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ProcData: An R Package for Process Data Analysis

Author

Listed:
  • Xueying Tang

    (University of Arizona)

  • Susu Zhang

    (University of Illinois at Urbana-Champaign)

  • Zhi Wang

    (Columbia University)

  • Jingchen Liu

    (Columbia University)

  • Zhiliang Ying

    (Columbia University)

Abstract

Process data refer to data recorded in log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents’ response problem-solving behaviors. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for inspecting, processing, and analyzing process data. We define an S3 class ‘proc’ for organizing process data and extend generic methods summary and print for ‘proc’. Feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for making predictions from neural-network-based sequence models. In addition, a real dataset of response processes from the climate control item in the 2012 Programme for International Student Assessment is included in the package.

Suggested Citation

  • Xueying Tang & Susu Zhang & Zhi Wang & Jingchen Liu & Zhiliang Ying, 2021. "ProcData: An R Package for Process Data Analysis," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 1058-1083, December.
    • Handle: RePEc:spr:psycho:v:86:y:2021:i:4:d:10.1007_s11336-021-09798-7
    DOI: 10.1007/s11336-021-09798-7
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    References listed on IDEAS

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    1. Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang, 2019. "Statistical analysis of complex problem-solving process data: an event history analysis approach," LSE Research Online Documents on Economics 100871, London School of Economics and Political Science, LSE Library.
    2. Xueying Tang & Zhi Wang & Qiwei He & Jingchen Liu & Zhiliang Ying, 2020. "Latent Feature Extraction for Process Data via Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 378-397, June.
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    Cited by:

    1. Susu Zhang & Zhi Wang & Jitong Qi & Jingchen Liu & Zhiliang Ying, 2023. "Accurate Assessment via Process Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 76-97, March.

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