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?:abstract
  • Response process data collected from human–computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem-solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess the performance of the new method. We use a case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be carried out with the new approach. (xsd:string)
?:author
?:comment
  • (ISSP) (xsd:string)
?:dataSource
  • PIAAC-Bibliography (xsd:string)
?:dateModified
  • 2023 (xsd:gyear)
?:datePublished
  • 2023 (xsd:gyear)
?:doi
  • 10.1111/bmsp.12290 ()
?:duplicate
?:eprint
?:fromPage
  • 211 (xsd:string)
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  • english (xsd:string)
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?:issueNumber
  • 1 (xsd:string)
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?:name
  • Subtask analysis of process data through a predictive model (xsd:string)
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  • article (xsd:string)
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  • Bibsonomy (xsd:string)
  • In British Journal of Mathematical and Statistical Psychology, 76(1), 211-235, 2023 (xsd:string)
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  • PIAAC - Programme for the International Assessment of Adult Competencies (xsd:string)
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  • 2023 (xsd:string)
  • FDZ_PIAAC (xsd:string)
  • FDZbericht (xsd:string)
  • PIAAC (xsd:string)
  • PIAAC_input2022 (xsd:string)
  • PIAAC_pro (xsd:string)
  • SCIEindexed (xsd:string)
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  • article (xsd:string)
  • english (xsd:string)
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  • 235 (xsd:string)
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?:volumeNumber
  • 76 (xsd:string)