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3 Mistakes You’re Making with Your Data Collection: A Guide for ABA and Autism Classrooms

Data collection is the backbone of effective Applied Behavior Analysis (ABA) and special education practices. However, even us seasoned professionals may unknowingly make mistakes that compromise the accuracy and reliability of their data. In this blog post, we’ll explore three common mistakes made in this area within ABA and autism classrooms. By identifying and addressing these pitfalls, we can improve the quality of our data and ultimately enhance educational outcomes for individuals with autism spectrum disorder (ASD).

1. Neglecting Consistent Data Collection Procedures

Consistency is key when it comes to data collection, yet one common mistake is the failure to establish and adhere to consistent measurement procedures. Without standardized procedures, data may vary based on individual interpretation, leading to inconsistencies and inaccuracies. Autism and ABA classroom staff must ensure that all team members are trained in the same data collection methods and protocols, including definitions of target behaviors, measurement tools, and recording procedures. Consistent measurement ensures reliable data that accurately reflects progress and informs intervention decisions.

Some ways to help ensure that data collection is consistent among staff include:

  • demonstrating teaching and data techniques in informal or formal staff training meetings.
  • asking support staff to role-play teaching and data procedures so that you can ensure mastery of these skills before they are working in instructional sessions with students.
  • using visual cues to model the data collection procedures that staff can refer to throughout sessions.

2. Not Individualizing Data Collection Methods per Student

One size does not fit all when it comes to data collection methods. Another common mistake is the failure to individualize data methods to meet the unique needs and preferences of each student. ABA and autism classrooms should recognize the importance of tailoring data collection methods to accommodate the diverse learning styles, communication abilities, and behavioral characteristics of their students with autism spectrum disorder (ASD).

Common elements of data collection that can be individualized are:

By individualizing data collection methods, we can ensure that we capture accurate and meaningful data that reflects each student’s progress and informs targeted intervention strategies effectively.

3. Failure to Analyze and Use Data Collection Effectively

Collecting data is only half the battle; the true value lies in analyzing the data in order to inform decision-making and drive meaningful outcomes. I personally find this to be the hardest pitfall to avoid! After all, we are pouring so much time into training staff, delivering effective instructional sessions with students, and navigating behavioral challenges as they arise. Carving out the time for structured data analysis can be difficult, but scheduling it as any other task (an IEP meeting or staff training) can help ensure that we are accountable for it getting done! Looking at the data that has been painstakingly collected over time can guide intervention planning, help us evaluate progress, and make data-driven adjustments to programming.

Examples of data-driven decisions when analyzing data can include:

  • adding new target skills if a learner has rapidly acquired several skills within one teaching program.
  • adjusting prompt levels (moving to more intrusive prompts if the data reflects little to no skill growth or moving to less intrusive prompts if the learner is flying through prompt hierarchies or target lists).
  • revising future IEP goals based on the rate of progress a learner is making (e.g., if they have acquired ten new numbers within this school year, I might include a target list of additional 10 numbers for their next IEP, rather than aim for 20 or more).


In conclusion, avoiding common mistakes in data collection is essential for any ABA or autism classroom striving to provide effective interventions and educational services for individuals with ASD. By prioritizing consistency in measurement procedures, individualizing data methods per student, and analyzing the data collected to drive decision-making, we can enhance the quality and utility of our data. I love to troubleshoot data collection methods or data sheets with fellow special education staff, so please email me any time with questions at


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