Results 251 to 260 of about 5,578,457 (308)
Refractory Psychosis as a Red Flag for End of Life in Individuals With Dementia With Lewy Bodies: A Case Series and Re-analysis of Prior Qualitative Data. [PDF]
Armstrong MJ +8 more
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Prompts, Pearls, Imperfections: Comparing ChatGPT and a Human Researcher in Qualitative Data Analysis. [PDF]
Wachinger J +4 more
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Patient lived experiences of functioning and disability following lumbar discectomy: a secondary analysis of qualitative data. [PDF]
Mistry J +9 more
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Assessing qualitative data richness and thickness: Development of an evidence-based tool for use in qualitative evidence synthesis. [PDF]
Ames HMR +7 more
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Patient feasibility as a novel approach for integrating IRT and LCA statistical models into patient-centric qualitative data-a pilot study. [PDF]
Klüglich M +4 more
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Conceptualizing Qualitative Data
Qualitative Health Research, 2021Qualitative research is practiced across diverse disciplines and contexts, and this produces a wide range of perspectives on the role of conceptualization and theory development. It also results in a hugely varied mix of submissions to qualitative research journals in terms of their level of conceptual elevation.
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Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces, 2017
Evaluation is increasingly recognized as an essential component of HCI research. However, evaluation itself is a changing research area. In particular, the many variations of qualitative research are emerging as important empirical methods. This half-day tutorial is designed for beginning to intermediate audiences.
Sheelagh Carpendale +3 more
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Evaluation is increasingly recognized as an essential component of HCI research. However, evaluation itself is a changing research area. In particular, the many variations of qualitative research are emerging as important empirical methods. This half-day tutorial is designed for beginning to intermediate audiences.
Sheelagh Carpendale +3 more
openaire +1 more source
Proceedings of the VLDB Endowment, 2016
Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and wrong business decisions. Data cleaning exercise often consist of two phases: error detection and error repairing.
Xu Chu, Ihab F. Ilyas
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Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and wrong business decisions. Data cleaning exercise often consist of two phases: error detection and error repairing.
Xu Chu, Ihab F. Ilyas
openaire +1 more source

