W1967396577 @default.
- W2782130816 cites W1981488071 @default.
- W2782130816 cites W1981903823 @default.
- W2782130816 cites W1983769299 @default.
- W2782130816 cites W1985054565 @default.
- W2782130816 cites W1991305952 @default.
- W2782130816 cites W1991985445 @default.
- W2782130816 cites W1997318672 @default.
- W2782130816 cites W2017966270 @default.
- W2782130816 cites W2020999234 @default.
- W2782130816 cites W2030421604 @default.
- W2782130816 cites W2031792658 @default.
- W2782130816 cites W2043475059 @default.
- W2782130816 cites W2043541840 @default.
- W2782130816 cites W2044306689 @default.
- W2782130816 cites W2046376969 @default.
- W2782130816 cites W2056760934 @default.
- W2782130816 cites W2057765075 @default.
- W2782130816 cites W2066084578 @default.
- W2782130816 cites W2075453825 @default.
- W2782130816 cites W2083875149 @default.
- W2782130816 cites W2100128674 @default.
- W2782130816 cites W2113338391 @default.
- W2782130816 cites W2115643478 @default.
- W2782130816 cites W2124763785 @default.
- W2782130816 cites W2127682847 @default.
- W2782130816 cites W2128981260 @default.
- W2782130816 cites W2129531883 @default.
- W2782130816 cites W2136796925 @default.
- W2782130816 cites W2138309709 @default.
- W2782130816 cites W2138703513 @default.
- W2782130816 cites W2140869847 @default.
- W2782130816 cites W2144089869 @default.
- W2782130816 cites W2148534890 @default.
- W2782130816 cites W2151197472 @default.
- W2782130816 cites W2152977846 @default.
- W2782130816 cites W2153281140 @default.
- W2782130816 cites W2160900637 @default.
- W2782130816 cites W2168003017 @default.
- W2782130816 cites W2168175751 @default.
- W2782130816 cites W2168912035 @default.
- W2782130816 cites W2170454983 @default.
- W2782130816 cites W2204383650 @default.
- W2782130816 cites W3124421956 @default.
- W2782130816 cites W32980360 @default.
- W2782130816 cites W4211177544 @default.
- W2782130816 cites W4237377395 @default.
- W2782130816 doi "https://doi.org/10.1016/j.jmp.2017.10.005" @default.
- W2782130816 hasPublicationYear "2018" @default.
- W2782130816 type Work @default.
- W2782130816 sameAs 2782130816 @default.
- W2782130816 citedByCount "11" @default.
- W2782130816 countsByYear W27821308162019 @default.
- W2782130816 countsByYear W27821308162020 @default.
- W2782130816 countsByYear W27821308162021 @default.
- W2782130816 countsByYear W27821308162023 @default.
- W2782130816 crossrefType "journal-article" @default.
- W2782130816 hasAuthorship W2782130816A5014261970 @default.
- W2782130816 hasAuthorship W2782130816A5080380640 @default.
- W2782130816 hasAuthorship W2782130816A5086720597 @default.
- W2782130816 hasConcept C105795698 @default.
- W2782130816 hasConcept C107673813 @default.
- W2782130816 hasConcept C111350023 @default.
- W2782130816 hasConcept C11413529 @default.
- W2782130816 hasConcept C124101348 @default.
- W2782130816 hasConcept C144986985 @default.
- W2782130816 hasConcept C158424031 @default.
- W2782130816 hasConcept C171606756 @default.
- W2782130816 hasConcept C177769412 @default.
- W2782130816 hasConcept C19499675 @default.
- W2782130816 hasConcept C19875794 @default.
- W2782130816 hasConcept C204693719 @default.
- W2782130816 hasConcept C26004113 @default.
- W2782130816 hasConcept C33923547 @default.
- W2782130816 hasConcept C41008148 @default.
- W2782130816 hasConcept C98763669 @default.
- W2782130816 hasConceptScore W2782130816C105795698 @default.
- W2782130816 hasConceptScore W2782130816C107673813 @default.
- W2782130816 hasConceptScore W2782130816C111350023 @default.
- W2782130816 hasConceptScore W2782130816C11413529 @default.
- W2782130816 hasConceptScore W2782130816C124101348 @default.
- W2782130816 hasConceptScore W2782130816C144986985 @default.
- W2782130816 hasConceptScore W2782130816C158424031 @default.
- W2782130816 hasConceptScore W2782130816C171606756 @default.
- W2782130816 hasConceptScore W2782130816C177769412 @default.
- W2782130816 hasConceptScore W2782130816C19499675 @default.
- W2782130816 hasConceptScore W2782130816C19875794 @default.
- W2782130816 hasConceptScore W2782130816C204693719 @default.