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.

Fig 1. Note: lda—Linear Discriminant Analysis; glm—Generalized Linear Modeling; svm—Support Vector Machines; knn—K-Nearest Neighbor; nb—Naïve Bayes; cart—Classification and Regression Trees; c50—C5.0 Classification; treebag—Bootstrapped Aggregated Trees; rf—Ensembled Decision Trees (Random Forest); gbm—Gradient Boosting Method; adabag—Multi-class Adaptive Boosting (AdaBoost).

Fig 2. Note: lda—Linear Discriminant Analysis; glm—Generalized Linear Modeling; svm—Support Vector Machines; knn—K-Nearest Neighbor; nb—Naïve Bayes; cart—Classification and Regression Trees; c50—C5.0 Classification; treebag—Bootstrapped Aggregated Trees; rf—Ensembled Decision Trees (Random Forest); gbm—Gradient Boosting Method; adabag—Multi-class Adaptive Boosting (AdaBoost).

Fig 3. Note: lda—Linear Discriminant Analysis; glm—Generalized Linear Modeling; svm—Support Vector Machines; knn—K-Nearest Neighbor; nb—Naïve Bayes; cart—Classification and Regression Trees; c50—C5.0 Classification; treebag—Bootstrapped Aggregated Trees; rf—Ensembled Decision Trees (Random Forest); gbm—Gradient Boosting Method; adabag—Multi-class Adaptive Boosting (AdaBoost).