Simulation-based Bayesian inference for latent traits of item response models: Introduction to the ltbayes package for R

被引:0
|
作者
Timothy R. Johnson
Kristine M. Kuhn
机构
[1] University of Idaho,Department of Statistical Science
[2] Washington State University,Department of Management, Information Systems, and Entrepreneurship
来源
Behavior Research Methods | 2015年 / 47卷
关键词
Item response theory; Bayesian statistics;
D O I
暂无
中图分类号
学科分类号
摘要
This paper introduces the ltbayes package for R. This package includes a suite of functions for investigating the posterior distribution of latent traits of item response models. These include functions for simulating realizations from the posterior distribution, profiling the posterior density or likelihood function, calculation of posterior modes or means, Fisher information functions and observed information, and profile likelihood confidence intervals. Inferences can be based on individual response patterns or sets of response patterns such as sum scores. Functions are included for several common binary and polytomous item response models, but the package can also be used with user-specified models. This paper introduces some background and motivation for the package, and includes several detailed examples of its use.
引用
收藏
页码:1309 / 1327
页数:18
相关论文
共 50 条
  • [41] Using Bayesian Inference Modeling in Estimating Important Production Parameters Used in the Simulation-based Production Planning
    Jen, Hen-yi
    Hsiao, Chun-yi
    PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ), 2018, : 1038 - 1041
  • [42] Simulation-based Evaluation of the Reliability of Bayesian Hierarchical Models for sc-RNAseq Data
    Li, Sijia
    Lopez-Garcia, Martin
    Cutillo, Luisa
    20TH INT CONF ON UBIQUITOUS COMP AND COMMUNICAT (IUCC) / 20TH INT CONF ON COMP AND INFORMATION TECHNOLOGY (CIT) / 4TH INT CONF ON DATA SCIENCE AND COMPUTATIONAL INTELLIGENCE (DSCI) / 11TH INT CONF ON SMART COMPUTING, NETWORKING, AND SERV (SMARTCNS), 2021, : 345 - 352
  • [43] bayesanova: An R package for Bayesian Inference in the Analysis of Variance via Markov Chain Monte Carlo in Gaussian Mixture Models
    Kelter, Riko
    R JOURNAL, 2022, 14 (01): : 54 - 78
  • [44] Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
    Kulkarni, Sourabh
    Krell, Mario Michael
    Nabarro, Seth
    Moritz, Csaba Andras
    ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 2022, 18 (02)
  • [45] Identification-robust simulation-based inference in joint discrete/continuous models for energy markets
    Bolduc, Denis
    Khalaf, Lynda
    Moyneur, Erick
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2008, 52 (06) : 3148 - 3161
  • [46] Finite-sample simulation-based inference in VAR models with application to Granger causality testing
    Dufour, Jean-Marie
    Jouini, Tarek
    JOURNAL OF ECONOMETRICS, 2006, 135 (1-2) : 229 - 254
  • [47] Bayesian inference on prevalence using a missing-data approach with simulation-based techniques: Applications to HIV screening
    MendozaBlanco, JR
    Tu, XM
    Iyengar, S
    STATISTICS IN MEDICINE, 1996, 15 (20) : 2161 - 2176
  • [48] Discard ban: A simulation-based approach combining hierarchical Bayesian and food web spatial models
    Grazia Pennino, Maria
    Helena Bevilacqua, Ana
    Angeles Torres, M.
    Bellido, Jose M.
    Sole, Jordi
    Steenbeek, Jeroen
    Coll, Marta
    MARINE POLICY, 2020, 116
  • [49] Robust Bayesian optimization for flexibility analysis of expensive simulation-based models with rigorous uncertainty bounds
    Kudva, Akshay
    Tang, Wei-Ting
    Paulson, Joel A.
    COMPUTERS & CHEMICAL ENGINEERING, 2024, 181
  • [50] Modeling Unproductive Behavior in Online Homework in Terms of Latent Student Traits: An Approach Based on Item Response Theory
    Emre Gönülateş
    Gerd Kortemeyer
    Journal of Science Education and Technology, 2017, 26 : 139 - 150