Modeling Pathways of Character Development across the First Three Decades of Life: An Application of Integrative Data Analysis Techniques to Understanding the Development of Hopeful Future Expectations
There were two purposes of the present research: first, to add to scholarship about a key character virtue, hopeful future expectations; and second, to demonstrate a recent innovation in longitudinal methodology that may be especially useful in enhancing the understanding of the developmental course...
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Veröffentlicht in: | Journal of youth and adolescence 2017-06, Vol.46 (6), p.1216-1237 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | There were two purposes of the present research: first, to add to scholarship about a key character virtue, hopeful future expectations; and second, to demonstrate a recent innovation in longitudinal methodology that may be especially useful in enhancing the understanding of the developmental course of hopeful future expectations and other character virtues that have been the focus of recent scholarship in youth development. Burgeoning interest in character development has led to a proliferation of short-term, longitudinal studies on character. These data sets are sometimes limited in their ability to model character development trajectories due to low power or relatively brief time spans assessed. However, the
integrative data analysis
approach allows researchers to pool raw data across studies in order to fit one model to an aggregated data set. The purpose of this article is to demonstrate the promises and challenges of this new tool for modeling character development. We used data from four studies evaluating youth character strengths in different settings to fit latent growth curve models of
hopeful future expectations
from participants aged 7 through 26 years. We describe the analytic strategy for pooling the data and modeling the growth curves. Implications for future research are discussed in regard to the advantages of integrative data analysis. Finally, we discuss issues researchers should consider when applying these techniques in their own work. |
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ISSN: | 0047-2891 1573-6601 |
DOI: | 10.1007/s10964-017-0660-1 |