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Using machine learning to understand age and gender classification based on infant temperament
Age and gender differences are prominent in the temperament literature, with the former particularly salient in infancy and the latter noted as early as the first year of life. This study represents a meta-analysis utilizing Infant Behavior Questionnaire-Revised (IBQ-R) data collected across multipl...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007342/ https://www.ncbi.nlm.nih.gov/pubmed/35417495 http://dx.doi.org/10.1371/journal.pone.0266026 |
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author | Gartstein, Maria A. Seamon, D. Erich Mattera, Jennifer A. Bosquet Enlow, Michelle Wright, Rosalind J. Perez-Edgar, Koraly Buss, Kristin A. LoBue, Vanessa Bell, Martha Ann Goodman, Sherryl H. Spieker, Susan Bridgett, David J. Salisbury, Amy L. Gunnar, Megan R. Mliner, Shanna B. Muzik, Maria Stifter, Cynthia A. Planalp, Elizabeth M. Mehr, Samuel A. Spelke, Elizabeth S. Lukowski, Angela F. Groh, Ashley M. Lickenbrock, Diane M. Santelli, Rebecca Du Rocher Schudlich, Tina Anzman-Frasca, Stephanie Thrasher, Catherine Diaz, Anjolii Dayton, Carolyn Moding, Kameron J. Jordan, Evan M. |
author_facet | Gartstein, Maria A. Seamon, D. Erich Mattera, Jennifer A. Bosquet Enlow, Michelle Wright, Rosalind J. Perez-Edgar, Koraly Buss, Kristin A. LoBue, Vanessa Bell, Martha Ann Goodman, Sherryl H. Spieker, Susan Bridgett, David J. Salisbury, Amy L. Gunnar, Megan R. Mliner, Shanna B. Muzik, Maria Stifter, Cynthia A. Planalp, Elizabeth M. Mehr, Samuel A. Spelke, Elizabeth S. Lukowski, Angela F. Groh, Ashley M. Lickenbrock, Diane M. Santelli, Rebecca Du Rocher Schudlich, Tina Anzman-Frasca, Stephanie Thrasher, Catherine Diaz, Anjolii Dayton, Carolyn Moding, Kameron J. Jordan, Evan M. |
author_sort | Gartstein, Maria A. |
collection | PubMed |
description | Age and gender differences are prominent in the temperament literature, with the former particularly salient in infancy and the latter noted as early as the first year of life. This study represents a meta-analysis utilizing Infant Behavior Questionnaire-Revised (IBQ-R) data collected across multiple laboratories (N = 4438) to overcome limitations of smaller samples in elucidating links among temperament, age, and gender in early childhood. Algorithmic modeling techniques were leveraged to discern the extent to which the 14 IBQ-R subscale scores accurately classified participating children as boys (n = 2,298) and girls (n = 2,093), and into three age groups: youngest (< 24 weeks; n = 1,102), mid-range (24 to 48 weeks; n = 2,557), and oldest (> 48 weeks; n = 779). Additionally, simultaneous classification into age and gender categories was performed, providing an opportunity to consider the extent to which gender differences in temperament are informed by infant age. Results indicated that overall age group classification was more accurate than child gender models, suggesting that age-related changes are more salient than gender differences in early childhood with respect to temperament attributes. However, gender-based classification was superior in the oldest age group, suggesting temperament differences between boys and girls are accentuated with development. Fear emerged as the subscale contributing to accurate classifications most notably overall. This study leads infancy research and meta-analytic investigations more broadly in a new direction as a methodological demonstration, and also provides most optimal comparative data for the IBQ-R based on the largest and most representative dataset to date. |
format | Online Article Text |
id | pubmed-9007342 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-90073422022-04-14 Using machine learning to understand age and gender classification based on infant temperament Gartstein, Maria A. Seamon, D. Erich Mattera, Jennifer A. Bosquet Enlow, Michelle Wright, Rosalind J. Perez-Edgar, Koraly Buss, Kristin A. LoBue, Vanessa Bell, Martha Ann Goodman, Sherryl H. Spieker, Susan Bridgett, David J. Salisbury, Amy L. Gunnar, Megan R. Mliner, Shanna B. Muzik, Maria Stifter, Cynthia A. Planalp, Elizabeth M. Mehr, Samuel A. Spelke, Elizabeth S. Lukowski, Angela F. Groh, Ashley M. Lickenbrock, Diane M. Santelli, Rebecca Du Rocher Schudlich, Tina Anzman-Frasca, Stephanie Thrasher, Catherine Diaz, Anjolii Dayton, Carolyn Moding, Kameron J. Jordan, Evan M. PLoS One Research Article Age and gender differences are prominent in the temperament literature, with the former particularly salient in infancy and the latter noted as early as the first year of life. This study represents a meta-analysis utilizing Infant Behavior Questionnaire-Revised (IBQ-R) data collected across multiple laboratories (N = 4438) to overcome limitations of smaller samples in elucidating links among temperament, age, and gender in early childhood. Algorithmic modeling techniques were leveraged to discern the extent to which the 14 IBQ-R subscale scores accurately classified participating children as boys (n = 2,298) and girls (n = 2,093), and into three age groups: youngest (< 24 weeks; n = 1,102), mid-range (24 to 48 weeks; n = 2,557), and oldest (> 48 weeks; n = 779). Additionally, simultaneous classification into age and gender categories was performed, providing an opportunity to consider the extent to which gender differences in temperament are informed by infant age. Results indicated that overall age group classification was more accurate than child gender models, suggesting that age-related changes are more salient than gender differences in early childhood with respect to temperament attributes. However, gender-based classification was superior in the oldest age group, suggesting temperament differences between boys and girls are accentuated with development. Fear emerged as the subscale contributing to accurate classifications most notably overall. This study leads infancy research and meta-analytic investigations more broadly in a new direction as a methodological demonstration, and also provides most optimal comparative data for the IBQ-R based on the largest and most representative dataset to date. Public Library of Science 2022-04-13 /pmc/articles/PMC9007342/ /pubmed/35417495 http://dx.doi.org/10.1371/journal.pone.0266026 Text en © 2022 Gartstein et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gartstein, Maria A. Seamon, D. Erich Mattera, Jennifer A. Bosquet Enlow, Michelle Wright, Rosalind J. Perez-Edgar, Koraly Buss, Kristin A. LoBue, Vanessa Bell, Martha Ann Goodman, Sherryl H. Spieker, Susan Bridgett, David J. Salisbury, Amy L. Gunnar, Megan R. Mliner, Shanna B. Muzik, Maria Stifter, Cynthia A. Planalp, Elizabeth M. Mehr, Samuel A. Spelke, Elizabeth S. Lukowski, Angela F. Groh, Ashley M. Lickenbrock, Diane M. Santelli, Rebecca Du Rocher Schudlich, Tina Anzman-Frasca, Stephanie Thrasher, Catherine Diaz, Anjolii Dayton, Carolyn Moding, Kameron J. Jordan, Evan M. Using machine learning to understand age and gender classification based on infant temperament |
title | Using machine learning to understand age and gender classification based on infant temperament |
title_full | Using machine learning to understand age and gender classification based on infant temperament |
title_fullStr | Using machine learning to understand age and gender classification based on infant temperament |
title_full_unstemmed | Using machine learning to understand age and gender classification based on infant temperament |
title_short | Using machine learning to understand age and gender classification based on infant temperament |
title_sort | using machine learning to understand age and gender classification based on infant temperament |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007342/ https://www.ncbi.nlm.nih.gov/pubmed/35417495 http://dx.doi.org/10.1371/journal.pone.0266026 |
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