Volume 28, Issue 4

Norm‐ and Criterion‐Referenced Student Growth

D. Betebenner

National Center for the Improvement of Educational Assessment

Search for more papers by this author
First published: 11 December 2009
Citations: 89

D. Betebenner is a Senior Associate at the National Center for the Improvemernt of Educational Assessment, PO Box 351, Dover, NH 03821‐0351; dbetebenner@nciea.org.

Abstract

Annual student achievement data derived from state assessment programs have led to widespread enthusiasm for statistical models suitable for longitudinal analysis. The current policy environment's adherence to high stakes accountability vis‐à‐vis No Child Left Behind (NCLB)'s universal proficiency mandate has fostered an impoverished view of what an examination of student growth can provide. To address this, student growth percentiles are introduced supplying a normative description of growth capable of accommodating criterion‐referenced aims like those embedded within NCLB and, more importantly, extending possibilities for descriptive data use beyond the current high stakes paradigm.

Number of times cited according to CrossRef: 89

  • An Alternative Approach to Cognitive and Achievement Relations Research: An Introduction to Quantile Regression, Journal of Pediatric Neuropsychology, 10.1007/s40817-020-00086-3, (2020).
  • Evaluating Random and Systematic Error in Student Growth Percentiles, Applied Measurement in Education, 10.1080/08957347.2020.1789139, (1-13), (2020).
  • Estimating the Accuracy of Relative Growth Measures Using Empirical Data, Journal of Educational Measurement, 10.1111/jedm.12243, 57, 1, (92-123), (2019).
  • Comparing the Accuracy of Student Growth Measures, Journal of Educational Measurement, 10.1111/jedm.12242, 57, 1, (71-91), (2019).
  • Allocation to groups: Examples of Lord's paradox, British Journal of Educational Psychology, 10.1111/bjep.12300, 90, S1, (35-49), (2019).
  • Is Creativity Compatible with Educational Accountability? Promise and Pitfalls of Using Assessment to Monitor and Enhance a Complex Construct, The Palgrave Handbook of Social Creativity Research, 10.1007/978-3-319-95498-1, (501-514), (2019).
  • Use of observational measures to predict student achievement, Studies in Educational Evaluation, 10.1016/j.stueduc.2019.05.001, 62, (197-208), (2019).
  • The Influence of Student Abilities and High School on Student Growth: A Case Study of Chinese National College Entrance Exam, IEEE Access, 10.1109/ACCESS.2019.2946503, 7, (148254-148264), (2019).
  • School board directors’ information needs and financial reporting’s role, Journal of Public Budgeting, Accounting & Financial Management, 10.1108/JPBAFM-09-2018-0097, 31, 4, (578-595), (2019).
  • Scoring Repeated Standardized Tests to Estimate Capacity, Not Just Current Ability, Policy Insights from the Behavioral and Brain Sciences, 10.1177/2372732219862578, 6, 2, (218-224), (2019).
  • Examining the Dual Purpose Use of Student Learning Objectives for Classroom Assessment and Teacher Evaluation, Journal of Educational Measurement, 10.1111/jedm.12233, 56, 4, (686-714), (2019).
  • Principals’ Uses and Interpretations of Student Growth Percentile Data, Journal of School Leadership, 10.1177/105268461602600101, 26, 1, (6-33), (2019).
  • Obscuring English Learners From State Accountability: The Case of Indiana’s Language Blind Policies, Educational Policy, 10.1177/0895904818823751, (089590481882375), (2019).
  • What Boosts Talent Development? Examining Predictors of Academic Growth in Secondary School Among Academically Advanced Youth Across 21 Years, Gifted Child Quarterly, 10.1177/0016986219869042, (001698621986904), (2019).
  • Development and Validation of a Vertical Scale for Formative Assessment in Mathematics, Frontiers in Education, 10.3389/feduc.2019.00103, 4, (2019).
  • The Concept of Academic Mobility: Normative and Methodological Considerations, American Educational Research Journal, 10.3102/0002831219876935, (000283121987693), (2019).
  • Study on student performance estimation, student progress analysis, and student potential prediction based on data mining, Computers & Education, 10.1016/j.compedu.2018.04.006, 123, (97-108), (2018).
  • Median Growth Percentiles (MGPs): Assessment of Intertemporal Stability and Correlations with Observational Scores, Educational Assessment, 10.1080/10627197.2018.1449634, 23, 2, (139-155), (2018).
  • Linking Not-Quite-Vertical Scales Through Multidimensional Item Response Theory, Measurement: Interdisciplinary Research and Perspectives, 10.1080/15366367.2018.1497350, 16, 3, (155-167), (2018).
  • Estimating school effectiveness with student growth percentile and gain score models, Journal of Applied Statistics, 10.1080/02664763.2018.1426742, 45, 14, (2536-2547), (2018).
  • Value-Added and Student Growth Percentile Models: What Drives Differences in Estimated Classroom Effects?, Statistics and Public Policy, 10.1080/2330443X.2018.1438938, 5, 1, (1-8), (2018).
  • Improving Educational Assessment: Multivariate Statistical Methods, Policy Insights from the Behavioral and Brain Sciences, 10.1177/2372732217747006, 5, 1, (19-24), (2017).
  • An elusive policy imperative: data and methodological challenges when using growth in student achievement to evaluate teacher education programs’ ‘Value-Added’, Teaching Education, 10.1080/10476210.2017.1296828, 28, 3, (296-316), (2017).
  • Leader perceptions and student achievement, International Journal of Educational Management, 10.1108/IJEM-03-2016-0054, 31, 7, (1103-1118), (2017).
  • Educational Theory, Learning Path Construction in e-Learning, 10.1007/978-981-10-1944-9_2, (15-29), (2017).
  • The Implications of Reduced Testing for Teacher Accountability, AERA Open, 10.1177/2332858417704411, 3, 2, (233285841770441), (2017).
  • Measurement Error and Bias in Value‐Added Models, ETS Research Report Series, 10.1002/ets2.12153, 2017, 1, (1-12), (2017).
  • Simulation-Extrapolation with Latent Heteroskedastic Error Variance, Psychometrika, 10.1007/s11336-017-9556-y, 82, 3, (717-736), (2017).
  • Keeping Great Teachers: A Case Study on the Impact and Implementation of a Pilot Teacher Evaluation System, Educational Policy, 10.1177/0895904816637685, 32, 3, (363-394), (2016).
  • Principal holistic judgments and high-stakes evaluations of teachers, Educational Assessment, Evaluation and Accountability, 10.1007/s11092-016-9256-7, 29, 2, (155-178), (2016).
  • Regression methods for estimating cut scores, International Journal of Research & Method in Education, 10.1080/1743727X.2016.1167866, 40, 5, (497-510), (2016).
  • Estimating True Student Growth Percentile Distributions Using Latent Regression Multidimensional IRT Models, Educational and Psychological Measurement, 10.1177/0013164416659686, 77, 6, (917-944), (2016).
  • A Call for Mixed Methods in Evaluating Teacher Preparation Programs, Handbook of Research on Professional Development for Quality Teaching and Learning, 10.4018/978-1-5225-0204-3.ch026, (547-572), (2016).
  • Might the Tidal Wave Recede? Considering the Future of Student Growth Measures in Teacher Accountability, Student Growth Measures in Policy and Practice, 10.1057/978-1-137-53901-4, (261-283), (2016).
  • Different Growth Measures on Different Vertical Scales, Quantitative Psychology Research, 10.1007/978-3-319-38759-8_6, (67-78), (2016).
  • Value-Added Models (VAMs): Caveat Emptor, Statistics and Public Policy, 10.1080/2330443X.2016.1164641, 3, 1, (1-9), (2016).
  • Differential and long-term language impact on math, Language Testing, 10.1177/0265532215594641, 33, 4, (577-605), (2016).
  • Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations, Topics in Theoretical and Applied Statistics, 10.1007/978-3-319-27274-0, (15-24), (2016).
  • Education, Information Quality, 10.1002/9781118890622, (79-108), (2016).
  • Vertical Articulation of Cut Scores Across the Grades: Current Practices and Methodological Implications in the Light of the Next Generation of K–12 Assessments, ETS Research Report Series, 10.1002/ets2.12115, 2016, 2, (1-20), (2016).
  • An NCME Instructional Module on Data Mining Methods for Classification and Regression, Educational Measurement: Issues and Practice, 10.1111/emip.12115, 35, 3, (38-54), (2016).
  • Is School Value Added Indicative of Principal Quality?, Education Finance and Policy, 10.1162/EDFP_a_00184, 11, 3, (283-309), (2016).
  • Criterion-referenced and norm-referenced assessments: compatibility and complementarity, Assessment & Evaluation in Higher Education, 10.1080/02602938.2015.1022136, 41, 3, (450-465), (2015).
  • Patterns of Statewide Test Participation for Students With Significant Cognitive Disabilities, The Journal of Special Education, 10.1177/0022466915582213, 49, 4, (209-220), (2015).
  • THE EFFICIENCY IMPLICATIONS OF USING PROPORTIONAL EVALUATIONS TO SHAPE THE TEACHING WORKFORCE, Contemporary Economic Policy, 10.1111/coep.12107, 34, 1, (47-62), (2015).
  • Validating “value added” in the primary grades: one district’s attempts to increase fairness and inclusivity in its teacher evaluation system, Educational Assessment, Evaluation and Accountability, 10.1007/s11092-015-9234-5, 28, 2, (139-159), (2015).
  • The Role of Student Growth Percentiles in Monitoring Learning and Predicting Learning Outcomes, Educational Assessment, 10.1080/10627197.2015.1028621, 20, 2, (151-163), (2015).
  • Practical Differences Among Aggregate-Level Conditional Status Metrics, Journal of Educational and Behavioral Statistics, 10.3102/1076998614548485, 40, 1, (35-68), (2015).
  • Alternative Statistical Frameworks for Student Growth Percentile Estimation, Statistics and Public Policy, 10.1080/2330443X.2014.962718, 2, 1, (1-9), (2015).
  • A Comparison of Student Growth Percentile and Value-Added Models of Teacher Performance, Statistics and Public Policy, 10.1080/2330443X.2015.1034820, 2, 1, (1-11), (2015).
  • Using Learning Progressions to Design Vertical Scales that Support Coherent Inferences about Student Growth, Measurement: Interdisciplinary Research and Perspectives, 10.1080/15366367.2015.1042814, 13, 2, (75-99), (2015).
  • The Impact of Measurement Error on the Accuracy of Individual and Aggregate SGP, Educational Measurement: Issues and Practice, 10.1111/emip.12062, 34, 1, (15-21), (2015).
  • Using Test Scores From Students With Disabilities in Teacher Evaluation, Educational Measurement: Issues and Practice, 10.1111/emip.12076, 34, 3, (28-38), (2015).
  • Examining the Reliability of Student Growth Percentiles Using Multidimensional IRT, Educational Measurement: Issues and Practice, 10.1111/emip.12092, 34, 4, (21-30), (2015).
  • Complexities and Issues to Consider in the Evaluation of Content Teachers of English Language Learners, Urban Education, 10.1177/0042085914543111, 51, 2, (221-248), (2014).
  • Selecting Growth Measures for Use in School Evaluation Systems, Educational Policy, 10.1177/0895904814557593, 30, 3, (465-500), (2014).
  • Using school-level student achievement to engage in formative evaluation: comparative school-level rates of oral reading fluency growth conditioned by initial skill for second grade students, Reading and Writing, 10.1007/s11145-014-9512-5, 28, 1, (105-130), (2014).
  • New metrics, measures, and uses for fluency data: an introduction to a special issue on the assessment of reading fluency, Reading and Writing, 10.1007/s11145-014-9516-1, 28, 1, (1-7), (2014).
  • Descriptive Statistics for Modern Test Score Distributions, Educational and Psychological Measurement, 10.1177/0013164414548576, 75, 3, (365-388), (2014).
  • Implicit theories about intelligence and growth (personal best) goals: Exploring reciprocal relationships, British Journal of Educational Psychology, 10.1111/bjep.12038, 85, 2, (207-223), (2014).
  • Covariate Measurement Error Correction for Student Growth Percentiles Using the SIMEX Method, Educational Measurement: Issues and Practice, 10.1111/emip.12058, 34, 1, (4-14), (2014).
  • Formative Information Using Student Growth Percentiles for the Quantification of English Language Learners’ Progress in Language Acquisition, Applied Measurement in Education, 10.1080/08957347.2014.905784, 27, 3, (196-213), (2014).
  • Towards Optimal Education Including Self-Regulated Learning in Technology-Enhanced Preschools and Primary Schools, European Educational Research Journal, 10.2304/eerj.2014.13.5.529, 13, 5, (529-552), (2014).
  • Score Scales, Test Equating, Scaling, and Linking, 10.1007/978-1-4939-0317-7, (371-485), (2014).
  • Evaluating the Predictive Value of Growth Prediction Models, Educational Measurement: Issues and Practice, 10.1111/emip.12031, 33, 2, (5-13), (2014).
  • A SUMMARY OF MODELS AND STANDARDS‐BASED APPLICATIONS FOR GRADE‐TO‐GRADE GROWTH ON STATEWIDE ASSESSMENTS AND IMPLICATIONS FOR STUDENTS WITH DISABILITIES, ETS Research Report Series, 10.1002/j.2333-8504.2010.tb02221.x, 2010, 1, (i-31), (2014).
  • Does the Model Matter? Exploring the Relationship Between Different Student Achievement-Based Teacher Assessments, Statistics and Public Policy, 10.1080/2330443X.2013.856169, 1, 1, (28-39), (2013).
  • Special Education Teacher Evaluation, Assessment for Effective Intervention, 10.1177/1534508413513315, 39, 2, (71-82), (2013).
  • Introduction to AEI ’s Special Issue on Special Education Teacher Evaluations , Assessment for Effective Intervention, 10.1177/1534508413511489, 39, 2, (67-70), (2013).
  • The Sensitivity of Value-Added Estimates to Specification Adjustments: Evidence From School- and Teacher-Level Models in Missouri, Statistics and Public Policy, 10.1080/2330443X.2013.856152, 1, 1, (19-27), (2013).
  • Principles of quantile regression and an application, Language Testing, 10.1177/0265532213493623, 31, 1, (63-87), (2013).
  • The Consequences of Using one Assessment System to Pursue two Objectives, The Journal of Economic Education, 10.1080/00220485.2013.825112, 44, 4, (339-352), (2013).
  • A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects, Journal of Educational and Behavioral Statistics, 10.3102/1076998613494819, 38, 6, (577-603), (2013).
  • The Gains From Vertical Scaling, Journal of Educational and Behavioral Statistics, 10.3102/1076998613508317, 38, 6, (551-576), (2013).
  • Contrasting OLS and Quantile Regression Approaches to Student “Growth” Percentiles, Journal of Educational and Behavioral Statistics, 10.3102/1076998611435413, 38, 2, (190-215), (2013).
  • Science and Art of Setting Performance Standards and Cutoff Scores in Kinesiology, Research Quarterly for Exercise and Sport, 10.1080/02701367.2013.845517, 84, 4, (456-468), (2013).
  • Comparing Least-Squares Value-Added Analysis and Student Growth Percentile Analysis for Evaluating Student Progress and Estimating School Effects, SSRN Electronic Journal, 10.2139/ssrn.2230187, (2013).
  • Evaluating Growth for ELL Students: Implications for Accountability Policies, Educational Measurement: Issues and Practice, 10.1111/emip.12012, 32, 3, (11-26), (2013).
  • Assessing the Growth of Gifted Students, Gifted Child Quarterly, 10.1177/0016986212463873, 57, 1, (56-67), (2012).
  • Incorporating Teacher Effectiveness Into Teacher Preparation Program Evaluation, Journal of Teacher Education, 10.1177/0022487112454437, 63, 5, (335-355), (2012).
  • Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations, SSRN Electronic Journal, 10.2139/ssrn.2188884, (2012).
  • Relationships of Measurement Error and Prediction Error in Observed‐Score Regression, Journal of Educational Measurement, 10.1111/j.1745-3984.2012.00182.x, 49, 4, (380-398), (2012).
  • Measurement Error Adjustment Using the SIMEX Method: An Application to Student Growth Percentiles, Journal of Educational Measurement, 10.1111/j.1745-3984.2012.00186.x, 49, 4, (446-465), (2012).
  • Pay for Percentile, American Economic Review, 10.1257/aer.102.5.1805, 102, 5, (1805-1831), (2012).
  • Fuzzy Cognitive Map Based Student Progress Indicators, Advances in Web-Based Learning - ICWL 2011, 10.1007/978-3-642-25813-8_19, (174-187), (2011).
  • Physical Education and School Contextual Factors Relating to Students' Achievement and Cross-Grade Differences in Aerobic Fitness and Obesity, Research Quarterly for Exercise and Sport, 10.1080/02701367.2010.10599694, 81, sup3, (S53-S64), (2010).
  • Updating the Duplex Design for Test-Based Accountability in the Twenty-First Century, Measurement: Interdisciplinary Research & Perspective, 10.1080/15366367.2010.511976, 8, 2-3, (110-129), (2010).
  • Using Growth for Accountability, Educational Researcher, 10.3102/0013189X10383560, 39, 7, (537-544), (2010).
  • Discussion: With Choices Come Consequences, Educational Measurement: Issues and Practice, 10.1111/j.1745-3992.2009.00162.x, 28, 4, (52-55), (2009).

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.