“I’m so happy you’re playing with me”: A mixed methods approach to exploring how temperament predicts peer engagement
Banerjee, Sanchari
Loading…
Permalink
https://hdl.handle.net/2142/108411
Description
Title
“I’m so happy you’re playing with me”: A mixed methods approach to exploring how temperament predicts peer engagement
Author(s)
Banerjee, Sanchari
Issue Date
2020-04-23
Director of Research (if dissertation) or Advisor (if thesis)
Bub, Kristen L
Department of Study
Educational Psychology
Discipline
Educational Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Temperament
Peer engagement
Abstract
This study explores how preschoolers with different temperamental traits experience peer engagement, and whether a new qualitative tool provides additional information about peer interactions to existing measures. Using data from a sample of 106 children (44.8% males) participating in a larger National Science Foundation project, quantitative analyses provided evidence that temperamental traits predicted peer engagement; children with higher effortful control, lower surgency/extraversion, and lower negative affectivity were perceived by teachers to be more interactive and less disruptive. Qualitative observations on a subsample of four temperamentally diverse children provided data which revealed inhibition in children who may be quantitatively rated as easygoing and how they can thrive with particular supports, the effect of preferred peers on high surgency/extraverted children and how calming spaces/activities can help them constructively engage with peers, and finally the significance of detecting quiet negative affect, which is often overlooked. The importance of understanding context and content of peer interactions for children with different temperamental traits is also discussed, especially for practical application in the classroom.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.