For continuously collected data, the calculation of percentage overreality was developed using time window analysis. Intervals of one second are imposed on the data streams of two observers and comparisons of seconds are made between them. If both data sets show an event (for discrete behaviors) or a second of common occurrence (for behaviors measured over time), this is counted as a concordance. Every second that a single record contains an event or behavior is a disagreement. The percentage of match is calculated by diverging the number of chords by the number of chords plus disagreements. MacLean et al. (1985) understood that their algorithm was too strict for discrete event data. Therefore, they recommended allowing tolerance for counting agreements by broadening the definition of a chord to observations when one observer records an event within the ± t seconds of the other observer. In the research articles studied, t ranged from 1 s (e.g. .B.

Romaniuk et al., 2002) to 5 s (z.B. Lalli, Mauro & Mace, 2000, experiment 3). Partial match in IOA intervals. To get around the described disadvantage, associated with using the IOA algorithm for the total number, the partial concordance in intervals approach (sometimes referred to as the “average number per interval” or “block by block”) breaks down the observation period into small intervals and then examines the concordance in each interval. This increases the accuracy of the compliance measure by reducing the likelihood that the total censuses were inferred from different events of the target responses within the observation. By breaking down the example of observation in Figure 1 into small steps/intervals (15 m intervals), the partial compliance approach calculates the IOA per interval and divided by the total number of intervals. In this case, IOA would be 50% (or .5) for interval 4, 100% (or 1.0) for intervals 5 to 14 (both agreed that 0 target response appeared at each of these intervals), but 0% for intervals 1 to 3 and interval 15. Therefore, the partial concordance approach would be calculated at regular intervals by adding the IOA values (in this case 10.5) to the total number of intervals (15), resulting in a more accurate and lower percentage (70%) of the IOA than the 100% figure obtained with the total counting algorithm. Duration-based IOA algorithms evaluate the concordance between the timing data of two observers. These measures consist of (a) the total duration and (b) the average duration per presence. Table 3 summarizes the strengths of both algorithms.

Consider as a common example of a time-based IOA the hypothetical data flow shown in Figure 3, in which two independent observers record the duration of a target response across four deposits. Interobserver`s compliance for the review process was assessed according to a stratified procedure in which approximately 20% of the articles at each level were randomly selected for independent review by the second author….

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