placestats

Trend in average test score and Learning rate

Where Trend in average test score is higher, Learning rate tends to be higher. The link is moderate: +0.57 across 1,538 counties, on a scale from −1 (opposite) to +1 (in step). That figure compares places with the same population density, income and education, so those differences can't explain it. It holds inside states too. Measured separately within each of 30 states, the middle value of the link is +0.56.

Holds after adjusting

Every place, plotted

One dot for each place reporting both measures. Hover over or tap a dot to see its name and values. The thin line is the straight line that best fits the dots. The bold line, shown for counties, is the link after adjusting for density, income and education. A dot far from the lines is a place that does not follow the pattern.

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What each measure is

Trend in average test score

Whether each new group of students scores higher or lower than the group before, in grade levels per year.

Stanford Education Data Archive · SEDA cs_mn_tav_ol

Learning rate

How much children improve each school year, in grade levels, where 0 is the national average pace. This is the part that happens in school, and it behaves quite differently from the score level.

Stanford Education Data Archive · SEDA cs_mn_lrn_ol

Highest and lowest

Where each measure is highest and lowest among the places reporting both (2023), with the other measure beside it. This is shown for every size of place where both are published. Metro-area values are added up from their counties. A measure that cannot be added up across counties, such as a middle value (median), has no metro figure. Counts are shown per 1,000 residents, since raw totals would only list the largest places. Cities and towns and ZIP codes are shown where the Census survey or the CDC publishes them; most other sources stop at the county. They are shown for places of 5,000 residents or more, and only where the survey’s margin of error is small enough for the figure to be usable. The CDC publishes its health estimates for cities in a version adjusted for age, so places with older or younger people can be compared fairly, but not for ZIP codes.

Counties

By trend in average test score

Trend in average test scoreLearning rate
Highest
Alfalfa County, OK0.06270.0485
Perkins County, NE0.06140.0811
Dakota County, NE0.0610.0931
Chickasaw County, MS0.06010.0204
Gosper County, NE0.05570.0915
Lowest
Park County, CO-0.0794-0.0594
Graham County, KS-0.0773-0.0987
Lincoln County, AR-0.0727-0.0248
East Carroll Parish, LA-0.0658-0.0679
Henderson County, IL-0.054-0.0568

By learning rate

Trend in average test scoreLearning rate
Highest
Bethel Census Area, AK-0.01960.112
Sierra County, NM0.01840.0992
Kinney County, TX0.02560.0942
Juneau City and Borough, AK0.01790.094
Dakota County, NE0.0610.0931
Lowest
Richmond city, VA-0.0319-0.147
Greene County, AL-0.0273-0.13
Putnam County, IL-0.0294-0.12
Caswell County, NC-0.0237-0.115
Choctaw County, AL-0.0422-0.112
Metro areas

By trend in average test score

Trend in average test scoreLearning rate
Highest
Oxford, MS0.05310.0255
Clarksburg, WV0.0464-0.00873
Grenada, MS0.04610.0437
Laredo, TX0.04330.0237
Lincoln, NE0.0426-0.0208
Lowest
Macomb, IL-0.0483-0.0189
Huntsville, TX-0.0478-0.0158
Williston, ND-0.0474-0.0284
Russellville, AL-0.0432-0.0835
Rexburg, ID-0.0428-0.107

By learning rate

Trend in average test scoreLearning rate
Highest
Juneau, AK0.01790.094
Sioux City, IA-NE-SD0.01420.0931
Vermillion, SD0.01630.0837
Dyersburg, TN0.03940.0764
Crossville, TN0.02030.0711
Lowest
Rexburg, ID-0.0428-0.107
Arcadia, FL-0.029-0.0856
Russellville, AL-0.0432-0.0835
Sweetwater, TX-0.0155-0.0795
Bainbridge, GA-0.00727-0.0775

Counties furthest from the pattern

Compared with the straight line that best fits all 1,542 counties: the five with far more learning rate than their trend in average test score would suggest, and the five with far less.

Trend in average test scoreLearning rate
Above the line
Bethel Census Area, AK-0.01960.112
Yoakum County, TX-0.03110.0655
North Slope Borough, AK-0.04720.0381
Sierra County, NM0.01840.0992
Brown County, KS-0.02780.051
Below the line
Cameron County, PA0.0148-0.105
Hardee County, FL0.016-0.102
Hooker County, NE0.0288-0.0861
Richmond city, VA-0.0319-0.147
Dinwiddie County, VA0.0169-0.0891

Two series, one county

Both measures over time for a county you pick. The two charts are stacked, so they share only the years along the bottom. You can pick from the thirty largest counties reporting both. This panel covers 2010–2024, and a measure published only once shows as a single dot.

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Every version of the link

Each row measures the same link, from −1 to +1, in a different way. If the rows mostly agree, the link does not depend on one choice of method.

Before adjusting for anything+0.5591,538 counties reporting both
After adjusting for density, income and education+0.567the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.573size taken out as well as density
Also comparing only within each state+0.551each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.466the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 174 equally weighted counties
Middle value inside single states+0.562the link worked out inside each of 30 states with at least 20 counties reporting
Using ranks, after adjusting+0.553uses each county’s rank, not its value, so a few extreme counties cannot create the link

How much to believe it

Could it be chance?

The p-value is the chance of seeing a link this strong if there were really none. A value of 0.05 or less means the link is unlikely to be chance. Very small values are written in short form: 3.1e-16 means 0.00000000000000031, and the bigger the number after “e-”, the smaller the value. Treating every county as a separate case gives p = 1.9e-131. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 8.5e-19. The site tested 20,910 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 1.8e-130.

Do nearby places look alike?

Moran’s I measures how much nearby places look alike: 0 means no map pattern, and higher means neighbours are more similar. For the part of each county that the adjustment leaves unexplained, it is +0.30 (p = 0.001). That unexplained part still clusters on the map. So some regional factor shared by both measures is not accounted for.

Did they change together?

Not measurable: at least one of these is published for too few of the years a change needs (2014–2019 or 2019–2023), or for too few counties in both.

Association only. Nothing here shows that either measure changes the other. This describes counties, not the people in them. Two measures going together across places does not mean the same people have both. How the US county data works · Explore Trend in average test score and Learning rate