#  Andrew Ho (Harvard) 

 



####  calendar\_today Date and Time 

 **February 11, 2026** 

 12:00PM - 01:30PM EST 

####  pin\_drop Location 

 **CGIS Knafel Building, Room K354**  



 

 [ Join via Zoom arrow\_circle\_right ](https://harvard.zoom.us/j/93110218231?pwd=Gmka2cTdUty8AcWec90hWmcSllXtkP.1) 

 



 

### Title

Two Watches: Measurement Error Models for Estimating Educational Progress from Discrepant Test Score Trends

### Abstract

Understanding large-scale educational progress often requires reconciling information from multiple testing programs that differ in their purpose, precision, and periodicity. Like two watches that disagree about the time, two tests may report different trends for the same populations, subjects, and time periods. We develop a precision-adjusted multilevel measurement error model of the relationship between score trends from the National Assessment of Educational Progress (NAEP) and state testing programs in the United States. The model jointly estimates the true variance in NAEP trends, the true variance in state trends, their respective reliabilities, their true correlation, and systematic bias in state trends. We find that NAEP trends have reliabilities around 0.5, whereas state test trends have reliabilities near unity due to census testing. The true correlation between NAEP and state test trends is around 0.6, and state tests show upward bias around 0.04 standard deviation units per two-year period from 2009 to 2024. This modeling framework accounts for both measurement error and negative serial correlation in consecutive trends, enabling optimal estimation of past trends and prediction of future trends that neither test alone provides.



 

 



 

 

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