IoA with undotted interval. The IOA algorithm with a little interval (also called « non-deposit » agreement in the research literature) is also stricter than simple interval-by-interval approaches, taking into account only intervals in which at least one observer records the lack of response. The justification for pointless IOA is similar to that of the IOA with the scored interval, except that this metric responds best for high rates (Cooper et al., 2007). In the figure 2 examples, the 5th and 6th intervals are ignored for calculation purposes, as both observers have received a response at these intervals. Thus, the IOA statistics are calculated from the remaining five intervals. Since agreement has only been reached on three of the five intervals (the second, third and fourth intervals), the approval rate is 60%. Test s.i.A. IOA. Savvy readers will find that IOA algorithms based on the above events are adapted to free-operator responses, responses that can occur at any time and are not anchored in events, but these measures do not explicitly take into account the experience-based reaction, which measures binary results (e.g. B presence/non-presence, yes/no, on-task/task).
Thus, the experimental IOA measures the number of trials with consent divided by the total number of trials. This metric is as strict as the exact approach to the agreement. In the rest of this article, three general categories of reliability metrics are explained: (a) on the basis of events, (b) on the basis of intervals and (c) on the basis of duration. Event-based measurements can be considered any form of IOA based on data collected using event records or frequency counts during observations. Interval-based data is provided from data collected through interval recordings (for example. B, partial or partial recordings) or as part of a regular sampling procedure. Finally, time-based algorithms are used when data is derived from timing (for example. B latency, durations, inter-response time). Interested readers can view each of the three tables in this manuscript based on the relative strengths of each algorithm and view the mathematical form of each algorithm in Appendix A.
Nevertheless, the user should always consult the research literature in order to obtain accurate information about when, why and how algorithms are used, given the nuanced aspects of their data. Note that the calculator table, an example of a calculation table completed with a brief explanation of the IOA analyses, is available on the « Behavioural Analysis in the Http://www.abainternational.org/Journals/bap_supplements.asp » site of the practice. Interested readers are encouraged to discuss IOA algorithms further and information on how to opt for a good metric on pages 114 to 120 of Cooper et al. (2007). Despite the general expectation that analysts will be aware of the various calculations of insurance statistics (BACB, 1995) and report IOA in their behavioural assessments (Kelly, 1977; Mudford et al., 2009), competing professional responsibilities may exclude appropriate analysis of IOA in the clinical environment. Beyond the barriers to competing responsibilities, Vollmer et al. (2008) outline other obstacles to the precise calculation of reliability. These obstacles include: (a) inadequate training in measurement protocols; b) the complexity of the procedure/analysis and/or c) the inability to generalize these data collection/analysis capabilities from teaching situations.