High Percent Agreement Low Kappa

The probability of coincidence probabilities for Cohens Kappa (e (K)) and Gwets AC1 (e(γ)) were calculated based on the above formulas, and in situations where the limit number was zero (the raters had a 100% agreement), as they were found for the Avoidant, Dependent, Passive-Aggressive and Paranoid PDs in the TW-SR and NW-SR pairs. Cohens Kappa gave a value of 0 for them all, while Gwets AC1 got a value of 0.858 for Avoidant and 0.890 for the other three PDs – the closest in terms of compliance level (Cohen`s Kappa could not be calculated with the SPSS program, as at least one variable in each 2-way table on which the association was calculated). There are actually two categories of reliability for data collectors: reliability on multiple data collectors, which is interrater-reliability, and the reliability of a single data collector called intrarater reliability. For a single data collector, the question is: in the same situation and phenomenon, will a person interpret the data in the same way and, with each collection of that data, record exactly the same value for the variable? Intuitively, it might appear that a person would behave in the same way with respect to the same phenomenon every time the data collector observes this phenomenon. However, research shows the error of this hypothesis. A recent study on the reliability of the intrarater in the assessment of X-rays for bone density revealed reliability coefficients of up to 0.15 and up to 0.90 (4). It is clear that researchers are right to carefully examine the reliability of data collection, as part of their concern for specific research results. Mchugh, M. L. (2012). The reliability of the interrater: the statistic of kappa meaning the measurement of the reliability of the interrater. Biochemia Medica, 22 (3), 276-282. Gwet KL: Calculating the reliability of inter-raters and their high-compliance variance.

Br J Math Stat Psychol. 2008, 61: 29-48. 10.1348/000711006X126600. A final concern about the reliability of advisors was introduced by Jacob Cohen, a leading statistician who, in the 1960s, developed key statistics for measuring the reliability of interratism, Cohens Kappa (5). Cohen indicated that there will likely be some degree of match among data collectors if they do not know the correct answer, but if they simply guess. He assumed that a number of conjectures would be speculated and that insurance statistics should be responsible for this fortuitous agreement. He developed Kappa`s statistics as an understanding of this random agree factor. To get the standard kappa error (SE), the following formula should be used: Gisev N, Bell JS, Chen TF: Interrater And Interrater-reliability Agreement: key concepts, approaches and applications. Res Social Adm Pharm.

In the press, you can try another randomly adjusted index that contains assumptions other than Cohen`s coefficients for the “Kappa.” Such an option would be Bennett et al. the score $S below, $q being the number of possible categories. In this example, if you adopt the same three category options, $S would be higher. Percentage agreement calculation (fictitious data). This is a simple procedure when the values are zero and one and the number of data collectors is two. If there are more data collectors, the procedure is a little more complex (Table 2). However, as long as the values are limited to only two values, the calculation remains simple. The researcher calculates only the percentage agreement for each line and on average the lines. Another advantage of the matrix is that it allows the researcher to determine whether errors are accidental and are therefore fairly evenly distributed among all flows and variables, or whether a data collector often indicates different values from other data collectors. Table 2, which has an overall reliability of 90% for interraters, found that no data collector had an excessive number of outlier assessments (scores that did not agree with the majority of the evaluators` scores).