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Data aggregation in mental health is complicated by using different questionnaires, and little is known about the impact of item harmonization strategies on measurement precision. Therefore, we aimed to assess the impact of various item harmonization strategies for a target and proxy questionnaire using correlated and bifactor models. Data were obtained from the Brazilian High-Risk Study for Mental Conditions (BHRCS) and the Healthy Brain Network (HBN; N = 6,140, ages 5–22 years, 39.6% females). We tested six item-wise harmonization strategies and compared them based on several indices. The one-by-one (1:1) expert-based semantic item harmonization presented the best strategy as it was the only that resulted in scalar-invariant models for both samples and factor models. The between-questionnaires factor correlation, reliability, and factor score difference in using a proxy instead of a target measure improved little when all other harmonization strategies were compared with a completely at-random strategy. However, for bifactor models, between-questionnaire specific factor correlation increased from 0.05–0.19 (random item harmonization) to 0.43–0.60 (expert-based 1:1 semantic harmonization) in BHRCS and HBN samples, respectively. Therefore, item harmonization strategies are relevant for specific factors from bifactor models and had little impact on p -factors and first-order correlated factors when the child behavior checklist (CBCL) and strengths and difficulties questionnaire (SDQ) were harmonized. [ABSTRACT FROM AUTHOR]

Objectives: Model configuration is important for mental health data harmonization. We provide a method to investigate the performance of different bifactor model configurations to harmonize different instruments. Methods: We used data from six samples from the Reproducible Brain Charts initiative (N = 8,606, ages 5–22 years, 41.0% females). We harmonized items from two psychopathology instruments, Child Behavior Checklist (CBCL) and GOASSESS, based on semantic content. We estimated bifactor models using confirmatory factor analysis, and calculated their model fit, factor reliability, between‐instrument invariance, and authenticity (i.e., the correlation and factor score difference between the harmonized and original models). Results: Five out of 12 model configurations presented acceptable fit and were instrument‐invariant. Correlations between the harmonized factor scores and the original full‐item models were high for the p‐factor (>0.89) and small to moderate (0.12–0.81) for the specific factors. 6.3%–50.9% of participants presented factor score differences between harmonized and original models higher than 0.5 z‐score. Conclusions: The CBCL‐GOASSESS harmonization indicates that few models provide reliable specific factors and are instrument‐invariant. Moreover, authenticity was high for the p‐factor and moderate for specific factors. Future studies can use this framework to examine the impact of harmonizing instruments in psychiatric research. [ABSTRACT FROM AUTHOR]