Diverse pathways to positive and negative affect in adulthood and later life

An integrative approach using recursive partitioning

Bibliographic Data

ID9790367
AuthorsTara L Gruenewald (0000-0002-3783-9171, University of California, Los Angeles, corresponding author), Daniel K Mroczek (0000-0003-3748-5059, Purdue University West Lafayette), Carol D Ryff (0000-0002-4693-9190, University of Wisconsin–Madison), Burton Herbert Singer (0000-0002-5295-5971, Institute on Aging)
Year2008
Volume44
Issue2
Pages330-343
Publication date2008-03-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueDevelopmental Psychology (JOURNAL)
Journal identifiersISSN: 0012-1649 • E-ISSN: 1939-0599
PublisherAmerican Psychological Association (APA) (PUBLISHER)
DOI10.1037/0012-1649.44.2.330
PMID18331126
OpenAlexW2163227110
LanguageEN
Citations received12
References cited27

Recursive partitioning is an analytic technique that is useful for identifying complex combinations of conditions that predict particular outcomes as well as for delineating multiple subgroup differences in how such factors work together. As such, the methodology is well suited to multidisciplinary, life course inquiry in which the goal is to integrate many interacting influences and understand subgroup variation. The authors conducted recursive partitioning analyses on a previously published study (D. K. Mroczek & C. M. Kolarz, 1998) that investigated life course profiles of positive and negative affect and incorporated various top-down (personality traits) and bottom-up (sociodemographic statuses, contextual influences) influences. The new analyses reveal multiway, nonlinear interactions among these variables in predicting affective experience and, importantly, life course differences in how these various factors combine. Included are details of how recursive partitioning trees are generated as well as descriptions of the software packages available for using such techniques. Overall, the methodology offers tractable strategies for discerning meaningful patterns in highly complex data sets

Affect (linguistics · Big Five personality traits · Cognitive psychology · Developmental psychology · Machine learning · Multidisciplinary approach · Personality · Recursive partitioning · Variation (astronomy · Communication · Computer Science · Health disparities and outcomes · Mental Health Research Topics · Psychological Well-being and Life Satisfaction · Psychology · Social Psychology

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