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 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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Publication Details

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UNC Libraries
Published
2025-04-18
DOI
https://doi.org/10.17615/eb4p-tg09
Primary Topic
Child and Adolescent Psychosocial and Emotional Development
Type
article
Field-Weighted Citation Impact
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article

Using machine learning to understand age and gender classification based on infant temperament

Jennifer A. Mattera, Michelle Bosquet Enlow, Maria A. Gartstein, Stephanie Anzman‐Frasca et al.
UNC Libraries
Child and Adolescent Psychosocial and Emotional Development
article

Using machine learning to understand age and gender classification based on infant temperament

Jennifer A. Mattera, Michelle Bosquet Enlow, Maria A. Gartstein, Stephanie Anzman‐Frasca, Megan R. Gunnar, Cynthia A. Stifter, Angela F. Lukowski, Ashley M. Groh, Elizabeth M. Planalp, Cat Thrasher, Susan J. Spieker, Anjolii Diaz, Diane M. Lickenbrock, Erich Seamon, Koraly Pérez‐Edgar, Shanna B. Mliner, Evan M. Jordan, Kameron J. Moding, Kristin A. Buss, Vanessa LoBue, David J. Bridgett, Sherryl H. Goodman, Rosalind J. Wright, Amy L. Salisbury, Carolyn J. Dayton, Elizabeth S. Spelke, Martha Ann Bell, Maria Muzik, Samuel A. Mehr, Tina Du Rocher Schudlich, Rebecca Santelli
article en

Abstract

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.

UNC Libraries
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 55%
Child and Adolescent Psychosocial and Emotional Development
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