Dissertation/Thesis Abstract

What Influences School District Effectiveness Growth Trajectories? A Growth Mixture Modeling (GMM) Analysis
by Ni, Xinyu, Ed.D., Teachers College, Columbia University, 2019, 104; 13805575
Abstract (Summary)

As a local education agency, school districts play an important role in providing instructional support for teachers and school leaders, making instructional goals, and allocating financial and human capital resources in a rational way to promote overall students’ learning outcomes. Studies on school districts that look to find reasons or characteristics related to school district success are known as district effectiveness research (DER). Previous quantitative research in DER using longitudinal dataset has assumed that all school district effectiveness (SDE) changes in a common pattern through a traditional ordinary linear regression or a hierarchal linear model while ignoring the probability that there might exist distinct subgroups of school district effectiveness trajectories. Thus, the purpose of the present study was to examine the existence of different SDE trajectories and how school district demographic variables and financial expenditures affect classification of SDE groups using a growth mixture model (GMM) with a national longitudinal dataset containing all public school districts in all 50 states and Washington D.C. from 2009 to 2015 (n = 11,185). The results indicated that (a) there are three different classes of school district effectiveness growth trajectories, which can be named as a constant SDE group (3.66%), a decreasing SDE group (34.16%), and an increasing SDE group (62.18%); (b) school district demographic characteristics such as a percentage of free lunch students and general administration expenditure per pupil are significantly associated with the probability of a school district being classified to a specific group; and (c) the longitudinal effects of school district demographic covariates and financial expenditures within each class such as school district locations (e.g., urban, suburban, etc.) are associated with the growth factors (intercept and slopes) in different ways.

Indexing (document details)
Advisor: DeCarlo, Lawrence T.
Commitee: Bowers, Alex J., Keller, Bryan S., Wells, Amy S.
School: Teachers College, Columbia University
Department: Human Development
School Location: United States -- New York
Source: DAI-A 77/09(E), Dissertation Abstracts International
Subjects: Education finance, Educational evaluation, Education Policy
Keywords: Accountability, District effectiveness, Financial expenditures, Growth mixture modeling, Latent growth curve modeling, Longitudinal analysis
Publication Number: 13805575
ISBN: 978-0-438-96928-5
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