This study examined indicators of 3rd Grade students’ non-school activity participation (NSAP) for associations with measures of social competence and reading performance. The Early Childhood Longitudinal Study (ECLS-K), representative of a 1999 kindergarten cohort of more than 14,000 students was used. The study constructed social competence composites from responses provided by students, parents and teachers. Principal component analysis and iterative bivariate correlations were utilized to derive the most robust composite for use in tests of the main hypotheses of the study. Results confirmed prior research findings that social competence has strong positive associations with academic performance. Thereafter, the social competence composite and ECLS Reading IRT Scale Score were used as alternative outcome measures in the bivariate analyses and linear regressions on non-school activity participation (NSAP) and breadth of non-school activity participation (BNSAP) scores. Cluster and multiple regression analyses combined in the study and brought demographic and cognitive controls to bear on iterations of five distinct views of the independent variables. Results indicated that girls influenced the association strengths observed for NSAP, and boys seemed to drive the direction and strength of BNSAP associations. Although regression betas for total samples were nominal, when viewed by demographic cluster samples the values were appreciatively improved. The use of the cluster distinctions provided views of significant associations that were otherwise dissolved into nominal aggregates. The results of these analyses support the construct validity of applying the aggregate scoring metric of EAP research to NSAP. Regression results prompted a call for future inquiries into student self-selection.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-2762 |
| Date | 01 January 2011 |
| Creators | Mahone, Robert H |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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