ArticlePrevention science : the official journal of the Society for Prevention Research2023
Harmonizing Depression Measures Across Studies: a Tutorial for Data Harmonization.
Article in Prevention science : the official journal of the Society for Prevention Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Assessing the Potential Cumulative Effects of Longitudinal Interventions for Depression and Deviant Peer Association on Academic Achievement: a Causal Inference Approach.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Harmonising Depression and Anxiety Measures Across International Cohorts: A Framework Validated Using Measurement Invariance.International journal of methods in psychiatric research · 2026Article
- Bidirectional data harmonization across the multiple chronic disease disparities research consortium: A pipeline description of opportunities and challenges.Journal of biomedical informatics · 2026Article
- Identifying psychological distress data available in nationally representative surveys: A scoping review and case study of Australian surveys.Social psychiatry and psychiatric epidemiology · 2026Review
- Task-aligned outcome learning in psychiatry: reducing endpoint dilution.Frontiers in psychiatry · 2026Article
- ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasets.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- Retrospective Psychometrics and Effect Heterogeneity in Integrated Data Analysis: Commentary on the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Advancing Intervention and Prevention Research for Behavioral Health Problems Through Data Synthesis.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Mental health data available in representative surveys conducted in Latin America and the Caribbean countries: a scoping review.BMJ open · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
There has been increasing interest in applying integrative data analysis (IDA) to analyze data across multiple studies to increase sample size and statistical power. Measures of a construct are frequently not consistent across studies. This article provides a tutorial on the complex decisions that occur when conducting harmonization of measures for an IDA, including item selection, response coding, and modeling decisions. We analyzed caregivers' self-reported data from the ADHD Teen Integrative Data Analysis Longitudinal (ADHD TIDAL) dataset; data from 621 of 854 caregivers were available. We used moderated nonlinear factor analysis (MNLFA) to harmonize items reflecting depressive symptoms. Items were drawn from the Symptom Checklist 90-Revised, the Patient Health Questionnaire-9, and the World Health Organization Quality of Life questionnaire. Conducting IDA often requires more programming skills (e.g., Mplus), statistical knowledge (e.g., IRT framework), and complex decision-making processes than single-study analyses and meta-analyses. Through this paper, we described how we evaluated item characteristics, determined differences across studies, and created a single harmonized factor score that can be used to analyze data across all four studies. We also presented our questions, challenges, and decision-making processes; for example, we explained the thought process and course of actions when models did not converge. This tutorial provides a resource to support prevention scientists to generate harmonized variables accounting for sample and study differences.
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Registered trials
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