Dataset Information

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

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.

SUBMITTER: Gartstein MA

PROVIDER: S-EPMC9007342 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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

Gartstein Maria A MA Seamon D Erich DE Mattera Jennifer A JA Bosquet Enlow Michelle M Wright Rosalind J RJ Perez-Edgar Koraly K Buss Kristin A KA LoBue Vanessa V Bell Martha Ann MA Goodman Sherryl H SH Spieker Susan S Bridgett David J DJ Salisbury Amy L AL Gunnar Megan R MR Mliner Shanna B SB Muzik Maria M Stifter Cynthia A CA Planalp Elizabeth M EM Mehr Samuel A SA Spelke Elizabeth S ES Lukowski Angela F AF Groh Ashley M AM Lickenbrock Diane M DM Santelli Rebecca R Du Rocher Schudlich Tina T Anzman-Frasca Stephanie S Thrasher Catherine C Diaz Anjolii A Dayton Carolyn C Moding Kameron J KJ Jordan Evan M EM

PloS one 20220413 4

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 disce ...[more]

PMID: 35417495

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Dataset Information

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

Publications

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

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