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    ProcData: An R Package for Process Data Analysis

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    ProcData_psychm_R2.pdf
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    Description:
    Final Accepted Manuscript
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    Author
    Tang, Xueying
    Zhang, Susu
    Wang, Zhi
    Liu, Jingchen
    Ying, Zhiliang
    Affiliation
    University of Arizona
    Issue Date
    2021-08-11
    Keywords
    autoencoder
    multidimensional scaling
    process data analysis
    sequence model
    
    Metadata
    Show full item record
    Publisher
    Springer
    Citation
    Tang, X., Zhang, S., Wang, Z., Liu, J., & Ying, Z. (2021). ProcData: An R Package for Process Data Analysis. Psychometrika.
    Journal
    Psychometrika
    Rights
    © 2021 The Psychometric Society
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    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. © 2021, The Psychometric Society.
    Note
    12 month embargo; published: 11 August 2021
    ISSN
    0033-3123
    EISSN
    1860-0980
    DOI
    10.1007/s11336-021-09798-7
    Version
    Final accepted manuscript
    Sponsors
    National Science Foundation
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11336-021-09798-7
    Scopus Count
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    UA Faculty Publications

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