Calculating inter-annotator agreement

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Are there situations when it is allowed to omit calculating expected agreement but use only observed agreement as reliable measure? I have multi-label classification (in particular annotation of semantic relations between words in a sentence, so the probability of chance agreement is very low) with multiple annotators.










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    Are there situations when it is allowed to omit calculating expected agreement but use only observed agreement as reliable measure? I have multi-label classification (in particular annotation of semantic relations between words in a sentence, so the probability of chance agreement is very low) with multiple annotators.










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      Are there situations when it is allowed to omit calculating expected agreement but use only observed agreement as reliable measure? I have multi-label classification (in particular annotation of semantic relations between words in a sentence, so the probability of chance agreement is very low) with multiple annotators.










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      Are there situations when it is allowed to omit calculating expected agreement but use only observed agreement as reliable measure? I have multi-label classification (in particular annotation of semantic relations between words in a sentence, so the probability of chance agreement is very low) with multiple annotators.







      expected-value inter-rater cohens-kappa






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          Yes; it is perfectly in order to calculate the proportion of agreement and give a confidence interval for it. In fact this is often helpful even in situations where the raters are only using two categories but one of them is very rare. In such cases Cohen's $kappa$ can be low while proportion of agreement is high, so presenting both gives a better picture. I would suggest giving both in your case too to let the reader see what exactly is happening.






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            Yes; it is perfectly in order to calculate the proportion of agreement and give a confidence interval for it. In fact this is often helpful even in situations where the raters are only using two categories but one of them is very rare. In such cases Cohen's $kappa$ can be low while proportion of agreement is high, so presenting both gives a better picture. I would suggest giving both in your case too to let the reader see what exactly is happening.






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              Yes; it is perfectly in order to calculate the proportion of agreement and give a confidence interval for it. In fact this is often helpful even in situations where the raters are only using two categories but one of them is very rare. In such cases Cohen's $kappa$ can be low while proportion of agreement is high, so presenting both gives a better picture. I would suggest giving both in your case too to let the reader see what exactly is happening.






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                up vote
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                Yes; it is perfectly in order to calculate the proportion of agreement and give a confidence interval for it. In fact this is often helpful even in situations where the raters are only using two categories but one of them is very rare. In such cases Cohen's $kappa$ can be low while proportion of agreement is high, so presenting both gives a better picture. I would suggest giving both in your case too to let the reader see what exactly is happening.






                share|cite|improve this answer














                Yes; it is perfectly in order to calculate the proportion of agreement and give a confidence interval for it. In fact this is often helpful even in situations where the raters are only using two categories but one of them is very rare. In such cases Cohen's $kappa$ can be low while proportion of agreement is high, so presenting both gives a better picture. I would suggest giving both in your case too to let the reader see what exactly is happening.







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                edited 1 hour ago









                Nick Cox

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                answered 2 hours ago









                mdewey

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                11.1k72041




















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