An Internal Consistency Reliability Study of the Catalyst Datafinch Applied Behavior Analysis Data Collection...

Area of Science:

• Psychometrics
• Behavioral Science
• Autism Research

Background:

• Applied behavior analysis (ABA) is a widely used therapeutic approach for individuals with autism spectrum disorder (ASD).
• Numerous psychometric studies have evaluated the reliability of various instruments used in ABA.
• The internal consistency reliability of the Catalyst Datafinch ABA Data Collection Application has not been previously investigated.

Purpose of the Study:

• To assess the internal consistency reliability of the Catalyst Datafinch ABA Data Collection Application.
• To determine if the application provides dependable measurements for behavioral data collection in individuals with autism.

Main Methods:

• Four datasets (n=62-103) were extracted from studies conducted at The Oxford Center between March 2023 and January 2024.
• Data were collected using the Catalyst Datafinch application by Board Certified Behavior Analysts (BCBAs) and behavioral technicians.
• Internal consistency reliability measures, including Cronbach's alpha, split-half, and interclass correlation coefficients (ICC), were computed using SPSS Statistics (v. 29.0).

Main Results:

• Cronbach's alpha values ranged from 0.916 to 0.980, indicating excellent reliability across datasets.
• Split-half reliability coefficients were consistently good to excellent (0.777–0.996).
• Average measures ICCs were excellent (0.916–0.980), while single measures ICCs ranged from acceptable to good (0.609–0.926).

Conclusions:

• The Catalyst Datafinch application demonstrates high internal consistency reliability for collecting behavioral data in individuals with autism.
• The application's psychometric properties suggest it is a reliable tool for practitioners.
• Findings support the use of Catalyst Datafinch for dependable behavioral data collection and analysis in autism research and practice.