Results Within Baseline and Within Intervention To analyze the effectiveness of the video modeling interventions, all participant’s assessment data will be reported on a line graph as depicted in...

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Results Within Baseline and Within Intervention To analyze the effectiveness of the video modeling interventions, all participant’s assessment data will be reported on a line graph as depicted in figure 1. These graphs will be analyzed for absolute change in level within the baseline and intervention phase. To calculate the absolute change in level, the last data point will be subtracted from the first data point in the phase. Within the baseline, we expect participants to have a 5% change or less for accuracy to show stability within the baseline phase. Within the intervention phase, we expect to see a change of 5% or more to show a high level of change in the intervention phase. To determine the level stability within the baseline and intervention phase, we will develop stability envelopes based on 20% of the median of the phase. The majority of data points should fall within the stability envelope to show stability within each level. The graphs will also be analyzed for trends within the baseline and intervention phases. We will use the quarter-intersect method to develop a trend line for both phases. Within the baseline, we would expect zero-celeration to show stability in the baseline phase before moving a participant into the intervention phase. Within the intervention phase, we would like to see an accelerating trend to show progress in reading fluency during the intervention. To determine the stability of the trend within each phase, stability envelopes will be developed based on 20% of the trend. The majority of data points should fall within the stability envelope to show the stability of the trend. Between Baseline and Intervention The graphs will also be analyzed for an absolute change in level between the baseline and intervention phase for immediacy of effect. As previously mentioned, reading fluency does not rapidly increase without intervention (Jefferson et al., 2017; Montgomerie, et al., 2014). Therefore, we would not expect to see a large immediacy of effect between the baseline and intervention phase. To analyze the trend between the baseline and intervention phases, the quarter-intersect method will be used to develop a trend line across all sessions. We would expect to see an accelerating trend line as we want to see the students reading fluency scores increase due to the intervention. To determine the stability of the trend, a stability envelope will be developed based on 20% of the trend. The majority of data points should fall within the stability envelope to show the stability of the trend. The percentage of non-overlapping data points will be analyzed to determine how often the intervention data points are not in the baseline range. We would like to see a higher percentage of non-overlapping data points as the data points in the intervention should be higher than the data points in the baseline. A higher percentage would help us show that the intervention was effective in increasing students reading fluency scores. Included in this proposal are graphs that include hypothesized data found in figure 1. We would expect to see stable data in the baseline and accelerating data in the intervention phase. These hypothesized results would show that the video modeling intervention would be effective in increasing students reading fluency scores.