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Using machine learning to understand age and gender classification based on infant temperament.
PLoS One. 2022; 17(4):e0266026.Plos

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.

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Authors+Show Affiliations

Gartstein MA
Washington State University, Pullman, WA, United States of America.
Seamon DE
University of Idaho, Moscow, ID, United States of America.
Mattera JA
Washington State University, Pullman, WA, United States of America.
Bosquet Enlow M
Boston Children's Hospital and Harvard Medical School, Boston, MA, United States of America.
Wright RJ
Department of Pediatrics, Kravis Children's Hospital, New York, NY, United States of America. Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
Perez-Edgar K
Pennsylvania State University, University Park, PA, United States of America.
Buss KA
Pennsylvania State University, University Park, PA, United States of America.
LoBue V
Rutgers University, New Brunswick, NJ, United States of America.
Bell MA
Virginia Tech, Blacksburg, VA, United States of America.
Goodman SH
Emory University, Atlanta, GA, United States of America.
Spieker S
University of Washington, Seattle, WA, United States of America.
Bridgett DJ
Northern Illinois University, DeKalb, IL, United States of America.
Salisbury AL
Virginia Commonwealth University, Richmond, VA, United States of America.
Gunnar MR
University of Minnesota, Minneapolis, MN, United States of America.
Mliner SB
University of Minnesota, Minneapolis, MN, United States of America.
Muzik M
University of Michigan, Ann Arbor, MI, United States of America.
Stifter CA
Pennsylvania State University, University Park, PA, United States of America.
Planalp EM
University of Wisconsin, Madison, WI, United States of America.
Mehr SA
Harvard University, Boston, MA, United States of America.
Spelke ES
Harvard University, Boston, MA, United States of America.
Lukowski AF
University of California, Irvine, CA, United States of America.
Groh AM
University of Missouri, Columbia, MO, United States of America.
Lickenbrock DM
Western Kentucky University, Bowling Green, KY, United States of America.
Santelli R
University of North Carolina, Chapel Hill, VA, United States of America.
Du Rocher Schudlich T
Western Washington University, Bellingham, WA, United States of America.
Anzman-Frasca S
University of Buffalo, Buffalo, NY, United States of America.
Thrasher C
University of Virginia, Charlottesville, VA, United States of America.
Diaz A
Ball State University, Muncie, IN, United States of America.
Dayton C
Wayne State University, Detroit, MI, United States of America.
Moding KJ
Purdue University, West Lafayette, IN, United States of America.
Jordan EM
Oklahoma State University, Stillwater, OK, United States of America.

MeSH

ChildChild, PreschoolFearFemaleHumansInfantInfant BehaviorMachine LearningMaleSurveys and QuestionnairesTemperament

Pub Type(s)

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

Language

eng

PubMed ID

35417495

Citation

Gartstein, Maria A., et al. "Using Machine Learning to Understand Age and Gender Classification Based On Infant Temperament." PloS One, vol. 17, no. 4, 2022, pp. e0266026.
Gartstein MA, Seamon DE, Mattera JA, et al. Using machine learning to understand age and gender classification based on infant temperament. PLoS One. 2022;17(4):e0266026.
Gartstein, M. A., Seamon, D. E., Mattera, J. A., Bosquet Enlow, M., Wright, R. J., Perez-Edgar, K., Buss, K. A., LoBue, V., Bell, M. A., Goodman, S. H., Spieker, S., Bridgett, D. J., Salisbury, A. L., Gunnar, M. R., Mliner, S. B., Muzik, M., Stifter, C. A., Planalp, E. M., Mehr, S. A., ... Jordan, E. M. (2022). Using machine learning to understand age and gender classification based on infant temperament. PloS One, 17(4), e0266026. https://doi.org/10.1371/journal.pone.0266026
Gartstein MA, et al. Using Machine Learning to Understand Age and Gender Classification Based On Infant Temperament. PLoS One. 2022;17(4):e0266026. PubMed PMID: 35417495.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Using machine learning to understand age and gender classification based on infant temperament. AU - Gartstein,Maria A, AU - Seamon,D Erich, AU - Mattera,Jennifer A, AU - Bosquet Enlow,Michelle, AU - Wright,Rosalind J, AU - Perez-Edgar,Koraly, AU - Buss,Kristin A, AU - LoBue,Vanessa, AU - Bell,Martha Ann, AU - Goodman,Sherryl H, AU - Spieker,Susan, AU - Bridgett,David J, AU - Salisbury,Amy L, AU - Gunnar,Megan R, AU - Mliner,Shanna B, AU - Muzik,Maria, AU - Stifter,Cynthia A, AU - Planalp,Elizabeth M, AU - Mehr,Samuel A, AU - Spelke,Elizabeth S, AU - Lukowski,Angela F, AU - Groh,Ashley M, AU - Lickenbrock,Diane M, AU - Santelli,Rebecca, AU - Du Rocher Schudlich,Tina, AU - Anzman-Frasca,Stephanie, AU - Thrasher,Catherine, AU - Diaz,Anjolii, AU - Dayton,Carolyn, AU - Moding,Kameron J, AU - Jordan,Evan M, Y1 - 2022/04/13/ PY - 2021/10/14/received PY - 2022/03/11/accepted PY - 2022/4/13/entrez PY - 2022/4/14/pubmed PY - 2022/4/16/medline SP - e0266026 EP - e0266026 JF - PloS one JO - PLoS One VL - 17 IS - 4 N2 - 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. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/35417495/Using_machine_learning_to_understand_age_and_gender_classification_based_on_infant_temperament_ DB - PRIME DP - Unbound Medicine ER -