Clinical Practice
Measurement, instruction, supervision and documentation for BCBAs and RBTs.
RBT Competency Assessment and Ongoing Supervision: What Practices Have to Get Right
Forty hours of training, an initial competency assessment, a background check and an exam get someone certified. Keeping them certified is a monthly supervision requirement that most practices track badly and discover late.
BCBA Supervised Fieldwork: How the Hours Actually Work
Supervised fieldwork versus concentrated supervised fieldwork, restricted versus unrestricted activities, monthly minimums and caps, and the contact and observation requirements that trip up trainees who were otherwise on track.
How to Write ABA Session Notes That Survive an Audit
SOAP, DAP, or narrative: the format matters less than whether the note establishes medical necessity, matches the billed code and units, and would let a clinician who was not there run the next session.
Generalization and Maintenance: Programming for the Skill to Survive Discharge
Stokes and Baer named the default approach train and hope in 1977, and it is still the default. Here are the strategies that actually produce responding across settings, people and time — and how to measure whether they worked.
Task Analysis and Chaining: Teaching Skills That Have More Than One Step
Handwashing has eleven steps. Which one you teach first is not arbitrary: backward chaining puts the learner at the finish line immediately, forward chaining builds in order, and total task presentation runs the whole sequence every time.
DTT vs NET: Two Teaching Formats, One Set of Principles
DTT is efficient at building new discriminations. NET is better at producing responses that occur outside the teaching table. Most good programs use both, and the interesting question is how they hand off to each other.
Token Economy Systems in ABA: Design, Exchange Schedules and the Ways They Fail
Tokens are generalized conditioned reinforcers, which means they only work if the backup reinforcers are worth something and the exchange rate is credible. Here is how to design one that survives contact with a real caseload.
Prompt Hierarchies and Prompt Fading: A Working Guide for ABA Teams
Response prompts, stimulus prompts, most-to-least, least-to-most, graduated guidance and time delay — what each one is for, and how to fade so the learner ends up independent rather than dependent.
Errorless Learning in ABA: When Preventing Mistakes Beats Correcting Them
Trial-and-error teaching lets a learner practice the wrong response before you correct it. Errorless learning prompts first and fades the prompt, which is faster for some learners and some targets — and unnecessary for others.
Frequency, Rate, Duration, Latency and IRT: Choosing the Continuous Measure That Answers Your Question
Counting is not always the right answer. If the clinical problem is that a learner takes four minutes to start a task, frequency data will never show it — and no amount of graphing will rescue the wrong measure.
How to Calculate IOA in ABA: Every Method, and When Each One Is Honest
Total count IOA is the easiest to calculate and the easiest to inflate. Here is how each IOA method works, which one belongs with which measurement system, and why the number you report can be technically correct and still misleading.
Momentary Time Sampling: The Most Practical Measurement System in ABA
Momentary time sampling asks one question at the end of each interval: is the behavior happening right now? That single constraint makes it less biased than partial or whole interval recording and dramatically easier to run.
Partial Interval vs Whole Interval Recording: Why Your Choice Changes the Answer
Partial interval recording systematically overestimates behavior. Whole interval systematically underestimates it. Pick the wrong one and you can show a reduction program working when it is not, or a skill acquisition program failing when it is not.
Continuous vs Discontinuous Measurement in ABA: Which One Your Data Actually Needs
Continuous measurement records every occurrence of a behavior. Discontinuous measurement samples it. Knowing when each is defensible — and which direction each one biases your data — is the difference between a graph you can trust and one that just looks tidy.
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