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Which insights can research on achievement motivation gain from network analysis? Comparing different network methods empirically

This article is a preprint and has not been certified by peer review [What does this mean?].

Author(s) / Creator(s)

Jähne, Miriam F.
Naumann, Alexander
Moeller, Julia
Baars, Jessica
Dietrich, Julia

Abstract / Description

Research on learning and achievement motivation in educational settings often relies on the analysis of multivariate covariance with structural equation models. Recently, network analyses have been proposed as alternative or additional analytical methods with the potential to provide novel insights to motivation research. However, there are many different versions of network analyses, many different kinds of data and calculations that can be fed into and illustrated by a network, and it remains so far unclear which kind of network will provide which kind of insight to the psychology of motivation. In this article we, first, give an overview of different versions of network analyses and the kinds of research questions they potentially address. Second, we empirically compare four network analysis methods by analyzing one dataset with these four approaches to compare which kinds of insights one method provides and the other possibly overlooks. We used self-report data of expectancy-value appraisals collected from N = 309 first-year university students. We chose six task value facets of intrinsic, attainment, and utility value (one facet each), and cost value (three facets) along with expectancy to represent aspects of achievement motivation specified in Eccles and Wigfield’s expectancy-value theory. These were analyzed as nodes in four different network analyses: (1) using zero-order correlations as edges, (2) using partial correlations as edges, (3) using intra-individual co-endorsements, and (4) co-rejections among the facets. We demonstrate that conclusions about associations can differ between different network approaches and that the specific research question should determine which network to choose.

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Date of first publication

2024-04-05

Publisher

PsychArchives

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    Description: Preprint of the manuscript: Which insights can research on achievement motivation gain from network analysis? Comparing different network methods empirically
  • Author(s) / Creator(s)
    Jähne, Miriam F.
  • Author(s) / Creator(s)
    Naumann, Alexander
  • Author(s) / Creator(s)
    Moeller, Julia
  • Author(s) / Creator(s)
    Baars, Jessica
  • Author(s) / Creator(s)
    Dietrich, Julia
  • PsychArchives acquisition timestamp
    2024-04-05T10:04:32Z
  • Made available on
    2024-04-05T10:04:32Z
  • Date of first publication
    2024-04-05
  • Abstract / Description
    Research on learning and achievement motivation in educational settings often relies on the analysis of multivariate covariance with structural equation models. Recently, network analyses have been proposed as alternative or additional analytical methods with the potential to provide novel insights to motivation research. However, there are many different versions of network analyses, many different kinds of data and calculations that can be fed into and illustrated by a network, and it remains so far unclear which kind of network will provide which kind of insight to the psychology of motivation. In this article we, first, give an overview of different versions of network analyses and the kinds of research questions they potentially address. Second, we empirically compare four network analysis methods by analyzing one dataset with these four approaches to compare which kinds of insights one method provides and the other possibly overlooks. We used self-report data of expectancy-value appraisals collected from N = 309 first-year university students. We chose six task value facets of intrinsic, attainment, and utility value (one facet each), and cost value (three facets) along with expectancy to represent aspects of achievement motivation specified in Eccles and Wigfield’s expectancy-value theory. These were analyzed as nodes in four different network analyses: (1) using zero-order correlations as edges, (2) using partial correlations as edges, (3) using intra-individual co-endorsements, and (4) co-rejections among the facets. We demonstrate that conclusions about associations can differ between different network approaches and that the specific research question should determine which network to choose.
    en
  • Publication status
    other
  • Review status
    notReviewed
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/9843
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.14387
  • Language of content
    eng
  • Publisher
    PsychArchives
  • Is related to
    https://www.psycharchives.org/handle/20.500.12034/9905
  • Dewey Decimal Classification number(s)
    150
  • Title
    Which insights can research on achievement motivation gain from network analysis? Comparing different network methods empirically
    en
  • DRO type
    preprint