Integrative Data Analysis and the Study of Global Health

Hussong, Andrea M.; Cole, Veronica T.; Curran, Patrick J.; Bauer, Daniel J.; Gottfredson, Nisha C.

doi:10.1007/978-3-030-35260-8_5

Cited by 6 publications

(4 citation statements)

References 56 publications

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“…Studies that use non‐self‐report data, particularly existing public record data (e.g., hospital, school, and arrest records), can be leveraged through methods championed in public health (e.g., linkage studies). Importantly, approaches to data pooling and integrating datasets that create larger samples with greater diversity may play a role in expanding the global lens of inquiry (Hussong, Cole, Curran, Bauer, & Gottfredson, 2020 ). For example, integrative data analysis and other data harmonization techniques as well as coordinated data collection efforts around key constructs may propel our individual scientific findings into a network of global knowledge (e.g., Hussong, Curran, & Bauer, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Adolescence Amid a Pandemic: Short‐ and Long‐Term Implications

Hussong

Benner

Erdem

et al. 2021

J of Research on Adolesc

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Members of the Society for Research on Adolescents COVID‐19 Response Team offer this commentary to accompany this special issue of the Journal of Research on Adolescence regarding the impact of the pandemic on adolescents’ social, emotional, and academic functioning. In addition to outlining the critical need for scholarly collaboration to address the global impact of this crisis on adolescent development, we argue that a broad investigative lens is needed to guide research and recovery efforts targeting youth development. We then use this broad lens to consider dimensions of the pandemic impact relative to developmental implications within community and policy contexts, educational contexts, social contexts, and family contexts. Finally, we describe guideposts for setting a global, shared research agenda that can hasten research to recovery efforts surrounding the pandemic and youth development.

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Section: Introductionmentioning

confidence: 99%

Adolescence Amid a Pandemic: Short‐ and Long‐Term Implications

Hussong

Benner

Erdem

et al. 2021

J of Research on Adolesc

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“…For the current analysis, these 25 studies represent a subset of 42 studies in Project Harmony that included item-level data from a semistructured interview measure. Other studies that were part of a larger combined IDA (Curran et al, 2008(Curran et al, , 2020 and individualpatient meta-analyses were excluded from this analysis if they (a) only included self-report PTSD measures (e.g., PCL, PTSD Symptom Scale-Self-Report Version [PSS-SR], Impact of Event Scale), (b) only submitted total scores for PTSD outcome measures, (c) were only allowed to submit aggregated summary data (i.e., means, standard deviations, correlations) by their IRBs based on the original consent form, or (d) had not submitted their data for inclusion into Project Harmony in time to be harmonized as part of this analysis. Table 1 shows the studies that were included in the current analysis along with information on sample sizes, population type, semistructured interview type, and the within-study percentages regarding gender and full PTSD diagnosis.…”

Section: Participants and Proceduresmentioning

confidence: 99%

“…Many single-dataset studies do not have a sufficient sample size nor sufficient variability across populations (e.g., demographic characteristics, comorbidities) to answer these questions. However, such studies can now be conducted within the integrative data analysis (IDA) framework, where data from multiple studies can be harmonized and combined into a single dataset, with specific considerations for MNI/DIF when the measures themselves may vary across studies (Hussong et al, 2020) in addition to MNI/DIF across populations.…”

mentioning

confidence: 99%

“…The systematic examination of MNI/DIF and its impact in estimating underlying PTSD severity across multiple predictors would simultaneously help to answer the question, “Which predictors contribute to measurement bias in estimating PTSD severity to a level where it would change estimates of “true” group differences?” Many single‐dataset studies do not have a sufficient sample size nor sufficient variability across populations (e.g., demographic characteristics, comorbidities) to answer these questions. However, such studies can now be conducted within the integrative data analysis (IDA) framework, where data from multiple studies can be harmonized and combined into a single dataset, with specific considerations for MNI/DIF when the measures themselves may vary across studies (Hussong et al., 2020) in addition to MNI/DIF across populations.…”

mentioning

confidence: 99%

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Estimating posttraumatic stress disorder severity in the presence of differential item functioning across populations, comorbidities, and interview measures: Introduction to Project Harmony

Morgan‐López

Hien

Saraiya

et al. 2022

Journal of Traumatic Stress

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Multiple factor analytic and item response theory studies have shown that items/symptoms vary in their relative clinical weights in structured interview measures for posttraumatic stress disorder (PTSD). Despite these findings, the use of total scores, which treat symptoms as though they are equally weighted, predominates in practice, with the consequence of undermining the precision of clinical decision‐making. We conducted an integrative data analysis (IDA) study to harmonize PTSD structured interview data (i.e., recoding of items to a common symptom metric) from 25 studies (total N = 2,568). We aimed to identify (a) measurement noninvariance/differential item functioning (MNI/DIF) across multiple populations, psychiatric comorbidities, and interview measures simultaneously and (b) differences in inferences regarding underlying PTSD severity between scale scores estimated using moderated nonlinear factor analysis (MNLFA) and a total score analog model (TSA). Several predictors of MNI/DIF impacted effect size differences in underlying severity across scale scoring methods. Notably, we observed MNI/DIF substantial enough to bias inferences on underlying PTSD severity for two groups: African Americans and incarcerated women. The findings highlight two issues raised elsewhere in the PTSD psychometrics literature: (a) bias in characterizing underlying PTSD severity and individual‐level treatment outcomes when the psychometric model underlying total scores fails to fit the data and (b) higher latent severity scores, on average, when using DSM‐5 (net of MNI/DIF) criteria, by which multiple factors (e.g., Criterion A discordance across DSM editions, changes to the number/type of symptom clusters, changes to the symptoms themselves) may have impacted severity scoring for some patients.

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