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Latent Feature Extraction for Process Data via Multidimensional Scaling

Author

Listed:
  • Xueying Tang

    (University of Arizona)

  • Zhi Wang

    (Columbia University)

  • Qiwei He

    (Educational Testing Service)

  • Jingchen Liu

    (Columbia University)

  • Zhiliang Ying

    (Columbia University)

Abstract

Computer-based interactive items have become prevalent in recent educational assessments. In such items, detailed human–computer interactive process, known as response process, is recorded in a log file. The recorded response processes provide great opportunities to understand individuals’ problem solving processes. However, difficulties exist in analyzing these data as they are high-dimensional sequences in a nonstandard format. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory analysis that extracts latent variables from process data through a multidimensional scaling framework. A dissimilarity measure is described to quantify the discrepancy between two response processes. The proposed method is applied to both simulated data and real process data from 14 PSTRE items in PIAAC 2012. A prediction procedure is used to examine the information contained in the extracted latent variables. We find that the extracted latent variables preserve a substantial amount of information in the process and have reasonable interpretability. We also empirically prove that process data contains more information than classic binary item responses in terms of out-of-sample prediction of many variables.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:2:d:10.1007_s11336-020-09708-3
    DOI: 10.1007/s11336-020-09708-3
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    References listed on IDEAS

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    1. R. Klein Entink & J.-P. Fox & W. Linden, 2009. "A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers," Psychometrika, Springer;The Psychometric Society, vol. 74(1), pages 21-48, March.
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    Cited by:

    1. Esther Ulitzsch & Qiwei He & Vincent Ulitzsch & Hendrik Molter & André Nichterlein & Rolf Niedermeier & Steffi Pohl, 2021. "Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 190-214, March.
    2. Esther Ulitzsch & Qiwei He & Steffi Pohl, 2022. "Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 3-35, February.
    3. 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.
    4. 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.
    5. Pujue Wang & Hongyun Liu, 2024. "Polytomous Effectiveness Indicators in Complex Problem-Solving Tasks and Their Applications in Developing Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 877-902, September.

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