Predicting Children’s Early School Outcomes from their Social-Regulatory Profiles: A Person-Centered Approach

Social information processing (SIP) and self-regulation skills have been widely examined as predictors of concurrent and long-term outcomes for children. Although aspects of SIP and self-regulation have been shown to differentially predict academic and behavioral outcomes for children, few studies have examined the development of these areas of functioning in early childhood using person-centered analytic techniques. This study utilized secondary data analysis and a person-centered approach to identify profiles of children’s social-regulatory processing prior to kindergarten entry and examine profile associations with individual and contextual predictors as well as first-grade academic and behavioral outcomes. Results supported four social-regulatory classes. Class membership was associated with early cognitive ability and maternal sensitivity, and classes differed significantly on first grade academic achievement outcomes. A group with higher functioning across SIP and self-regulation variables earned higher first-grade reading and math scores than a group with lower functioning across the same variables. Additionally, the group with higher overall SIP and self-regulation functioning performed better on math achievement than a group with lower attribution scores and moderately low self-regulation, and a group with higher SIP and moderate regulation performed better in math than the group with low SIP and self-regulation. No differences were found between groups for indicators of first grade behavioral adjustment. Results are discussed within the context of prior research and current educational practice, and recommendations for future person-centered study of SIP and self-regulation factors are presented.

Author dc:creator

• Coco, Susanna

Advisor dc:contributor.advisor

• Cook, Clayton R

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1773/36584

OAI identifier oai:identifier

oai:digital.lib.washington.edu:1773/36584