Corpus ID: 63550204

Construction of the Berkeley Innovation Index: A Higher-Order Item Response Theory Model Approach

@inproceedings{Larsson2016ConstructionOT,
  title={Construction of the Berkeley Innovation Index: A Higher-Order Item Response Theory Model Approach},
  author={Johan E Larsson and Alexander Fred-Ojala},
  year={2016}
}
  • Johan E Larsson, Alexander Fred-Ojala
  • Published 2016
  • Computer Science
  • The Berkeley Innovation Index (BII) is a tool developed for assessment of individual innovation capability. [...] Key Method In this thesis, the algorithm for the BII is constructed by applying a Higher-Order Item Response Theory model for hierarchical latent trait estimation. Simultaneous estimation of the vast amount of model parameters is done by employing a Markov Chain Monte Carlo (MCMC) method that utilizes a multi-level bayesian inference sampling technique. The validity, feasibility, and usefulness of…Expand Abstract

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 51 REFERENCES

    A Multilevel Higher Order Item Response Theory Model for Measuring Latent Growth in Longitudinal Data.

    • Hung-Yu Huang
    • Mathematics, Medicine
    • Applied psychological measurement
    • 2015
    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Generalized Boosted Models: A guide to the gbm package

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    The development of hierarchical factor solutions

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Measurement of Innovation Mindset A Method and Tool within the Berkeley Innovation Index Framework

    • Ikhlaq Sidhu, Jean-Etienne Goubet, Ye Xia
    • Computer Science
    • 2016 International Conference on Engineering, Technology and Innovation/IEEE lnternational Technology Management Conference (ICE/ITMC)
    • 2016