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McDaniel HL, Saavedra LM, Morgan-López AA, Bradshaw CP, Lochman JE, Kaihoi CA, Powell NP, Qu L, Yaros AC. Harmonizing Social, Emotional, and Behavioral Constructs in Prevention Science: Digging into the Weeds of Aligning Disparate Measures. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2023; 24:1581-1594. [PMID: 36753042 DOI: 10.1007/s11121-022-01467-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2022] [Indexed: 02/09/2023]
Abstract
While integrative data analysis (IDA) presents great opportunity, it also necessitates a myriad of methodological decisions related to harmonizing disparate measures collected across multiple studies. There is a lack of step-by-step methodological guidance for harmonizing disparate measures of latent constructs differently conceptualized or operationalized across studies, such as social, emotional, and behavioral constructs often utilized in prevention science. The current paper addressed this gap by providing methodological guidance and a case illustration focused on harmonizing measures of disparately conceptualized and operationalized constructs. We do so by outlining a five-phased harmonization approach paired with an illustrative example of the approach as applied to harmonization of broadband latent emotional and behavioral health constructs assessed with different measures across studies. This approach builds on and expands upon procedures currently recommended in the IDA literature with parallels to best practices in test development procedures. The illustrative example of our phased approach is drawn from an IDA study of 11 randomized controlled trials of Coping Power (Lochman & Wells, 2004), an evidence-based preventive intervention. We demonstrate the harmonization of two constructs, internalizing and externalizing problems, as harmonized across the teacher-reported scales of the Achenbach System of Empirically Based Assessment (Achenbach, 1991a) and the Behavior Assessment System for Children (Reynolds & Kamphaus, 2004). Finally, we consider the potential strengths and limitations of this phased approach, underscoring areas for future methodological research and conclude with some recommendations.
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Affiliation(s)
- Heather L McDaniel
- School of Education and Human Development, University of Virginia, Charlottesville, VA, USA.
| | - Lissette M Saavedra
- Community Health Research Division, RTI International, Research Triangle Park, NC, USA
| | | | - Catherine P Bradshaw
- School of Education and Human Development, University of Virginia, Charlottesville, VA, USA
| | - John E Lochman
- Center for Youth Development and Intervention, University of Alabama, Tuscaloosa, AL, USA
| | - Chelsea A Kaihoi
- School of Education and Human Development, University of Virginia, Charlottesville, VA, USA
| | - Nicole P Powell
- Center for Youth Development and Intervention, University of Alabama, Tuscaloosa, AL, USA
| | - Lixin Qu
- Center for Youth Development and Intervention, University of Alabama, Tuscaloosa, AL, USA
| | - Anna C Yaros
- Community Health Research Division, RTI International, Research Triangle Park, NC, USA
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Lipnicki DM, Lam BCP, Mewton L, Crawford JD, Sachdev PS. Harmonizing Ethno-Regionally Diverse Datasets to Advance the Global Epidemiology of Dementia. Clin Geriatr Med 2023; 39:177-190. [PMID: 36404030 PMCID: PMC9767705 DOI: 10.1016/j.cger.2022.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Understanding dementia and cognitive impairment is a global effort needing data from multiple sources across diverse ethno-regional groups. Methodological heterogeneity means that these data often require harmonization to make them comparable before analysis. We discuss the benefits and challenges of harmonization, both retrospective and prospective, broadly and with a focus on data types that require particular sorts of approaches, including neuropsychological test scores and neuroimaging data. Throughout our discussion, we illustrate general principles and give examples of specific approaches in the context of contemporary research in dementia and cognitive impairment from around the world.
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Affiliation(s)
- Darren M Lipnicki
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia.
| | - Ben C P Lam
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia
| | - Louise Mewton
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia
| | - John D Crawford
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, University of New South Wales, Level 1, AGSM (G27), Gate 11, Botany Street, Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, Australia
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