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I-CAL_final version.png (162.58 kB)
DATASET
Correspondence analysis 22 February 2021.xls (79 kB)
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Krippendorff alpha for inter_rater reliability.pdf (880.38 kB)
DATASET
Narratives per GoldCriterion 19 November 2020.xlsx (17.95 kB)
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Pathways for factor analysis.xlsx (1.36 MB)
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A_Frequencies of Student per Undergraduate, Postgraduate and Overall_All cases 2 November 2020.docx (21.53 kB)
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B_Cross tables 10 November 2020.docx (31 kB)
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A_Frequencies of Student per Undergraduate, Postgraduate and Overall_All cases 2 November 2020.docx (21.53 kB)
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Frequencies Pathways per Undergraduate, Postgraduate and Overall _ All cases 2 November 2020.docx (41.25 kB)
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A_Cross tables Gold standar by Student 15 December 2020.docx (35.23 kB)
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B_Cross tables Gold standard by Student Correct cases excluded 17 February 2021.docx (43.79 kB)
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D_Legal Status validity testing.docx (15.06 kB)
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regression plain with case accuracy.doc (1.24 MB)
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regression stepwise with case accuracy.doc (495.13 kB)
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Predictive validity and reliability of tests with intelligent collector algorithms (ICAL) on statistical analysis using Krippendorff's alpha
journal contribution
posted on 2022-07-06, 08:30 authored by Gerhard GroblerGerhard Grobler, van Staden, W.Datasets for showing the Intelligent Collector Algorithms (ICAL). This was tested for predictive validity and reliability. The predictive validity was tested by correspondence analysis for both the purposively developed narratives and the decision pathways. This was done by cross matching the research subjects' findings with the gold standard outcomes to assess these legal status outcomes. Regression analysis was used to examine the effect of the various clinical complicators. The reliability testing was done by calculating the Cronbach's alpha for assess the internal consistency and the Krippendorf alphas to assess inter rater reliability.
