Latent feature extraction for process data via multidimensional scaling

DOI10.1007/S11336-020-09708-3zbMath1458.62279arXiv1904.09699OpenAlexW3035833574WikidataQ96642525 ScholiaQ96642525MaRDI QIDQ2220372

Xueying Tang, Qiwei He, Jingchen Liu, Zhiliang Ying, Zhi Wang

Publication date: 22 January 2021

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1904.09699

zbMATH Keywords

multidimensional scalinglog file analysisPIAACresponse process

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Applications of statistics to psychology (62P15)

Related Items (4)

Combining clickstream analyses and graph-modeled data clustering for identifying common response processes ⋮ Accurate assessment via process data ⋮ A continuous-time dynamic choice measurement model for problem-solving process data ⋮ ProcData: an R package for process data analysis

Cites Work

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• Modern multidimensional scaling. Theory and applications.
• A Similarity Measure for Sequences of Categorical Data Based on the Ordering of Common Elements
• Cognitive diagnosis modelling incorporating item response times
• 11 Applications of Multidimensional Scaling in Psychometrics
• A Stochastic Approximation Method

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