Harmonizing Depression Measures Across Studies: a Tutorial for Data Harmonization

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.

Keywords: Depression; Harmonized measure; Integrative data analysis (IDA); Item response theory (IRT).

Figure 1

Item Characteristic Curves Note. The X-axis for this figure is the depression theta variable and the Y-axis is probability of endorsement. The X values corresponding to probability=.5 on the ICC represents the difficulty for that item.

Figure 2

Factor Score Distributions for Depression by Study