Longitudinal achievement growth analysis

Research Method · All research methods
Evidence · 22 claims (19 for, 3 mixed) · 11 studies (10 associational, 1 theoretical), q1–q2 · 0 of 11 report an effect size · 22 claims rest on one study

Description

This principle holds that educators and researchers should use longitudinal data analysis to track how academic achievement for DHH students progresses over time. The article argues that "Longitudinal data analysis is critical to understanding how academic achievement for DHH students progresses over time and where they may need additional support on their academic journey to achieve at the level of their hearing peers." It grounds support decisions in multi-year growth evidence rather than single snapshots.

Accounts

• Use longitudinal data analysis to understand how DHH students' achievement progresses over time and where they need additional support: This principle holds that educators and researchers should use longitudinal data analysis to track how academic achievement for DHH students progresses over time. The article argues that "Longitudinal data analysis is critical to understanding how academic achievement for DHH students progresses over time and where they may need additional support on their academic journey to achieve at the level of their hearing peers." It grounds support decisions in multi-year growth evidence rather than single snapshots. (Stephanie Cawthon et al. (2022))
• Mathematica's value-added analysis approach for Oklahoma's Teacher and Leader Effectiveness Evaluation System: This element is the analytic approach Mathematica applied to produce value-added results for Oklahoma's TLE evaluation system. The brief states it "provides an overview of the approach that Mathematica applied to produce the value-added results used in the evaluation system." The work was carried out under the associated project Value-Added Analysis Services for the State of Oklahoma, with a time frame of 2013–2018, and was prepared for the State of Oklahoma. (Albert Y. Liu et al. (2016))
• Student-level value-added regression model with teacher dosage variables and student background controls: The analysis uses a student-level regression model in which "test score at the end of each year (post -test)" is the outcome and "test score at the end of the previous year (pre -test)" is a key control, with teacher fixed effects as the object of interest. Teacher dosage variables equal the percentage of the year a student was taught by each teacher. Controls include special education status, FRL eligibility, English language learner status, over-age for grade, race/ethnicity, and a classroom-level control for student turnover. Measurement error in the pretest is addressed with an errors-in-variables two-stage procedure, and robust standard errors pool data across years. (Steven Glazerman et al. (2011))
• Growth norms modeled to be sensitive to summer breaks and instructional exposure: The 2020 norms model learning trajectories as a function of instructional exposure so that the expected growth between terms reflects summer breaks and the ebb of instructional time. The report states: "We have modeled the norms to be sensitive to the impact of summ er breaks on learning." This allows term-to-term growth norms (fall to winter, winter to spring, spring to fall) that differ according to the instructional days separating the test events, rather than assuming uniform growth across calendar time. (Thum et al. (2020))
• The learning productivity measurement system as a framework for using student and school descriptive indicators of learning progress: The article presents a measurement framework that moves from the conditional growth index toward a learning productivity measurement system for describing learning progress. Its title frames this as "From the conditional growth index to the learning productivity measurement system", indicating a system that organizes student and school descriptive indicators of learning progress into a coherent measurement approach for interpreting growth over time. (The effective use of student et al. (2015))
• Value-added academic productivity indicator estimating each school's contribution to student learning from longitudinal test scores: The Consortium's academic productivity research uses a value-added approach in which, "Using Iowa Tests of Basic Skills (ITBS) data, the productivity indicator estimates each school's contribution, or value-added, to the learning of students enrolled at that school." A more refined statistical model developed with Steve Raudenbush assumes each student has an academic growth trajectory shaped by latent growth parameters and exposure to instruction in a given class and school, permitting estimation of student growth curves and time trends for classroom and school effects. The agenda proposes extending this to classroom-level analysis and comparing it against NCLB adequate-yearly-progress bench (Sebring (2003))
• Track English learners as kindergarten-entry cohorts over time rather than as a point-in-time snapshot to avoid selection bias from reclassification: The report argues that studying only current ELs distorts the picture of EL progress, because the highest-achieving ELs are reclassified out of the EL pool and newcomers enter it later. It instead follows "all California public school students who began receiving EL services starting in kindergarten" through all subsequent years, including years after reclassification. The authors state that "We can more clearly gauge the progress of EL students using this longitudinal, tracking-over-years approach." (Novicoff et al. (2024))
• Do not assume linear within-year growth when estimating learning from achievement data: Analyses that estimate how much students learn during the school year — for school-year-length policy, summer-loss studies, or gap research — should test rather than assume that within-year growth is linear. The article's own finding is that "assuming linear within-year growth is often not justified, particularly in reading," so linear-growth assumptions can distort such estimates, especially in reading. (Megan Kuhfeld (2020))
• Studies of cognitive predictors of achievement should include domain-knowledge indicators at every measurement point: The article concludes that IQ and WM may matter mainly in early schooling when domain knowledge is not yet developed, and that their impact fades once prior domain knowledge is estimated. It therefore recommends that "future research on the topic should be based on longitudinal designs" that "include relevant indicators of domain knowledge at each time point in order to estimate the size of specific IQ and WM effects and their changes over time." This is a methodological design principle for research on cognitive predictors of learning. (Schneider Wolfgang (2017))

Claims

• DHH students in grades 2–8 continue to build reading and mathematics skills and do not necessarily plateau in the elementary grades [+M]
• Oklahoma educators received value-added results in spring 2016 based on instruction provided in the 2014–2015 school year [+W]
• Spring 2016 was the third year Oklahoma's TLE provided value-added results to educators [+W]
• The value-added models used for Oklahoma's spring 2016 results were updated relative to prior years [+W]
• The working paper examines the sensitivity and precision of teacher value-added estimates under specifications differing in student-level, peer-level, and double-lagged achievement controls [+W]
• Omitting same-subject pre-tests affects value-added estimates more than excluding other student background characteristics [+W]
• Teacher value-added estimates are highly correlated across model specifications that differ in student and peer control variables [+W]
• The paper tests two previously unevaluated VAM model variations: teacher-year level average peer characteristics and demographic variation in the lagged-achievement relationship [+W]
• Value-added teacher rankings are insensitive to correcting measurement error in the pretest, with correlations of 0.91–0.99 for elementary and 0.77–0.98 for middle school teachers [+W]
• The conditional growth index serves as a descriptive indicator of student learning progress [+W]
• Federal policy requirements drove growth modeling and teacher value-added evaluation under NCLB and RTTP [+W]
• The volume addresses criteria for evaluating growth models and how outputs from growth models should be utilized [+W]
• Student growth modeling and teacher value-added modeling are complex due to non-random assignment, measurement error, multidimensionality, and covariates [+W]
• Multiple sources of student information can be accumulated to estimate teacher effectiveness [+W]
• Teacher effects may be heterogeneous across students of differing aptitudes, affecting growth modeling and teacher evaluation [+W]
• During Phase I reform, a subset of very disadvantaged, racially isolated African-American schools showed weak improvement [+M]
• CPS elementary school productivity improvements stalled after 1997, with no evidence of significant productivity growth since then [+W]
• ITBS test form progression systematically influences eighth-grade math gain scores, with Form L-to-M years showing the largest gains and Form K-to-L years the smallest [~W]
• CPS elementary ITBS scores improved across the 1990s in both reading and math for all age groups, but trend gains flattened after 1998 [~W]
• All six CPS age cohorts saw a slight 2001 decline in math scores of less than one month, which a Rasch-score re-analysis suggests reflects an actual drop in learning rather than test-form effects [~W]
• Within-school-year achievement growth is often not linear, particularly in reading [+M]
• Previous domain knowledge accounts for most of the variance in later achievement, considerably reducing the predictive impact of IQ and WM [+M]

Related Research Methods

-

Key Sources

• Stephanie Cawthon, Johny Daniel, North Cooc, Ana Vielma. (2022). Longitudinal models of reading and mathematics achievement in deaf and hard of hearing students. The Journal of Deaf Studies and Deaf Education. https://doi.org/10.1093/deafed/enac033
• Albert Y. Liu, Elias Walsh, Dallas Dotter. (2016). Measuring Teacher and School Value Added in Oklahoma, Spring 2016. Oakland, CA: Mathematica Policy Research. https://www.mathematica.org/publications/measuring-teacher-and-school-value-added-in-oklahoma-spring-2016
• Steven Glazerman and Jeffrey Max. (2011). Do Low-Income Students Have Equal Access to the Highest-Performing Teachers? NCEE Technical Appendix. http://ies.ed.gov/ncee/pubs/20114016/pdf/20114016.pdf
• Thum, Y. M., & Kuhfeld, M. (2020). NWEA 2020 MAP Growth Achievement Status and Growth Norms for Students and Schools. NWEA Research Report. Portland, OR: NWEA. https://www.nwea.org/research/publication/nwea-2020-map-growth-achievement-status-and-growth-norms-for-students-and-schools/
• The effective use of student and school descriptive indicators of learning progress: From the conditional growth index to the learning productivity measurement system. (2015). Value Added Modeling and Growth Modeling with Particular Application to Teacher and School Effectiveness, 237–269. https://www.nwea.org/research/publication/the-effective-use-of-student-and-school-descriptive-indicators-of-learning-progress-from-the-conditional-growth-index-to-the-learning-productivity-measurement-system/
• Sebring, Penny Bender. (2003). Research Agenda 2004-2008. Consortium on Chicago School Research. https://www.consortium-chicago.org/publications

Page metadata

Field	Value
type	research-method
id	longitudinal-achievement-growth-analysis
title	Longitudinal achievement growth analysis
description	This principle holds that educators and researchers should use longitudinal data analysis to track how academic achievement for DHH students progresses over time.
status	review
moved_from	element/mathematica-value-added-approach-oklahoma-tle, element/student-level-value-added-regression-model, theory/growth-norms-sensitive-to-summer-breaks, theory/learning-productivity-measurement-system-framework, theory/value-added-academic-productivity-indicator-ccsr
generated	by process:settle-candidates on 2026-10-09
trust	unverified — no one has confirmed this page's content
sources	5 — albert-y-liu-2016, steven-glazerman-and-jeffrey-max-2011, thum-2020, the-effective-use-of-student-and-school-descriptive-indicators-of-learning-progress-from-the-conditional-growth-index-to-the-learning-productivity-measurement-system-2015, sebring-2003

Frontmatter is the page's machine-readable record — see the Schema & Guide.