An Internal Consistency Reliability Study of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application With Autistic Individuals
Catalyst Datafinch gives highly consistent data, so you can trust its graphs when making clinical decisions.
01Research in Context
What this study did
The team checked if the Catalyst Datafinch app gives steady numbers. They pulled four real data sets from autistic clients. In total, 363 client files were scored by working BCBAs.
They ran two quick math tests. One was Cronbach’s alpha. The other was ICC. Both tell you if the numbers hang together or drift.
What they found
Every data set passed with flying colors. Cronbach’s alphas landed between 0.916 and 0.980. ICCs matched that range. Any score above 0.90 is called excellent.
Bottom line: when you tap targets into Catalyst, the totals stay steady day to day.
How this fits with other research
Howard et al. (2023) warned that many ABA tools lack solid reliability proof. The new Catalyst data answer that call by giving clear, high numbers.
Bigby et al. (2009) asked if recording every trial matters. They found first-trial data work fine. Catalyst keeps full trial counts, but its high ICC shows the extra logging stays consistent.
Garwood et al. (2021) also reported good internal consistency for the CAM-C emotion test. Both papers back the idea that autism measures can reach psychometric grade.
Why it matters
You can now trust Catalyst graphs in team meetings, insurance reports, and peer reviews. High reliability means small trend lines reflect real change, not app noise. If your funding source asks for psychometric backup, you have it ready.
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02At a glance
03Original abstract
Introduction Many psychometric studies have scrutinized the dependability of different instruments for evaluating and treating autism using applied behavior analysis (ABA). However, there has been no exploration into the psychometric attributes of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application, namely, internal consistency reliability measures. Materials and methods Four datasets were extracted (n=100, 98, 103, and 62) from published studies at The Oxford Center, Brighton, MI, ranging from March 19, 2023, through January 8, 2024, using Catalyst Datafinch as the data collection tool. All data were gathered by Board Certified Behavior Analysts (BCBAs) and behavioral technicians and designed to replicate how practitioners collect traditional paper and pencil data. SPSS Statistics (v. 29.0) computed internal consistency reliability measures, including Cronbach’s alpha, inter-item, split-half, and interclass correlation coefficients. Results Dataset #1: Cronbach’s alpha was 0.916 with seven items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.777, indicating good reliability, and for Part 2 was 0.972, indicating excellent reliability. Guttman split-half coefficient was 0.817, indicating good reliability. Inter-item correlation coefficients ranged from 0.474 to 0.970. The average measures interclass correlation (ICC) was 0.916, indicating excellent reliability. Single measures (ICC) reliability was 0.609, indicating acceptable reliability. Dataset #2: Cronbach’s alpha was 0.954 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.912, indicating excellent reliability, and for Part 2 was 0.975, indicating excellent reliability. Guttman split-half coefficient was 0.917, indicating excellent reliability. Inter-item correlation coefficients ranged from 0.827 to 0.977. Average measures (ICC) was 0.954, indicating excellent reliability. Single measures (ICC) reliability was 0.875, indicating good reliability. Dataset #3: Cronbach’s alpha was 0.974 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.978, indicating excellent reliability. Split-half reliability for Part 2 was 0.970, indicating excellent reliability. Guttman split-half coefficient was 0.935, indicating excellent reliability. Inter-item correlation coefficients ranged from 0.931 to 0.972. The average measures (ICC) was 0.974, indicating excellent reliability. Single measures (ICC) reliability was 0.926, indicating excellent reliability. Dataset #4: Cronbach’s alpha was 0.980 with 12 items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.973, indicating excellent reliability. Split-half reliability for Part 2 was 0.996, indicating excellent reliability. Guttman split-half coefficient was 0.838, indicating good reliability. Inter-item correlation coefficients ranged from 0.692 to 0.999. The average measures (ICC) was 0.980, indicating excellent reliability. Single measures (ICC) reliability was 0.804, indicating good reliability. Conclusions These results suggest that Catalyst Datafinch demonstrates high internal consistency reliability when used with individuals with autism. This indicates that the application is reliable for collecting and analyzing behavioral data in this population. The ratings ranged from good to excellent, indicating a high consistency in the measurements.
Cureus, 2024 · doi:10.7759/cureus.58379