Title: IMPACTS OF FACTOR ANALYTIC CHOICES ON SCALE INTERPRETATION COMMONLY USED TO ASSESS CHILDREN WITH AND WITHOUT AUTISM SPECTRUM DISORDER

Abstract: Introduction: Autism Spectrum Disorder (ASD) measures are often designed as diagnostic tools and used as outcome variables in research. This dual usage poses validation challenges since these tools are typically assessed only for diagnostic accuracy, not as outcome measures. This issue is particularly pronounced for ordinal scales, where their utility as outcome variables is more complex than their validation for clinical diagnostics.

Objective: This study examines how various factor analytic decisions influence the quality of scales. It uses the Childhood Symptom Behavior Questionnaire (CSBQ) as a model, focusing on its development as an outcome variable.

Methods: Using responses from approximately 7,000 children in the CDC's Pathways dataset, this study compares four factor analytic methods—Maximum Likelihood (ML), Categorical ML (cML), Diagonally Weighted Least Squares (DWLS), and Unweighted Least Squares (ULS). These methods are evaluated using fit indices in their regular, scale-shifted, and robust forms to determine their impact on model fit. Results: Analysis revealed significant model variations fit across methods, with adjustments in item-pair residuals generally enhancing fit. Scale-shifted and robust conditions notably affected model stability, highlighting the importance of selecting appropriate analytic techniques for ordinal data.

Conclusion: The choice of factor analytic method significantly influences scale interpretation accuracy in ASD assessments. This study advocates using methods that accommodate the ordinal nature of data, enhancing the utility of ASD scales in clinical and public health settings.

Chair: Dr. Brian Barger