Which of the following can be used to calculate the agreement between raters who are using subjective measure?

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BlackTom AI · Accepted Answer

To evaluate this question, consider what statistic best captures how much two or more raters agree when using a subjective measure. 
Option 1: the reliability coefficient. While reliability is related to consistency, this term is generic and does not specify a method for quantifying inter-rater agreement, especially when raters assign categorical or ordinal judgments. It’s too broad to be the precise answer here. 
Option 2: kappa. This is the classic statistic used to measure agreement between raters that goes beyond chance. It adjusts for the amount of agreement that would be expected by random classification, making it appropriate for subjective ratings where exact matches are not guaranteed. 
Option 3: r. This usually refers to Pearson correlation, which assesses linear association between two continuous variables, not agreement between raters on categories or ordered scales. It can be misleading for assessing concordance in ratings. 
Option 4: Spearman's rho. This is a rank-order correlation measure that captures monotonic relationships, not explicit agreement between raters. It does not account for chance agreement and is therefore not the standard choice for inter-rater reliability on subjective assessments.

In summary, among these options, kappa is the designated statistic for measuring agreement between raters when using subjective measures, because it quantifies agreement beyond what would be expected by chance. The other metrics either generalize reliability, or assess correlation rather than agreement, or are inappropriate for the inter-rater context.

Which of the following can be used to calculate the agreement between raters who are using subjective measure?

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