Detecting changes in simulated events II: Using variations of momentary time‐sampling to measure changes in duration events

Carroll, Regina A.; Rapp, John T.; Colby-Dirksen, Amanda M.; Lindenberg, Ally M.

doi:10.1002/bin.286

Cited by 10 publications

(24 citation statements)

References 4 publications

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“…They examined the line graphs generated by these alternative measurement systems and found that, although there was additional error represented in their analysis, the overall interpretations of figures by an expert panel remained unchanged relative to those collected using continuous recording in the majority of cases. Thus, from a pragmatic perspective, error is only problematic to the extent to which it changes interpretation of the data (for similar evaluations, see Carroll, Rapp, Colby-Dirksen, & Lindenberg, 2009;Devine, Rapp, Testa, Henrickson, & Schnerch, 2011;Rapp et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

On the Representativeness of Behavior Observation Samples in Classrooms

Tiger

Miller

Mevers

et al. 2013

Journal of Applied Behavior Analysis

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School consultants who rely on direct observation typically conduct observational samples (e.g., 1 30-min observation per day) with the hopes that the sample is representative of performance during the remainder of the day, but the representativeness of these samples is unclear. In the current study, we recorded the problem behavior of 3 referred students for 4 consecutive school days between 9:30 a.m. and 2:30 p.m. using duration recording in consecutive 10-min sessions. We then culled 10-min, 20-min, 30-min, and 60-min observations from the complete record and compared these observations to the true daily mean to assess their accuracy (i.e., how well individual observations represented the daily occurrence of target behaviors). The results indicated that when behavior occurred with low variability, the majority of brief observations were representative of the overall levels; however, when behavior occurred with greater variability, even 60-min observations did not accurately capture the true levels of behavior.

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Section: Discussionmentioning

confidence: 99%

On the Representativeness of Behavior Observation Samples in Classrooms

Tiger

Miller

Mevers

et al. 2013

Journal of Applied Behavior Analysis

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“…Carroll et al () noted that there are currently no guidelines for determining an acceptable level of false positives with interval methods of data collection. Given the absence of formal guidelines in the literature, we arbitrarily designated methods that produced 20% or fewer false negatives and 33% or fewer false positives as being ‘sensitive’ to behavior change.…”

Section: Study 2: Evaluating False Positives With Duration Events Andmentioning

confidence: 99%

“…Because of the labor-intensive nature of conducting this analysis, we opted to only evaluate false positives for interval sizes of PIR and MTS that detected 80% or more of changes in duration events or frequency events in study 1. Carroll et al (2009) noted that there are currently no guidelines for determining an acceptable level of false positives with interval methods of data collection. Given the absence of formal guidelines in the literature, we arbitrarily designated methods that produced 20% or fewer false negatives and 33% or fewer false positives as being 'sensitive' to behavior change.…”

Section: Study 2: Evaluating False Positives With Duration Events Andmentioning

confidence: 99%

Detecting Changes in Simulated Events Using Partial‐Interval Recording and Momentary Time Sampling III: Evaluating Sensitivity as a Function of Session Length

Devine

Rapp

Testa

et al. 2011

Behavioral Interventions

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In a series of two studies, we graphed simulated data representing continuous duration recording and continuous frequency recording into ABAB reversal designs depicting small, moderate, and large behavior changes during 10-min, 30-min, and 60-min sessions. Data sets were re-scored using partialinterval recording and momentary time sampling with interval sizes set at 10 s, 20 s, 30 s, 1 min, and 2 min. In study 1, we visually inspected converted data for experimental control and compared the conclusion with those from the respective continuous duration recording or continuous frequency recording data to test for false negatives. In study 2, we evaluated the extent to which interval methods that were sensitive to changes in study 1 produced false positives. In part, the results show that momentary time sampling with interval sizes up to 30 s detected a wide range of changes in duration events and frequency events during lengthier observation periods. The practical implications of the findings are briefly discussed.

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“…By contrast, statistical procedures are routinely used to determine probability of obtaining false positives in group designs. Some recent studies have focused on the probability of obtaining false positives and false negatives with interval recording (e.g., partial interval recording [PIR]) methods in single-subject designs (Carroll, Rapp, Colby-Dirksen, & Lindenberg, 2009;Meany-Daboul, Roscoe, Bourrett, & Ahearn, 2007;Rapp, Colby-Dirksen, Michalski, Carroll, & Lindenberg, 2008;Rapp et al, 2007); however, none has evaluated false positives for continuous measures of behavior. In addition, although some studies have focused on increasing the objectiveness of visual analysis of data depicted within AB (e.g., Fisher, Kelley, & Lomas, 2003;Stewart, Carr, Brandt, & McHenry, 2007) and ABAB designs (Kahng et al, 2010), at SIMON FRASER LIBRARY on June 9, 2015 bmo.sagepub.com Downloaded from relatively few studies have focused on increasing the objectiveness of data depicted within multielement designs.…”

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confidence: 99%

Detecting False Positives in Multielement Designs

2011

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The authors assessed the extent to which multielement designs produced false positives using continuous duration recording (CDR) and interval recording with 10-s and 1-min interval sizes. Specifically, they created 6,000 graphs with multielement designs that varied in the number of data paths, and the number of data points per data path, using a random number generator. In Experiment 1, the authors visually analyzed the graphs for the occurrence of false positives. Results indicated that graphs depicting only two sessions for each condition (e.g., a control condition plotted with multiple test conditions) produced the highest percentage of false positives for CDR and interval recording with 10-s and 1-min intervals. Conversely, graphs with four or five sessions for each condition produced the lowest percentage of false positives for each method. In Experiment 2, they applied two new rules, which were intended to decrease false positives, to each graph that depicted a false positive in Experiment 1. Results showed that application of new rules decreased false positives to less than 5% for all of the graphs except for those with two data paths and two data points per data path. Implications for brief assessments are discussed.

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Detecting changes in simulated events II: Using variations of momentary time‐sampling to measure changes in duration events

Cited by 10 publications

References 4 publications

On the Representativeness of Behavior Observation Samples in Classrooms

On the Representativeness of Behavior Observation Samples in Classrooms

Detecting Changes in Simulated Events Using Partial‐Interval Recording and Momentary Time Sampling III: Evaluating Sensitivity as a Function of Session Length

Detecting False Positives in Multielement Designs

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