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Package accounts October 13, 2022, Title Infraclass Correlation Coefficient for Count Data Version 1.1.1 Author Joseph L. Carrasco jlcarrasco@ub.edu Maintainer Joseph L. Carrasco jlcarrasco@ub.edu Depends
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01
Determine the number of raters or observers (n) involved in the assessment.
02
Calculate the mean square for raters using the formula: MSr = Σ(ri)^2 / n, where ri is the sum of scores for each rater and n is the number of raters.
03
Calculate the mean square for subjects using the formula: MSs = Σ(sj)^2 / p, where sj is the sum of scores for each subject and p is the number of subjects.
04
Calculate the mean square for error using the formula: MSe = MSw - MSr - MSs, where MSw is the overall mean square.
05
Calculate the intraclass correlation coefficient using the formula: ICC = (MSr - MSe) / (MSr + (k-1)*MSe), where k is the average number of ratings per subject.

Who needs icccounts intraclass correlation coefficient?

01
Researchers who are conducting studies where multiple raters assess the same subjects or objects.
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Psychologists and social scientists looking to assess the reliability and agreement among raters or observers.
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Healthcare professionals who need to evaluate the consistency of assessments made by different individuals.
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ICC accounts for the correlation between data points within the same group.
Researchers or statisticians conducting studies involving within-group correlation.
The coefficient can be calculated using statistical software like SPSS or R based on the data collected.
It is used to assess the reliability or consistency of measurements taken within the same group.
The data points, groupings, and the resulting ICC value.
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