ArticlePrevention science : the official journal of the Society for Prevention Research2023
Informing Harmonization Decisions in Integrative Data Analysis: Exploring the Measurement Multiverse.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Beyond Jacobson and Truax: Estimation of Clinical Significance Trajectories in the Coping Power Intervention Using Measurement Error-Corrected Multilevel Modeling.Behavior therapy · 2025Trial
- Statistical methods for retrospective harmonization of longitudinal epidemiological data: a scoping review.European journal of epidemiology · 2026Article
- A Taxonomy of Data Synthesis.Personality & social psychology bulletin · 2026Article
- Bidirectional data harmonization across the multiple chronic disease disparities research consortium: A pipeline description of opportunities and challenges.Journal of biomedical informatics · 2026Article
- Differences in substance use initiation patterns among biracial and monoracial adolescents: an integrative data analysis of two nationally representative samples.Addictive behaviors · 2026Article
- How changes in post-traumatic stress disorder (PTSD) severity mediate substance use disorder (SUD) severity during and after treatment for co-occurring PTSD and SUD: Results from Project Harmony.Addiction (Abingdon, England) · 2025Observational
- Preventing Suicidal Thoughts and Behaviors Among Youth: Integrative Data Analysis of Crossover Impacts of the Coping Power Preventive Intervention.JAACAP open · 2025Article
- An individual-level data synthesis of Clinical Trials Network studies to examine the impact of psychosocial treatments for Black people who use cocaine and/or opioids.Drug and alcohol dependence · 2025Article
- Assessing Whether Negative Parenting Cognitions Bias Parent Report of Preschoolers' Externalizing Symptoms: A Regularized Moderated Non-Linear Factor Analysis Approach.Research on child and adolescent psychopathology · 2025Article
- Positioning adolescence in the developmental timeline.Journal of research on adolescence : the official journal of the Society for Research on Adolescence · 2024Article
- The Impact of Sparse Datasets When Harmonizing Data from Studies with Different Measures of the Same Construct.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- 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
- Integrative data analysis of clinical trials network studies to examine the impact of psychosocial treatments for black people who use cocaine: Study protocol.Contemporary clinical trials · 2023Article
- Measurement invariance of maternal depressive symptoms across the first 2 years since birth and across racial group, education, income, primiparity, and age.Psychological assessment · 2023Article
- Sleep Quality and the Edge-On Edge-Off Cycle of Caffeine and Alcohol Use.Substance use : research and treatmentArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychometric models to find and account for cross-study differences in measurement. Embedded in logical and analytic harmonization are many decisions, from deciding whether items can be combined prima facie to how best to find covariate effects on specific items. Researchers may not have specific hypotheses about these decisions, and each individual choice may seem arbitrary, but the cumulative effects of these decisions are unknown. In the current study, we conducted an IDA of the relationship between alcohol use and delinquency using three datasets (total N = 2245). For analytic harmonization, we used moderated nonlinear factor analysis (MNLFA) to generate factor scores for delinquency. We conducted both logical and analytic harmonization 72 times, each time making a different set of decisions. We assessed the cumulative influence of these decisions on MNLFA parameter estimates, factor scores, and estimates of the relationship between delinquency and alcohol use. There were differences across paths in MNLFA parameter estimates, but fewer differences in estimates of factor scores and regression parameters linking delinquency to alcohol use. These results suggest that factor scores may be relatively robust to subtly different decisions in data harmonization, and measurement model parameters are less so.
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Registered trials
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