Quantitative structure
fits
As a precursor to estimating statistical models, item response distributions were assessed for both item sparseness and floor or ceiling effects, analyses which function as leading indicators of potentially problematic issues that can be further evaluated using more specific investigations of item functioning in the downstream item-response theory (IRT) modeling [The rate of completion for the PKDD and PKDIA measures at each assessment was reported for pre- and post-treatment periods to characterize the observed missing data and completion patterns. The overall response patterns were evaluated initially to determine any unusual answering that may indicate a problematic response pattern (such as levels of non-completion, consistent misinterpretation of the item question, the frequency of maximum/minimum item scoring, or large within-assessment variability). The frequency and percentage of each response option for each item on the PKDD and PKDIA measures were assessed in the sample across administrations (i.e., approximately daily [PKDD] or weekly [PKDIA] for the duration of participant enrolment) were evaluated. Spearman correlations were used to detect excessively strong or weak inter-item relationships to inform the presence of unstable inter-item relationships that could affect the model-fitting process. Items consistently showing |r|< 0.15 or |r|> 0.85 were used to determine problematic items and were flagged for further investigation [IRT modelling, specifically the heterogeneous graded response model (GRM) [IRT model fits for the PKDD and PKDIA were used to inform a weighted scoring algorithm which translated model-based sum scores to a T-score metric using expected a priori (EAP) scoring methods within the “mirt” version 1.33.2 packages’ "fscores" function [