For indicators with sub-questions or sub-indicators, the team`s consistency value was assessed separately for each sub-question and then averaged through the sub-questions of that indicator. We calculated the average percentage of correspondence between all MMS teams to measure the IRR for one indicator (i.e. the proportion of teams that reached 100%). An illustration of the calculation of the IRR score for indicators, sub-indicators and domains is provided (supplementary file 3). While evaluators tend to agree, the differences between evaluators` observations are close to zero. When one appraiser is generally higher or lower than the other by a consistent amount, the distortion of zero is different. If evaluators tend to disagree, but in the absence of a consistent model where one rating is higher than the other, the average is close to zero. Confidence limits (usually 95%) can be calculated both for distortion and for each of the compliance limits. The IRR assessment quantifies the degree of agreement between two or more programmers who issue independent assessments of the characteristics of a number of subjects. In this article, topics are used as generic terms for people, things, or events evaluated in a study, for example. B the frequency with which a child uses a reference person, the level of empathy demonstrated by an interviewer, or the presence or absence of a psychological diagnosis. Coders are used as an umbrella term for people who assign ratings in a study, for example.B trained research assistants or randomly selected participants. Bland and Altman expanded this idea by graphically representing the difference of each point, the mean difference, and the limits of the vertical concordance against the mean of the two horizontal evaluations.
The resulting Bland Altman chart shows not only the overall degree of compliance, but also whether the agreement is related to the underlying value of the item. For example, two evaluators might closely agree on estimating the size of small elements, but would disagree on larger elements. Kappa statistics measure the degree of agreement observed between coders for a number of nominal notations and correct the concordance that is expected by chance and provide a standardized index of IRR, which can be generalized through studies. The degree of correspondence observed is determined by cross-evaluations for two programmers, and the expected random match is determined by the limits of each encoder`s ratings. Kappa is based on the equation Higher ICC values indicate a higher IRR, an ICC estimate of 1 showing a perfect match and 0 only indicates a random match. Negative CCI estimates indicate systematic discrepancies and some KICs may be less than −1 if there are three or more coders. Cicchetti (1994) provides often cited cutoffs for qualitative conformity assessments based on ICC values, the IRR being bad for ICC values below .40, just for values ranging from .40 to .59, good for values between .60 and .74 and for values between .75 and 1.0. Intraclass correlation (ICC) is one of the most frequently used statistics to assess IRR for ordinal, intermittent and relational variables. CICs are suitable for studies involving two or more coders and can be used if all subjects are evaluated in a study by several programmers or if only a portion of subjects are evaluated by several programmers and the rest by one encoder. CICs are suitable for completely cross-design or if a new set of encoders is randomly selected for each participant.
Unlike Cohens (1960) kappa, who quantifies the IRR on the basis of an all-or-nothing agreement, CICs take into account the size of the disagreement in calculating irr estimates, with larger disagreements leading to smaller ONES.
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- On 8 septiembre, 2021
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