Sec 4 Functional Assessment Implementation

Plans in Action

  • Jonas, Marion, and Marshawn are collecting data to develop effective intervention plans for individuals they serve.

  • The focus is on the data collection process for functional assessments involving their respective clients.

Data Collection Process

Jonas and Tim

  • Context: Jonas collected data for Tim, who has a history of intense behaviors, including self-injury.

  • Assessment Plan: This includes archival records review, interviews, observations, and functional analysis.

Archival Records:

  • History of self-injurious behaviors: face slapping and biting palms.

  • Previous interventions: medication, ignoring behaviors, redirection—but no significant reduction in behaviors was noted.

Communication Methods:

  • Tim does not communicate verbally; he uses pointing or noises as a communication method.

Family Interview Insights:

  • Behavior History: Behaviors started around 18 months old, worsened when he is asked to do undesirable tasks.

  • Response to Behavior: Family members comfort him with reassurances and by reducing noise.

  • Behavior Intensity: Injuries include persistent bites on hands and facial slapping leading to visible redness.

  • Behavior-Free Activities: Engaged in enjoyable activities like swimming or watching TV without showing self-injury.

Observational Data:

  • ABC Recording:

    • Antecedents include being told to perform tasks (e.g., bathroom).

    • Behavior exhibited involved face slapping and hand biting.

    • Consequence involved being told to stop and gestural prompts.

  • Partial Interval Recording: Focused on self-injury incidence.

  • Functional Analysis: Data indicates specific behaviors linked to context (e.g., demand conditions significantly increasing behaviors).

Marion and Cody

  • Context: Marion collected data on Cody, focusing on his wandering behavior.

  • Assessment Goal: Identify attempts to leave designated areas without prompts due to safety concerns.

Teacher Interview Insights:

  • Frequency of Wandering: Cody approaches the door 3-4 times daily, typically intercepted by teachers.

  • Behavior Patterns: Leaves seat during various activities; higher engagement with the door when at centers.

  • Safety Concerns: Attempts to escape outdoor and other environments were noted.

Parent Interview Insights:

  • Historical Escapes: Multiple instances of Cody leaving the house with minimal recent actual escapes.

  • Safety Issues: Engagement in risky activities like climbing and jumping, indicating a need for careful supervision.

Observational Data:

  • Frequency Recording: Tracking how often Cody leaves his seat without permission.

  • Latency Data: Measures the time taken for Cody to comply after receiving requests.

Marshawn and Alonzo

  • Context: Marshawn focused on Alonzo's school behavior through data collection and interviews.

  • Behavioral Background: Alonzo has a history of disruptive behaviors leading to suspensions.

School Records Insights:

  • Behavioral Issues: Incidents of disrespectful behavior and disruptive tendencies, especially during independent tasks or interactions with specific peers.

Teacher Interview Insights:

  • Troublesome Behaviors: Leaving desks, shouting obscenities—less frequent redirection needed during group activities.

  • Struggles with Tasks: Independent reading tasks trigger most behavioral issues.

Observational Data:

  • ABC Recording: Focused on antecedents prompting disruptive behaviors, with specific examples noted.

  • Frequency Recording: Data collected on the incidences of refusal, attempted departures, and use of inappropriate language.

Data Analysis Techniques

  • Different data collection methods provide insight into patterns and functions of behavior concerning individuals' needs.

  • Observer Agreement: Ensures accuracy of observational data; involves multiple observers recording behaviors concurrently.

Interobserver Agreement (IOA)

  • Calculated using a straightforward formula for reliability assessment: smaller count divided by larger count multiplied by 100 gives a reliability percentage.

  • Example Analysis: IOA calculations provide validation for collected data involving behaviors, such as talking out in class.

Graphical Data Representation

  • Jonas’s Data: Shows peak incidents of self-injurious behavior at specific times, indicating variability based on external conditions.

  • Marion’s Data: Graphs show frequency of leaving seat and latency in compliance; demonstrates variability in his response to prompts.

  • Marshawn’s Data: Highlights peaks in refusal and inappropriate language in relation to specific triggers in demands.

Behavioral Function Hypotheses

  • Analysis of data leads to forming hypotheses about behavioral functions, primarily focusing on reinforcement or escape mechanisms.

  • Tim's Hypotheses: Self-injurious behaviors occur to gain attention or escape non-desired tasks, varied based on environmental demands.

  • Cody's Hypotheses: Leaving tasks primarily functions to escape demands.

  • Alonzo's Hypotheses: Disruptive behavior serves to escape challenging tasks or situations.

Conclusion

  • The ongoing analysis aims to ensure interventions are driven by accurate behavioral understandings based on a comprehensive review of multiple data types.