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  • Complex interactive test items are becoming more widely used in assessments. Being computer-administered, assessments using interactive items allow logging time-stamped action sequences. These sequences pose a rich source of information that may facilitate investigating how examinees approach an item and arrive at their given response. There is a rich body of research leveraging action sequence data for investigating examinees’ behavior. However, the associated timing data have been considered mainly on the item-level, if at all. Considering timing data on the action-level in addition to action sequences, however, has vast potential to support a more fine-grained assessment of examinees’ behavior. We provide an approach that jointly considers action sequences and action-level times for identifying common response processes. In doing so, we integrate tools from clickstream analyses and graph-modeled data clustering with psychometrics. In our approach, we (a) provide similarity measures that are based on both actions and the associated action-level timing data and (b) subsequently employ cluster edge deletion for identifying homogeneous, interpretable, well-separated groups of action patterns, each describing a common response process. Guidelines on how to apply the approach are provided. The approach and its utility are illustrated on a complex problem-solving item from PIAAC 2012. (xsd:string)
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  • 2021 (xsd:gyear)
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  • 2021 (xsd:gyear)
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  • 10.1007/s11336-020-09743-0 ()
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  • true (xsd:boolean)
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  • en (xsd:string)
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?:issn
  • 1860-0980 ()
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  • 1 (xsd:string)
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  • Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes (xsd:string)
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  • Zeitschriftenartikel (xsd:string)
  • journal_article (en)
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  • GESIS-SSOAR (xsd:string)
  • In: Psychometrika, 86, 2021, 1, 190-214 (xsd:string)
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  • urn:nbn:de:0168-ssoar-85084-7 ()
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  • 86 (xsd:string)