Harmonizing Depression Measures across Studies: A Tutorial for Data Harmonization

Autor/inn/en	Zhao, Xin; Coxe, Stefany; Sibley, Margaret H.; Zulauf-McCurdy, Courtney; Pettit, Jeremy W.
Titel	Harmonizing Depression Measures across Studies: A Tutorial for Data Harmonization
Quelle	In: Prevention Science, 24 (2023) 8, S.1569-1580 (12 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Zhao, Xin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1389-4986
DOI	10.1007/s11121-022-01381-5
Schlagwörter	Data Analysis; Sample Size; Decision Making; Test Items; Item Analysis; Coding; Models; Factor Analysis; Depression (Psychology); Symptoms (Individual Disorders); Check Lists; Patients; Quality of Life; Factor Structure; Hopkins Symptom Checklist + Suchen Sie Ihr Suchwort? Auswertung; Decision-making; Entscheidungsfindung; Test content; Testaufgabe; Itemanalyse; Codierung; Programmierung; Analogiemodell; Faktorenanalyse; Psychiatrische Symptomatik; Checkliste; Patient; Lebensqualität; Faktorenstruktur
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. (As Provided).
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Update	2024/1/01