Among all event-based IOA algorithms, analysis of the match between frequency counts and event records is usual. These measures consist of (a) the global census, (b) partial agreement at regular intervals, (c) a precise agreement and (d) IOA trial test algorithms. After a brief overview of the different event-based algorithms, Table 1 summarizes the strengths of the four event-based algorithms for behavioral reliability analysis considerations. Suppose a research team collects frequency data to respond to 15-1 m observations (see Figure 1). 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. 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%. The idea that practicing behavioural analysts should collect and report reliability or interobserver agreement (IOA) in behavioural assessments is demonstrated by the Behavior Analyst Certification Board`s (BACB) assertion that behavioural analysts are responsible for the use of “different methods of evaluating the results of measurement methods such as inter-observer agreement, accuracy and reliability” (BACB, 2005). In addition, Vollmer, Sloman and St. Peter Pipkin (2008) argue that the exclusion of these data significantly limits any interpretation of the effectiveness of a behavioural change procedure. Validity requirements in a behavioural assessment study should therefore be conditional on the inclusion of insurance data (Friman, 2009). In light of these considerations, it is not surprising that a recent review of articles in the Journal of Applied Behavior Analysis (JABA) from 1995 to 2005 (Mudford, Taylor, Martin, 2009) revealed that 100% of articles reporting continuously recorded dependent variables contained IOA calculations.