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Item Response Theory (IRT) is a powerful statistical approach for evaluating test items and determining test taker abilities through response analysis.
On the convergence of adam and beyond
Reddi, S. J., Kale, S., and Kumar, S. (2019) · 1904
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A mathematical theory of communication
Shannon, C. E. (1948) · 1948
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Some latent trait models and their use in inferring an examinee’s ability
Birnbaum, A. (1968) · 1968
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Estimating item parameters and latent ability when responses are scored in two or more nominal categories
Bock, R. D. (1972) · 1972
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The ‘ability’ scale in item characteristic curve theory
Lord, F. M. (1975) · 1975
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A new family of models for the multiple choice item
Samejima, F. (1979) · 1979
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Applications of item response theory to practical testing problems
Lord, F. M. (1980) · 1980
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Marginal maximum likelihood estimation of item parameters: application of an EM algorithm
Bock, R. D. and Aitkin, M. (1981) · 1981
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Estimating latent distributions
Mislevy, R. J. (1984) · 1984
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A response model for multiple choice items
Thissen, D. and Steinberg, L. (1984) · 1984
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Modeling item responses when different subjects employ different solution strategies
Mislevy, R. J. and Verhelst, N. (1990) · 1990
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Nonlinear principal component analysis using autoassociative neural networks
Kramer, M. A. (1991) · 1991
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Kernel smoothing approaches to nonparametric item characteristic curve estimation
Ramsay, J. O. (1991) · 1991
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A generalized partial credit model: Application of an EM algorithm
Muraki, E. (1992) · 1992
Cited alongside, same era.
Incorporating second-order functional knowledge for better option pricing
Dugas, C., Bengio, Y., Bélisle, F., Nadeau, C., and Garcia, R. (2000) · 2000
Cited alongside, same era.
Variational item response theory: Fast, accurate, and expressive
Wu, M., Davis, R. L., Domingue, B. W., Piech, C., and Goodman, N. (2020) · 2002
Cited alongside, same era.
Item Response Theory: Parameter Estimation Techniques
Baker, F. B. and Kim, S.-H. (2004) · 2004
Cited alongside, same era.
Modeling dichotomous item responses with free-knot splines
Johnson, M. S. (2007) · 2007
Cited alongside, same era.
Ramsay-curve item response theory for the three-parameter logistic item response model
Handbook of Item Response Theory, Volume 3: Applications
van der Linden, W. J. (2018) · 2018
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019) · 2019
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Optimal scores: an alternative to parametric item response theory and sum scores
Wiberg, M., Ramsay, J. O., and Li, J. (2019) · 2019
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Better rating scale scores with information–based psychometrics
Ramsay, J. O., Li, J., and Wiberg, M. (2020) · 2020
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A deep learning algorithm for high-dimensional exploratory item factor analysis
Urban, C. J. and Bauer, D. J. (2021) · 2021
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Exploring factor structures using variational autoencoder in personality research
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Woods, C. M. (2008) · 2008
Cited alongside, same era.
The elements of statistical learning: data mining, inference, and prediction
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009) · 2009
Cited alongside, same era.
Item response theory with estimation of the latent density using davidian curves
Woods, C. M. and Lin, N. (2009) · 2009
Cited alongside, same era.
mirt: a multidimensional item response theory package for the R environment
Chalmers, R. P. (2012) · 2012
Cited alongside, same era.
Methods and Procedures in TIMSS 2015
Martin, M. O., Mullis, I. V. S., and Hooper, M., editors (2016) · 2015
Cited alongside, same era.
Maximum marginal likelihood estimation of a monotonic polynomial generalized partial credit model with applications to multiple group analysis
Falk, C. F. and Cai, L. (2016) · 2016
Cited alongside, same era.
Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J. (2018) · 2018
Cited alongside, same era.
Huang, Y. and Zhang, J. (2022) · 2022
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Sparse factor autoencoders for item response theory
Paaßen, B., Dywel, M., Fleckenstein, M., and Pinkwart, N. (2022) · 2022
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Modeling item response theory with stochastic variational inference
Wu, M., Davis, R. L., Domingue, B. W., Piech, C., and Goodman, N. (2022) · 2022
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Constrained monotonic neural networks
Runje, D. and Shankaranarayana, S. M. (2023) · 2023
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Analyzing polytomous test data: A comparison between an information-based IRT model and the generalized partial credit model
Wallmark, J., Ramsay, J. O., Li, J., and Wiberg, M. (2023) · 2023
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TestGardener: optimal analysis of test and rating scale data
Ramsay, J. O. and Li, J. (2024) · 2024
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IRTorch: Item response theory with Python
Wallmark, J. (2024) · 2024
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