Data is an important aspect in the analysis and modification of behavior programs or any other forums involving analysis of traits and parameters. Data trends are a salient benchmark in the establishment of a modification and troubleshooting platform used to guide the success of a behavior program. Two and three-dimensional curves and trend lines plotted using groups of data provide an informative visual outlay of program effects on a target behavior. Further, data usage provides an accurate and statistically evidential method for justifying changes and modifications in the initial drafting of a program. Program effectiveness is considered a subset of outcomes in data trends with positive and improved trends laying foundation for a potentially effective program. Part of the data utilization process involves data collection which is used for providing feedback and surplus training on the newly implemented behavior support plans (BSPs) among teachers and assistants. Data collection also provides an organized and regularly availed chance for behavior assessors to make observations about staff interpretations of BSP content. Treatment integrity evaluations are also useful in making proper data-related decisions so that in the event of a failing treatment plan, analysts can determine whether the cause is related to a faulty BSP or an inconsistency in the implementation process.
Analyses conducted in all four program sections is quantitative in nature because they involve firsthand collection of data and information in form of questionnaires, direct observations, and physical counting and tallying methods. This type of data is relevant to the program evaluation process because it provides a firsthand account of trends and occurrences in the program. Data trends obtained from quantitative research are accurate depictions of the effectiveness of the program.
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Figure 1: Staff Feedback
Staff feedback through questionnaires is a crucial strategy towards assessing the performance and effectiveness of the BSP program at various stages. Feedback relating to a specific goal is important in creating performance goals so that if a staff member gives an honest opinion on the dynamics of the BSP program, this feedback is used in setting clear performance outlooks. Staff feedback also provides a decentralized method of troubleshooting the program for errors and omissions if any, and making the necessary upgrades. Program effectiveness extracts from not only the trends in target behavior but also from how the program favors or disfavors the immediate user and in this case the staff member. Performance is characterized by four definitive factors according to Duncan & Smoot (2013): precisely defining performance, precisely measuring it, clear feedback delivered on time, and clear roadmaps between pay and performance. The latter is especially important in annotating staff feedback regarding the program. Based on the responses recorded in the staff feedback section, the effectiveness of the program lies on the higher side with four out of the six responses indicating an impact facilitated by the program, regardless of whether the impact is negative or positive.
Figure 2: Frequency of Aggressive Behaviors
The frequency of aggressive behaviors before the start of the BSP program i.e. at baseline indicates a relatively high rate of occurrence with the curve indicating an increasing trend over time. The rate of behavior incidents in the three months prior to the program increases with time probably due to peer influence within the school premises complemented by a rapidly popularizing aggressive behavior trend among the students. After implementation of the program, the trend in aggressive behavior recorded a slight increase during the first month followed by a steep decline in the succeeding months. A gradient of almost 28 was recorded with implementation of the program and over the next six-month period after implementation, a range of -270 had been recorded. This value represents a decline from the highest value recorded within the program (290) to the lowest value (30). The variance for the given set of data is about 9225 which indicates a relatively large spread of values from the mean and between any two values. The intervention prompted by the BSP program caused a relatively high decrease in the frequency of occurrence of aggressive behavior incidents. This result is indicated by the large values of data range and variance which signify a significantly large difference in frequency.
Based on the data findings for this observation, it is ascertained that the program is highly effective. A reduction in the frequency of occurrence of aggressive behavior at a rate of approximately 28 cases per month and a decrease from a high of 290 cases per month to only 30 cases after a six-month period signifies program effectiveness. The final value recorded in the month of June was also the closest the program had gotten to completely eliminating aggression cases in the school. Linear forecasting performed on the data set indicates that based on the trend effected by the program, aggressive behavior cases would be completely eliminated by the month of August.
Figure 3: Frequency of Physical Aggression
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The percentage of IOA is given as (Maag, 2016):
The conventional value for interobserver agreement is at least 80% hence a value of 60% is below the acceptable lower limit of agreement. It is observed that as the number of disagreements go up, the percentage IOA decreases. In an effort to increase IOA percentage, it is important to consider the factors affecting the level of agreement. These factors are (Martella, Nelson, Morgan, & Marchand-Martella, 2013):
Reactivity – these are differences in IOA arising from the consciousness of the observers to being assessed. Reactivity causes higher IOA and better accuracy of a set of observations.
Observer drift – arises when observers change methods of behavior assessment because of shifts in their perception of the definition of behavior. It is not obvious that observer drift causes reduced IOA since drift can be positively realized when observers work together. However, drift affects overall data accuracy: it can thus be avoided through boosted training focusing on behavior definitions.
Measurement system complexity: this is caused by aspects such as the number of observed subjects, the number of behaviors, and observation durations. A higher system complexity results in decreased IOA and hence there is need to balance complexity with the intended IOA and data accuracy.
Expectations of the observer are also known to influence IOA outcomes so that an observer expecting special outcomes from a given intervention will be more inclined to observing that specific effect more closely. The issue of observer expectation escalates in observations involving researcher feedback.
IOA is a handy tool in determination of BSP program effectiveness because it is a quantitative tool that provides measures of researcher accuracy and effectiveness of behavior observations in accommodating a particular behavior intervention. Percentages and ratios are used to provide a comparative framework against conventional standards to establish what needs to be changed or improved within the program.
Figure 4: Treatment Integrity Measures
The factors are 40 at baseline, 90 after one day of training, and 60 at a three-month follow-up.
According to the factor ratings obtained, it is observed that the performance of the staff member improved significantly after a day of training in the BSP. The program is most effective at this stage because the staff is freshly equipped with BSP knowledge through training. A three-month follow up of the staff member indicates a deterioration in the performance outcome of the BSP hence a relatively lesser effectiveness of the program. This drop is perhaps due to cumulative laxity or due to exposure to constantly changing student subjects hence limiting the scope of expertise obtained during training.
A Behavior Support Plan (BSP) is characterized by various levels of training and in most cases there are two stages. At the second stage, more personalized training is awarded to individuals and this occurs just prior to the plan implementation phase. A treatment integrity analysis is conducted to monitor and rate the effectiveness and accuracy of the implemented BSP (Fienup, Baranek, Derderian, Knox, & Pace, 2013). Treatment integrity contributes significantly to the therapeutic aspect of BSP structures. A checklist is created as a platform for reflecting on individual student BSP outcomes, for instance in the assessment of target behaviors.
References
Duncan, P.K., and Smoot D.T. Pay for performance. In W.K. Redmon, T.C. Mawhinney, and C.M. Johnson (Eds.), Handbook of organizational performance: behavior analysis and management . New York, NY: Routledge.
Fienup, D.M., Baranek, A., Derderian, J., Knox M. & Pace, G.M. (2013). Components of a private school program serving children and adolescents with severe problem behavior. In D.D. Reed, F.D. DiGennaro Reed, and J.K. Luiselli (Eds.), Handbook of crisis intervention and developmental disabilities (pp. 351-380). New York, NY: Springer.0
Maag, J. W. (2016). Behavior management: From theoretical implications to practical applications . Boston, MA: Cengage Learning.
Martella, R. C., Nelson, J. R., Morgan, R. L., & Marchand-Martella, N. E. (2013). Understanding and interpreting educational research . New York, NY: Guilford Press.
