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Factor Coding

この資料は次の言語でも読めます: 日本語

Annotations in Japanese to Schad et al (2020) to understand how to code nominal scales using R

Schad, D. J., Vasishth, S., Hohenstein, S., & Kliegl, R. (2020). How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Journal of Memory and Language. https://doi.org/10.1016/j.jml.2019.104038

The annotations also covers the following chapters of textbooks for the psychologists/linguists who want to master statistics:

  1. Vasishth, S., Schad, D., Bürki, A., & Kliegl, R. (In preparation). Contrast coding. In Vasishth, S., Schad, D., Bürki, A., & Kliegl, R. (Eds.), Linear Mixed Models in Linguistics and Psychology: A Comprehensive Introduction. https://vasishth.github.io/Freq_CogSci/basic-concepts-illustrated-using-a-two-level-factor.html
  2. Nicenboim, B., Schad, D., and Vasishth, S. (In preparation). Contrast coding. In Nicenboim, B., Schad, D., and Vasishth, S. (Eds.), An Introduction to Bayesian Data Analysis for Cognitive Science. https://vasishth.github.io/bayescogsci/book/ch-contr.html

What is the difference between this material and the original article?

  • Step-by-step code presentation using flipbookr
  • more effective, more seamless, and more understandable coding following tidyverse style guide
  • written in Japanese
    • one of the first annotations to the original article in non-English languages

License

This is annotations to "How to capitalize on a priori contrasts in linear (mixed) models: A tutorial" in Japanese created by translating/modifying it (© Daniel J Schad, Shravan Vasishth, Sven Hohenstein, Reinhold Kliegl 2020 https://doi.org/10.1016/j.jml.2019.104038 (Licensed under CC BY 4.0)).

The content of this project itself is licensed under the CC BY-NC 4.0. The source code used to format and display that content and the code in the materials are licensed under the MIT license.