Using machine learning to understand age and gender classification based on infant temperament Article Swipe
Maria A. Gartstein
,
Erich Seamon
,
Jennifer A. Mattera
,
Michelle Bosquet Enlow
,
Rosalind J. Wright
,
Koraly Pérez‐Edgar
,
Kristin A. Buss
,
Vanessa LoBue
,
Martha Ann Bell
,
Sherryl H. Goodman
,
Susan J. Spieker
,
David J. Bridgett
,
Amy L. Salisbury
,
Megan R. Gunnar
,
Shanna B. Mliner
,
Maria Muzik
,
Cynthia A. Stifter
,
Elizabeth M. Planalp
,
Samuel A. Mehr
,
Elizabeth S. Spelke
,
Angela F. Lukowski
,
Ashley M. Groh
,
Diane M. Lickenbrock
,
Rebecca Santelli
,
Tina Du Rocher Schudlich
,
Stephanie Anzman‐Frasca
,
Cat Thrasher
,
Anjolii Diaz
,
Carolyn J. Dayton
,
Kameron J. Moding
,
Evan M. Jordan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.17615/eb4p-tg09
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.17615/eb4p-tg09
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17615/eb4p-tg09
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415939047
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4415939047Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17615/eb4p-tg09Digital Object Identifier
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Using machine learning to understand age and gender classification based on infant temperamentWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-04-18Full publication date if available
- Authors
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Maria A. Gartstein, Erich Seamon, Jennifer A. Mattera, Michelle Bosquet Enlow, Rosalind J. Wright, Koraly Pérez‐Edgar, Kristin A. Buss, Vanessa LoBue, Martha Ann Bell, Sherryl H. Goodman, Susan J. Spieker, David J. Bridgett, Amy L. Salisbury, Megan R. Gunnar, Shanna B. Mliner, Maria Muzik, Cynthia A. Stifter, Elizabeth M. Planalp, Samuel A. Mehr, Elizabeth S. Spelke, Angela F. Lukowski, Ashley M. Groh, Diane M. Lickenbrock, Rebecca Santelli, Tina Du Rocher Schudlich, Stephanie Anzman‐Frasca, Cat Thrasher, Anjolii Diaz, Carolyn J. Dayton, Kameron J. Moding, Evan M. JordanList of authors in order
- Landing page
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https://doi.org/10.17615/eb4p-tg09Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doi.org/10.17615/eb4p-tg09Direct OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.However, | 182 |
| abstract_inverted_index.accurate | 159, 210 |
| abstract_inverted_index.children | 83 |
| abstract_inverted_index.consider | 136 |
| abstract_inverted_index.informed | 146 |
| abstract_inverted_index.modeling | 65 |
| abstract_inverted_index.multiple | 42 |
| abstract_inverted_index.overall. | 214 |
| abstract_inverted_index.overcome | 48 |
| abstract_inverted_index.provides | 235 |
| abstract_inverted_index.research | 219 |
| abstract_inverted_index.subscale | 78, 207 |
| abstract_inverted_index.superior | 186 |
| abstract_inverted_index.youngest | 99 |
| abstract_inverted_index.childhood | 176 |
| abstract_inverted_index.collected | 40 |
| abstract_inverted_index.direction | 228 |
| abstract_inverted_index.indicated | 151 |
| abstract_inverted_index.leveraged | 68 |
| abstract_inverted_index.mid-range | 106 |
| abstract_inverted_index.prominent | 5 |
| abstract_inverted_index.providing | 132 |
| abstract_inverted_index.utilizing | 34 |
| abstract_inverted_index.accurately | 80 |
| abstract_inverted_index.categories | 129 |
| abstract_inverted_index.childhood. | 63 |
| abstract_inverted_index.classified | 81 |
| abstract_inverted_index.performed, | 131 |
| abstract_inverted_index.represents | 31 |
| abstract_inverted_index.suggesting | 164, 192 |
| abstract_inverted_index.techniques | 66 |
| abstract_inverted_index.Algorithmic | 64 |
| abstract_inverted_index.accentuated | 200 |
| abstract_inverted_index.age-related | 166 |
| abstract_inverted_index.attributes. | 181 |
| abstract_inverted_index.comparative | 238 |
| abstract_inverted_index.differences | 3, 142, 173, 194 |
| abstract_inverted_index.elucidating | 54 |
| abstract_inverted_index.limitations | 49 |
| abstract_inverted_index.literature, | 9 |
| abstract_inverted_index.opportunity | 134 |
| abstract_inverted_index.temperament | 8, 144, 180, 193 |
| abstract_inverted_index.contributing | 208 |
| abstract_inverted_index.development. | 202 |
| abstract_inverted_index.gender-based | 183 |
| abstract_inverted_index.laboratories | 43 |
| abstract_inverted_index.particularly | 13 |
| abstract_inverted_index.simultaneous | 123 |
| abstract_inverted_index.temperament, | 57 |
| abstract_inverted_index.Additionally, | 122 |
| abstract_inverted_index.meta-analysis | 33 |
| abstract_inverted_index.meta-analytic | 221 |
| abstract_inverted_index.participating | 82 |
| abstract_inverted_index.classification | 124, 156, 184 |
| abstract_inverted_index.demonstration, | 232 |
| abstract_inverted_index.investigations | 222 |
| abstract_inverted_index.methodological | 231 |
| abstract_inverted_index.representative | 249 |
| abstract_inverted_index.classifications | 211 |
| abstract_inverted_index.Questionnaire-Revised | 37 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 31 |
| citation_normalized_percentile |