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

Maria A. Gartstein, D. Erich Seamon, Jennifer A. Mattera, Michelle Bosquet Enlow, Rosalind J. Wright, Koraly Perez-Edgar, Kristin A. Buss, Vanessa LoBue, Martha Ann Bell, Sherryl H. Goodman, Susan 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. SpelkeAngela F. Lukowski, Ashley M. Groh, Diane M. Lickenbrock, Rebecca Santelli, Tina Du Rocher Schudlich, Stephanie Anzman-Frasca, Catherine Thrasher, Anjolii Diaz, Carolyn Dayton, Kameron J. Moding, Evan M. Jordan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Article numbere0266026
JournalPloS one
Issue number4 April
StatePublished - Apr 2022

Bibliographical note

Funding Information:
MBE: R01HL095606, R01HD082078; National Institutes of Health KPE, KB, & VL: NIH R01 MH109692; R21 MH103627 National Institutes of Health MAB: R01 HD049878; R03 HD043057 National Institutes of Health SG: 1P50 MH58922-01A1; 1P50 MH077928-01A1 National Institutes of Health SS: 5R01HD080851-05 National Institutes of Health AS: R01MH78033 National Institutes of Health DL: 8P0GM103436; P20GM103436; 8P20GM103436 National Institutes of Health TDRS: MFS 901; MFS 907 Western Washington University SAF: DK72996; M01RR10732 National Institutes of Health None of the funders had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2022 Gartstein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


  • Child
  • Child, Preschool
  • Fear
  • Female
  • Humans
  • Infant
  • Infant Behavior
  • Machine Learning
  • Male
  • Surveys and Questionnaires
  • Temperament

PubMed: MeSH publication types

  • Journal Article
  • Meta-Analysis
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural


Dive into the research topics of 'Using machine learning to understand age and gender classification based on infant temperament'. Together they form a unique fingerprint.

Cite this