What applying growth mixture modeling can tell us about predictors of number line estimation
Author(s) / Creator(s)
DeVries, Jeffrey M.
Kuhn, Jörg-Tobias
Gebhardt, Markus
Abstract / Description
Number line estimation tasks have been considered a good indicator of mathematical competency for many years and are traditionally analyzed by fitting individual regression curves to individual responders. We innovate on this technique by applying growth mixture modeling and compare it to traditional regression using a sample of 2nd graders (n = 325) who completed both 0–20 and 0–100 number line tasks. We explore the effects of gender, special education needs, and migration background. Using growth mixture modeling, more children were identified as logarithmic responders than were identified using regressions. Growth mixture modeling was able to identify the significant effects of gender on class membership for both tasks, and of special education needs for the 0–20 task. Overall, growth mixture modeling provided a more complete picture of individual response patterns than traditional regression techniques. We discuss the implications of these findings and provide recommendations for future researchers to use growth mixture modeling with future number line task analyses.
Keyword(s)
number line estimation growth mixture modeling mixture modeling latent growth modeling special education needs migration backgroundPersistent Identifier
Date of first publication
2020-06-15
Journal title
Journal of Numerical Cognition
Volume
6
Issue
1
Page numbers
66–82
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
DeVries, J. M., Kuhn, J.-T., & Gebhardt, M. (2020). What applying growth mixture modeling can tell us about predictors of number line estimation. Journal of Numerical Cognition, 6(1), 66-82. https://doi.org/10.5964/jnc.v6i1.212
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Author(s) / Creator(s)DeVries, Jeffrey M.
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Author(s) / Creator(s)Kuhn, Jörg-Tobias
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Author(s) / Creator(s)Gebhardt, Markus
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PsychArchives acquisition timestamp2022-04-14T11:21:44Z
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Made available on2022-04-14T11:21:44Z
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Date of first publication2020-06-15
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Abstract / DescriptionNumber line estimation tasks have been considered a good indicator of mathematical competency for many years and are traditionally analyzed by fitting individual regression curves to individual responders. We innovate on this technique by applying growth mixture modeling and compare it to traditional regression using a sample of 2nd graders (n = 325) who completed both 0–20 and 0–100 number line tasks. We explore the effects of gender, special education needs, and migration background. Using growth mixture modeling, more children were identified as logarithmic responders than were identified using regressions. Growth mixture modeling was able to identify the significant effects of gender on class membership for both tasks, and of special education needs for the 0–20 task. Overall, growth mixture modeling provided a more complete picture of individual response patterns than traditional regression techniques. We discuss the implications of these findings and provide recommendations for future researchers to use growth mixture modeling with future number line task analyses.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationDeVries, J. M., Kuhn, J.-T., & Gebhardt, M. (2020). What applying growth mixture modeling can tell us about predictors of number line estimation. Journal of Numerical Cognition, 6(1), 66-82. https://doi.org/10.5964/jnc.v6i1.212en_US
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ISSN2363-8761
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/5471
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.6075
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Language of contenteng
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PublisherPsychOpen GOLD
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Is version ofhttps://doi.org/10.5964/jnc.v6i1.212
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Is related tohttps://doi.org/10.23668/psycharchives.2526
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Keyword(s)number line estimationen_US
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Keyword(s)growth mixture modelingen_US
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Keyword(s)mixture modelingen_US
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Keyword(s)latent growth modelingen_US
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Keyword(s)special education needsen_US
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Keyword(s)migration backgrounden_US
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Dewey Decimal Classification number(s)150
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TitleWhat applying growth mixture modeling can tell us about predictors of number line estimationen_US
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DRO typearticle
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Issue1
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Journal titleJournal of Numerical Cognition
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Page numbers66–82
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Volume6
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Visible tag(s)Version of Recorden_US