Using Student Test Scores to Measure Principal Performance

Abstract

Expansion of the use of student test score data to measure teacher performance has fueled recent policy interest in using those data to measure the effects of school administrators as well. However, little research has considered the capacity of student performance data to uncover principal effects. Filling this gap, this article identifies multiple conceptual approaches for capturing the contributions of principals to student test score growth, develops empirical models to reflect these approaches, examines the properties of these models, and compares the results of the models empirically using data from a large urban school district. The paper then assesses the degree to which the estimates from each model are consistent with measures of principal performance that come from sources other than student test scores, such as school district evaluations. The results show that choice of model is substantively important for assessment. While some models identify principal effects as large as 0.15 standard deviations in math and 0.11 in reading, others find effects as low as 0.02 in both subjects for the same principals. We also find that the most conceptually unappealing models, which over-attribute school effects to principals, align more closely with non-test measures than do approaches that more convincingly separate the effect of the principal from the effects of other school inputs. AbstractExpansion of the use of student test score data to measure teacher performance has fueled recent policy interest in using those data to measure the effects of school administrators as well. However, little research has considered the capacity of student performance data to uncover principal effects. Filling this gap, this article identifies multiple conceptual approaches for capturing the contributions of principals to student test score growth, develops empirical models to reflect these approaches, examines the properties of these models, and compares the results of the models empirically using data from a large urban school district. The paper then assesses the degree to which the estimates from each model are consistent with measures of principal performance that come from sources other than student test scores, such as school district evaluations. The results show that choice of model is substantively important for assessment. While some models identify principal effects as large as 0.15 standard deviations in math and 0.11 in reading, others find effects as low as 0.02 in both subjects for the same principals. We also find that the most conceptually unappealing models, which over-attribute school effects to principals, align more closely with non-test measures than do approaches that more convincingly separate the effect of the principal from the effects of other school inputs. In contributing to the nascent literature on the use of student test score data to measure principal performance, this paper has four goals. First, it identifies a range of possible valueadded-style models for capturing principal effects...

The Relationship Between School Turnaround and Student Outcomes: A Meta-Analysis

“…Frameworks of turnaround leader competencies have been developed (Steiner et al, 2008), although they have yet to be validated, including documenting the extent to which measures of turnaround leadership are conceptually and empirically distinct from transformational or instructional leadership. Another approach would be to apply Grissom and colleagues’ (2015) measures of principal value-added to the leaders of the lowest performing schools, with a particular focus on the stability of principal value-added when principals work in multiple low-performing schools.…”

Principal Quality and Student Attendance

“…A handful of prior studies used VA modeling to estimate principal effects on student test scores. 5 Four studies used large state or district administrative data sets to estimate models with principal and school fixed effects (Branch et al, 2012; Chiang et al, 2016; Dhuey & Smith, 2018; Grissom et al, 2015). The magnitude of principals’ effects ranges between 0.05 and 0.20 SD —in other words, a 1 SD increase in principal quality increases student test scores by 0.05 to 0.20 SD .…”

“…Neither students nor principals are randomly assigned to schools, which heightens concern that principals are rewarded or punished for factors beyond their control. In particular, prior work highlighted the importance of separating principal effectiveness from school factors such as location or financial resources and demonstrated empirically that school value-added is a poor measure of principal effectiveness (Chiang et al, 2016; Grissom et al, 2015). 6…”

“…Despite conceptual support for a more dynamic model of principal effects, prior work most often restricted principal quality to be fixed. However, principals’ contributions to student outcomes may grow over time (e.g., Coelli & Green, 2012; Grissom et al, 2015). For instance, a new-to-school principal inherits many of the teachers hired under the old principal.…”

“…Drawing on 11 years of statewide data from Tennessee, this study contributes to the literature by examining whether principals affect student absences using a value-added (VA) framework. A large body of research linked effective leadership to school performance, including higher student test scores (Branch et al, 2012; Chiang et al, 2016; Coelli & Green, 2012; Dhuey & Smith, 2014, 2018; Grissom et al, 2015), better school climate (Burkhauser, 2017; Kraft et al, 2016; Sebastian & Allensworth, 2012), and lower teacher turnover (Boyd et al, 2011; Grissom & Bartanen, 2019b; Ladd, 2011). To date, however, no studies have estimated principal effects on student absenteeism.…”