Assessing Need & Recognizing Peer Progress: An Additional Way to Understand Maryland School Ratings
Every year, the Maryland State Department of Education (MSDE) gives public schools a rating from 1 to 5 stars against fixed statewide benchmarks. That official rating is an essential tool for assessing where students and schools need the most support and resources. This project offers a complementary Peer Comparison Star Rating—not a replacement—that shows how schools perform compared to peers serving similar levels of student poverty, helping communities see both where resources are needed and where schools are excelling given their student demographics.
Answering Two Different Questions: Where Support Is Needed Most & How Schools Compare to Similar Peers
In Maryland's official Report Card system, every school is evaluated against the same fixed statewide thresholds: 75% of points for 5 stars, 60% for 4 stars, 45% for 3 stars, and 30% for 2 stars. Holding all schools to a common statewide benchmark is valuable for identifying where achievement gaps exist and directing resources and interventions to communities with the greatest need.
At the same time, students in high-poverty neighborhoods face real-life challenges outside of school—such as food insecurity, unstable housing, and fewer out-of-school academic resources. Because out-of-school poverty strongly affects test scores and attendance, schools serving high-poverty communities can achieve strong student growth while still falling short of a fixed statewide cutoff. In 2025–26, 60.1% of the difference in Elementary school scores (and 50.7% in High schools) was associated with a single factor: the percentage of students facing Economic Disadvantage (ED%).
Because fixed cutoffs reflect both school performance and neighborhood poverty, adding a complementary Peer Comparison Star Rating lets us answer a second vital question: How well is a school serving its students compared to other Maryland schools with similar demographics?
Interactive Simulator: Compare Standard & Peer Comparison Star Ratings
Live CalculatorMove the sliders below to see how a school's Economic Disadvantage (ED%) and Points Earned (%) affect its official state star and its peer comparison star:
How Adding a Peer Comparison Star Rating Supports Students, Families, and Educators
While MSDE's standard star rating helps state and district leaders target funding and academic support to high-need schools, providing a complementary Peer Comparison Star Rating alongside it offers vital perspective for students, families, and staff:
Celebrating Hard Work & Building School Pride
When students in a high-poverty school work hard every day, improve their reading and math skills, and overcome out-of-school barriers, they deserve to see their growth recognized alongside standard state benchmarks. Seeing their school's peer comparison star rating alongside state benchmarks builds student pride and motivation.
Seeing Both Resource Needs & "Hidden Gem" Schools
Families benefit from seeing both ratings side by side: the official MSDE rating shows where a school stands relative to statewide goals and where extra community investment is needed, while the Peer Comparison Star Rating highlights neighborhood schools where children are making exceptional progress compared to similar peers.
Supporting Educator Retention & Peer Learning
Teachers, principals, and support staff in high-need schools pour their energy into supporting students. Pairing MSDE's official rating with a Peer Comparison Star Rating recognizes effective instruction in high-need classrooms, reduces educator burnout, and helps districts identify peer exemplar schools to learn from.
What Critics Might Say: Tough Questions & Honest Answers
Every school rating system has trade-offs. The official Maryland Report Card scores schools from 1 to 5 stars using the same fixed target for every school. Adding a Peer Comparison Star Rating gives communities a fuller picture, but it also raises fair questions. Here are five common criticisms and how the data and research answer them:
Question 1
“Does this lower expectations for low-income kids?”
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“Does this lower expectations for low-income kids?”
“Giving 4 or 5 stars to a high-poverty school with lower test scores lowers the bar. Every child needs to learn how to read and do math at grade level, no matter how much money their family makes.”
Every child deserves high expectations, which is why this site never hides or replaces the official state star rating. However, an unadjusted score mostly reflects student family economic background rather than classroom instruction alone. Showing both ratings side by side lets families see where a school still needs to raise test scores and recognizes how well teachers are helping students grow.
Question 2
“Will higher stars take money and help away from struggling schools?”
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“Will higher stars take money and help away from struggling schools?”
“Low star ratings can bring extra funding and support to struggling schools. If a high-poverty school jumps from 2 stars to 4 stars on this site, state leaders might think the school is doing fine and stop sending help.”
Official school funding and state support are still tied to the state's original Report Card, so no school loses money because of this tool. At the same time, slapping a “1-star” or “2-star” label on a hard-working school can actually hurt kids. Studies show that low school ratings push wealthier families to move away, lower neighborhood home values, and increase school segregation. Low ratings can also drive away great teachers and principals. Firing staff or closing a low-rated school rarely helps low-income students unless they move to a much higher-performing school. Recognizing real progress helps keep strong teachers in the classrooms that need them most.
Question 3
“Is it fair that some wealthier schools lose a star?”
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“Is it fair that some wealthier schools lose a star?”
“To keep the total number of 1- to 5-star schools the same across Maryland, whenever one school gains a star, another school has to drop a star. Why should a school in a wealthier area drop from 4 stars to 3 stars when its test scores are still above average?”
Just as high-poverty schools deserve recognition when their teachers help students overcome steep barriers, well-resourced schools deserve genuine accountability. An unadjusted 5-star rating often reflects historical access to higher quality educational resources rather than exceptional school practice alone. When a low-poverty school receives 3 peer stars alongside its 4 or 5 state stars, it shows that students are achieving strong absolute outcomes, while highlighting an opportunity to push instruction even further alongside demographic peers.
Question 4
“Doesn't the state report card already count student growth?”
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“Doesn't the state report card already count student growth?”
“The official Maryland Report Card already gives points for student growth, not just raw test scores. If the state already measures how much kids improve from year to year, why adjust the stars again for poverty?”
While the state report card includes student growth in elementary and middle schools, it mixes those points with raw test scores, attendance, and other measures (and high schools do not include student growth percentiles at all). Because out-of-school poverty affects those other areas so heavily, more than half of a school's final score is still tied to family income. Adding a Peer Comparison rating makes sure a school's hard-earned growth does not get buried by neighborhood poverty.
Question 5
“Is family income the only thing that makes schools different?”
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“Is family income the only thing that makes schools different?”
“Maryland only counts students as low-income if their families are enrolled in programs like food stamps (SNAP), cash assistance (TANF), foster care, or homelessness services. That misses many working-poor and immigrant families, and it ignores schools serving many English learners or students with disabilities.”
Looking only at food assistance records does not tell the whole story of a school community. That is why this site lets you include more than just poverty. In the sidebar menu on the chart and table tabs, you can switch to Model 3 (Full Student Background) or Model 4 (Custom Weight Simulator) to include students with disabilities and English learners in the comparison.
What Happens When We Adjust Maryland's Star Ratings Across 1,292 Schools?
High-poverty schools gain nearly a full star on average (+0.74★) across 316 schools, while the statewide average remains virtually identical.
Among Maryland's 316 high-poverty schools, 5-star ratings jump from just 5 schools to 46 schools for beating expectations.
More than half of high-poverty schools (56.0%) earn 4 or 5 stars (up from 18.0%) when compared against similar peer schools.
Three-quarters of 1★ and 2★ high-poverty schools move up, with 69.6% of high-poverty schools gaining at least 1 star.
Before vs. After: Star Ratings for High-Poverty Schools (≥60% Low-Income, n=316 Schools)
Compare how many schools earn 1★, 2★, 3★, 4★, and 5★ under both ratings (Use buttons above to switch cohorts):
What This Means for School Districts Across Maryland (Average Star Shift)
Districts serving higher-poverty communities see how their schools perform compared to similar schools:
Spotlight Schools: Outperforming Similar Schools Across Maryland
When we compare schools against peers serving similar student populations, communities across Maryland—such as SEED School (+1.50★ average gain), Kent County (+0.80★), Baltimore City (+0.68★), Somerset County (+0.62★), Allegany County (+0.55★), Wicomico County (+0.50★), Baltimore County (+0.48★), and Worcester County (+0.46★)—see dozens of hard-working schools recognized as 4-star and 5-star peer leaders. Click any school below to inspect both ratings on its full diagnostic card:
Explore the Interactive Maryland Report Card Dashboard
Choose any section below to open interactive charts, district summaries, school lookups, and guides:
Official State Star Ratings (Resource Need Perspective)
Fixed thresholds at 30%, 45%, 60%, and 75% of possible points (Click any dot to inspect school)
Peer Comparison Star Ratings (Compared to Similar Schools)
Color-coded by peer comparison star rating with peer cutoff lines (Click any dot to inspect school)
Star Rating Changes (State Star vs. Peer Comparison Star)
Rows show Official State Stars; columns show Peer Comparison Stars under the selected model:
| State ↓ \ Peer Comparison → | 1★ | 2★ | 3★ | 4★ | 5★ | Total Orig |
|---|
Highlighted High-Poverty Exemplar Schools
Click any exemplar school below to open its full Accountability Diagnostic Card:
Accountability Indicator Poverty Gradients (BERC 2019 Framework)
Testing which components of Maryland's ESSA model measure out-of-school poverty versus in-school educator impact
A foundational finding of Wohn & Boberiene (2019) is that Maryland's accountability indicators do not relate equally to student poverty. Standardized test proficiency (Academic Achievement) tracks neighborhood socioeconomic status with extraordinary severity (R² ≈ 70% in Elementary), reflecting out-of-school family wealth. Conversely, annual student gains (Academic Progress / SGPs) have a much flatter gradient (R² ≈ 27%), proving that high growth is achieved in high-poverty schools every year. In addition, student attendance (Chronic Absenteeism) heavily tracks poverty (R² ≈ 55%), penalizing schools for community healthcare, housing, and transportation barriers.
Complete Indicator-by-Indicator Poverty Gradient Reference Table
Linear regression of each accountability indicator on Economic Disadvantage (%) for the currently filtered cohort (Click any school in the plots above to inspect)
| Report Card Indicator / Metric | Bands | Reporting (n) | Mean Score | Pearson r | R² (ED%) | Slope (Δ per 10% ED) | Fitted OLS Equation | Accountability / Equity Takeaway |
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District Star Rating Changes (Peer Comparison Star minus State Star)
Comparing district-level impact across Maryland school districts (24 LEAs; SEED School included when specialized programs are toggled on)
Maryland School Districts Summary (Click any district to view its schools)
Click any column header or sort menu to reorder| LEA Name | Schools (n) | Mean ED% | Mean Pts% | Avg State Star | Avg Peer Comparison Star | 2026 Shift | 5★ (State → Peer) | 1–2★ (State → Peer) |
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Maryland School Performance Directory
Showing 1–50 of 1,292 schools (Click any column header to sort • Click any school row for full diagnostic card)
| District | School Name (ID) | Level | State Score (%) | Peer Comparison Score (%) | Poverty (ED%) | Special Ed (SWD%) | English Learner (ML%) | State Star | Peer Comparison Star | Star Shift | Vs. Peers (+/-) |
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User Guide: Exploring Maryland's Equitable Report Card System
Step-by-step instructions for navigating the public-facing Data Storyboard, comparing adjustment models, filtering districts, grade bands, and poverty ranges, and inspecting individual school diagnostic cards.
Opening Tab: Data Storyboard (Public-Facing Landing Page & Launchpad)
When opened, the application loads directly to the full-width ★ Data Storyboard—designed as an accessible, public-facing title and overview page (written at a middle-school reading level without the technical sidebar or KPI header cards) that positions Peer Comparison Star Ratings as a complementary perspective alongside MSDE's official star ratings:
Choose a Comparison Model (Left Sidebar)
On any analytical tab (Tabs 1–5), use the Choose Comparison Model dropdown in the left sidebar to switch between six accountability models in real time. Every model dynamically updates both the Peer Comparison Star Rating (1★–5★) and the Peer Comparison Score Percentage:
- 1. Peer Comparison Model (2019 BERC • Recommended): Compares each school against peers with similar student poverty rates, setting peer comparison star cutoffs that preserve exact statewide star totals.
- 2A. Straight-Line Peer Comparison (OLS Residuals): Draws one statewide trend line and ranks schools by how many points they score above or below similar schools. (93% match with Model 1).
- 2B. Standardized Bell-Curve Score: Rescales scores to prevent bunching in the middle, then applies standard 30/45/60/75% cutoffs.
- 2C. Curved Trend Line (Monotone Spline): Uses a smooth curve that bends to account for extreme poverty challenges (above 80% ED) without ever curving upward.
- 3. Full Student Background: Simultaneously accounts for economic disadvantage, students with disabilities, and multilingual learners.
- 4. Interactive Custom Weight Simulator: Unlocks three sliders so you can test custom policy weights on economic disadvantage, students with disabilities, and multilingual learners.
Filter by School Level, District, & Poverty Range
Every chart, KPI card, transition matrix, and lookup table updates immediately when you adjust the cohort filters in the left sidebar:
- Exclude Special Ed, Alternative & SEED Schools (Default On): Compares traditional public schools (1,292 schools). Uncheck at any time to include all specialized centers, alternative programs, and the SEED School (1,311 schools).
- School Level Buttons: Toggle between All Schools (1,292), Elementary (760), Middle (216), High (193), or Combined (123). Regressions are performed within each grade band to maintain statistical accuracy, and multi-band schools (E/M, M/H, E/M/H) are combined proportionally based on total possible points so each school appears once.
- District (LEA) & Quick Spotlights: Filter to any of Maryland's 24 school districts (or the SEED School when specialized schools are toggled on), or use the quick buttons for ★ Spotlight: Baltimore City, High Poverty (≥60% ED), or Low Poverty (≤20% ED).
- Poverty Range Sliders: Drag the Min ED% and Max ED% sliders to isolate any specific student poverty band.
- School Search: Type any school name or 4-digit MSDE ID to instantly find specific schools across all views.
Explore the Three Analytical Views (Tabs 1–3)
- Tab 1 — Interactive Chart (Two Modes):
- Mode 1 — Star Ratings (State vs. Peer Comparison): Side-by-side comparison of official state horizontal thresholds on the left versus peer comparison thresholds on the right, plus the Star Rating Changes Matrix and High-Poverty Exemplar Schools.
- Mode 2 — What Drives Scores? (Test Scores vs. Student Growth): Visually compare how test scores (R² ≈ 70%) track out-of-school family poverty, while annual academic progress (R² ≈ 27%) reflects classroom teaching.
- Tab 2 — School Districts: Bar chart and complete table comparing district-level star ratings, 5★ counts, and shifts across Maryland's 24 school districts. Tip: Click any district row to view its schools in Tab 3!
- Tab 3 — School Directory: Searchable, sortable directory of all Maryland schools featuring State Score (%), Peer Comparison Score (%), State Star, Peer Comparison Star, and Vs. Peers (+/-). Click ↓ Export Filtered CSV to download your filtered view to Excel.
Inspect Individual School Diagnostic Cards
Click any school dot on either scatter plot, any exemplar school card on the Storyboard or Tab 1, or any row in the Tab 3 School Lookup table to open that school's pop-up Accountability Diagnostic Card:
- School Demographics & Student Context: Displays school type and student subgroup context including ED%, SWD%, and ML%.
- Side-by-Side Model Comparison (Stars + Est. % Pts): Displays both the Star Rating (1★–5★) and the Model-Specific Estimated % Points across all six specifications simultaneously (Original MSDE, BERC QR, OLS Rank, OLS Standardized, Monotone I-Spline, Multivariable, and Custom Weight).
- "Nearest Demographic Peers" Comparison Table: Automatically identifies the 6 Maryland schools serving the most similar student poverty rate (ED%, breaking ties by SWD% and ML%) and ranks them by Peer Comparison Points % alongside the selected school. Click any peer row to jump directly to its diagnostic card.
- Combined Multi-Band School Rollup (ΣEarned / ΣPossible): For any of Maryland's 124 traditional multi-band schools (136 when non-traditional schools are included) (E/M, M/H, E/M/H), automatically displays a complete grade-band breakdown table showing how each individual grade level's points sum together to determine the overall school-wide percentage and star rating.
- School-Specific Quantile Cutoffs, Full Indicator Breakdown & Printable / Shareable Reports: Displays the exact points percentage required for 2★, 3★, 4★, and 5★ at that school's ED% alongside full, unabbreviated MSDE accountability indicators and every individual metric, plus header buttons to Copy Direct Link (#school=LEA_SCH_SPAN) or Print / Save PDF as a clean report.
How the Math Works: Adjusting Maryland School Ratings
You don't need a graduate degree in statistics to understand how we adjust school ratings. If you've taken high school Algebra or Intro to Statistics, you already know the core concepts: scatter plots, lines of best fit, and residuals. Here is how the entire system works from start to finish.
The 6 Core Statistics Concepts
Concept 1: The Trend Line (y = mx + b)
Just like in high school Algebra, we draw a line of best fit across all Maryland schools:
- x-axis: Percentage of low-income students (0% to 100%).
- y-axis: Points earned on the state report card (0% to 100%).
- Slope (m = −0.39): For every 10% increase in student poverty, a school's score drops by about 4 points on average across Maryland.
Concept 2: What is R²? (Statistical Association)
In statistics, R² (R-squared) measures how much of the variation in school accountability scores is statistically associated with student household economic disadvantage:
What this means: Over 60% of the difference in elementary school scores is statistically associated with student household income. As national research by Reardon (2011, 2019) demonstrates, unadjusted status test scores heavily reflect accumulated family resources, neighborhood socioeconomic conditions, and opportunity gaps already present before kindergarten, rather than school practices or instructional quality alone.
Concept 3: What is a "Residual"? (Peer-Adjusted Value-Added)
A residual is simply the vertical distance between where a school scored and what the trend line predicted:
- Dot above the line (+): The school is beating expectations compared to similar peers!
- Dot below the line (−): The school has room to improve compared to similar peers.
Why this matters: Econometric evaluations (Ehlert et al., 2014; Guarino et al., 2015; Parsons, Koedel, & Tan, 2019) demonstrate that evaluating schools relative to demographic peers isolates school-contributed value-added, providing a fairer, far more accurate measure of school quality than unadjusted proficiency or unadjusted Student Growth Percentiles (SGPs).
Concept 4: No Grade Inflation!
We don't hand out extra stars. We set adjusted cutoffs so that the statewide number of stars stays 100% identical:
Maryland gives 5★ to the top ~10% of schools and 4★ to the next ~40%. By matching those exact percentiles, stars simply shift to schools that outperform peers.
Concept 5: The 0–100% Score (Mapping)
To make numbers easy to read, we convert each school's peer standing onto Maryland's familiar 15-point star scale:
Just like converting Celsius to Fahrenheit, this doesn't change a school's rank—it translates their standing into intuitive, familiar percentages.
Concept 6: Combined Schools (K–8 / 6–12)
What about schools that serve both Elementary and Middle grades?
Just like calculating your GPA (where a 4-credit course counts more than a 2-credit course), each grade band is evaluated against its own peers first, then combined proportionally based on total points.
Step-by-Step Walkthrough: Harlem Park Elementary/Middle
Let's see how the math works for a real Maryland school: the Elementary span of Harlem Park Elementary/Middle in Baltimore City:
The 6 Comparison Models (At a Glance)
In the sidebar dropdown on the analytical tabs, you can test six different statistical specifications. Here is what each one does:
How it works: Solves empirical quantile check-loss functions to draw custom conditional cutoff lines for each star level. Grounded in the empirical quantile threshold framework of Wohn & Boberiene (2019) and the proportional accountability standards of Ehlert et al. (2014), this ensures schools serving every poverty level have an equal, proportional opportunity to earn 1 to 5 stars.
How it works: Draws one single straight trend line through all schools, calculates each school's distance from the line, and ranks schools by how far they score above or below similar schools. (93.8% identical to Model 1).
How it works: Prevents scores from bunching in the middle by standardizing the spread of scores before applying standard 30/45/60/75% cutoffs.
How it works: Uses a flexible curved line that bends to account for the unique challenges of extreme concentrated poverty (over 80% low-income), while ensuring the line never goes up.
How it works: Adjusts simultaneously for Economic Disadvantage (ED%), Students with Disabilities (SWD%), and Multilingual Learners (ML%). Empirically justified because Direct Certification undercounts poverty among working-poor and immigrant families (Koedel & Parsons, 2021; Domina et al., 2018), English learners experience structural reclassification turnover (Saunders & Marcelletti, 2013), and students with disabilities encounter distinct baseline achievement disparities (Schwartz, Hopkins, & Stiefel, 2021).
How it works: Unlocks three interactive sliders in the sidebar so you can choose your own adjustment strength for economic disadvantage, students with disabilities, and multilingual learners.
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4. Advanced Mathematical Specifications & Technical Deep Dives
▼ Click to Expand
For researchers, econometricians, and policymakers requiring exact mathematical formulations, piecewise-linear mapping equations, and multi-band rollup proofs, the complete technical documentation is provided below:
A. Bivariate Ordinary Least Squares (OLS) Formulation
- Yi : Observed unadjusted Total Points % (Earned / Possible × 100).
- Ŷi : Expected Points % for school i given student poverty EDi%.
- ei = Yi − Ŷi : Peer comparison residual measuring performance above/below statewide expectation for similar schools.
B. Quantile Regression Check-Loss Formulation (Model 1)
Evaluated at empirical cumulative proportions τ ∈ {τ1,b, τ2,b, τ3,b, τ4,b} matching MSDE's 1★, 2★, 3★, 4★, and 5★ statewide distribution. Grounded in the empirical quantile threshold framework formulated by Wohn & Boberiene (2019) and the proportional school accountability principles established by Ehlert et al. (2014), this approach guarantees proportional accountability across the entire socioeconomic continuum while allowing heteroskedastic boundary slopes to adapt naturally to differing variance across poverty bands.
C. Monotone Decreasing I-Spline (Model 2C) & Multivariable OLS (Model 3)
Empirical Justifications for Model 3 Demographic Covariates:
- Direct Certification Limitations (ED%): Maryland's state report card relies on Direct Certification (SNAP/TANF/foster/homeless participation), which systematically undercounts low-income students in working-poor and immigrant households who do not participate in public assistance programs (Koedel & Parsons, 2021; Domina et al., 2018).
- English Learner Reclassification Dynamics (ML%): Multilingual learners face the structural Catch-22 of reclassification: as soon as students attain English proficiency, they exit the subgroup while incoming immigrants enter, depressing cross-sectional subgroup status scores regardless of school quality (Saunders & Marcelletti, 2013).
- Students with Disabilities Baseline Disparities (SWD%): Students receiving special education services face substantial, persistent structural baseline score disparities on standardized assessments that are not fully captured by income measures alone (Schwartz, Hopkins, & Stiefel, 2021).
D. Piecewise-Linear Mapping Formula onto MSDE's 0–100% Scale
Under MSDE's unadjusted system, star cutoffs are spaced exactly 15 percentage points apart (30%, 45%, 60%, 75%). To translate each model's metric into an intuitive Estimated Adjusted Points Percentage (Adj_Pct) where 30%, 45%, 60%, and 75% retain identical meaning across all models, we apply monotonic piecewise-linear interpolation:
E. Combined Grade Band Rollup (COMB, N = 1,292 Traditional / 1,311 Full): Mathematical Formulation
In Maryland's traditional cohort, 123 multi-band schools serve 2 or 3 grade levels inside the same facility (104 E/M, 14 M/H, 5 E/M/H). Because Elementary, Middle, and High schools have different indicator mixes, points possible, and baseline distributions, regressions are always estimated within Elementary, Middle, and High cohorts first, and then combined into whole-school ratings weighted by MSDE Possible Points (Possibleb):
1. Unadjusted Combined Score
Sums earned and possible points directly, then compares to standard 30/45/60/75% cutoffs.
2. Threshold Point Summation
Sums raw required cutoff points across bands first, then maps once to MSDE scale.
3. Possible-Points-Weighted Adj %
Maps each band to MSDE scale first, then computes weighted average of adjusted points.
- Elementary: 42.0/90 (46.6%, 3★ unadj), ED = 93.2% → Adj 4★ (66.6%)
- Middle: 51.9/90 (57.7%, 3★ unadj), ED = 90.4% → Adj 5★ (75.0%)
- Elementary (52.6% wt): 38.9/100 (38.9%, 2★ unadj), ED = 83.0% → Adj 3★ (51.0%)
- Middle (47.4% wt): 46.1/90 (51.2%, 3★ unadj), ED = 86.3% → Adj 4★ (66.9%)
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5. Complete 2025–26 Grade-Band Parameter & Quantile Cutoff Reference Table
▼ Click to Expand
Parameters below reflect the default cohort excluding Special Education Centers, Alternative Education Schools, and the SEED School (N = 1,420 spans across 1,292 schools). When the sidebar toggle is unchecked (N = 1,457 spans across 1,311 schools), the application automatically switches to the full-cohort parameter table (BAND_META_ALL).
| Parameter / Threshold Equation | Elementary Band (E, n = 869) | Middle Band (M, n = 339) | High School Band (H, n = 212) |
|---|---|---|---|
| Unadjusted Star Proportions (1★ / 2★ / 3★ / 4★ / 5★) | 0.1% / 7.6% / 33.3% / 46.6% / 12.4% | 1.2% / 19.1% / 48.5% / 28.2% / 2.9% | 0.0% / 17.8% / 41.8% / 30.0% / 10.3% |
| Cumulative Quantile Cutoffs (τ1, τ2, τ3, τ4) | 0.0012, 0.0771, 0.4097, 0.8757 | 0.0118, 0.2029, 0.6882, 0.9706 | 0.0000*, 0.1784, 0.5962, 0.8967 |
| Bivariate OLS Equation (Points% ~ ED%) | 79.99 − 0.3921×ED% (R²=60.1%) | 70.35 − 0.3412×ED% (R²=44.7%) | 76.73 − 0.4408×ED% (R²=50.7%) |
| QR 4★/5★ Cutoff Line (Q̂τ4) | 85.30 − 0.3310×ED% | 82.76 − 0.2774×ED% | 84.69 − 0.4085×ED% |
| QR 3★/4★ Cutoff Line (Q̂τ3) | 79.54 − 0.4196×ED% | 73.46 − 0.3319×ED% | 77.86 − 0.4395×ED% |
| QR 2★/3★ Cutoff Line (Q̂τ2) | 73.46 − 0.4628×ED% | 64.52 − 0.3603×ED% | 74.01 − 0.5473×ED% |
| QR 1★/2★ Cutoff Line (Q̂τ1) | 55.04 − 0.3270×ED% | 55.81 − 0.3859×ED% | 51.03 − 0.4408×ED%* |
| Monotone I-Spline Knot Ŷ (0/20/40/60/80/100% ED) | 82.3 / 72.1 / 63.0 / 57.1 / 50.4 / 28.2 (R²=61.3%) | 76.5 / 64.4 / 55.1 / 48.6 / 44.5 / 44.5 (R²=47.1%) | 81.2 / 69.6 / 56.4 / 50.0 / 44.1 / 44.1 (R²=53.5%) |
| Monotone I-Spline Slopes (per 20% ED bracket) | −0.51, −0.46, −0.29, −0.33, −1.11 | −0.60, −0.46, −0.32, −0.21, 0.00 | −0.58, −0.66, −0.32, −0.30, 0.00 |
| Multivariable OLS (ED%, SWD%, ML%) | 80.12 − 0.387·ED − 0.000·SWD − 0.022·ML | 74.57 − 0.279·ED − 0.341·SWD − 0.211·ML | 82.58 − 0.286·ED − 0.753·SWD − 0.349·ML |
6. Citations, Public Data Sources, & Author Credits
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Foundational BERC Research Brief (June 2019):
Wohn, C., & Boberiene, L. (2019). Looking at School Performance From a Different Perspective: A Maryland Report Card Reflecting Students Served. Baltimore Education Research Consortium (BERC). Available at baltimore-berc.org (Full PDF Brief). -
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Official Maryland State Department of Education (MSDE) Primary Datasets:
Maryland State Department of Education (2026). Maryland Report Card Public Accountability & Demographic Data Releases. The quantitative models, school-level indicators, and demographic characteristics in this explorer are calculated directly from two official primary MSDE data downloads:Accountability Detail Dataset: MSDE Data Download File #608 (Official school-level accountability points, indicators, academic achievement, student growth, English proficiency, and state star ratings).Special Services & Demographics Dataset: MSDE Data Download File #593 (Official school-level student population characteristics: Economically Disadvantaged, FARMS, Students with Disabilities, Multilingual Learners, and Title I status).Official MSDE Report Card Portal: reportcard.msde.maryland.gov -
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Supporting Literature Referenced in the BERC Framework:
Berner, A., & Steiner, D. (2019). Quality and schools: Managing school closings to optimize student outcomes (BERC); Hasan, S., & Kumar, A. (2018). Digitization and divergence: Online school ratings and segregation in America; Reardon, S. F. (2011). The widening academic achievement gap between the rich and poor; Salmon, K. B. (2018). A Message from the State Superintendent of Schools (MSDE). -
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AI Coding & Web Implementation Assistance:
Google. (2026). Gemini [Large language model]. https://gemini.google.com/
Conceptual framework, statistical modeling, and analysis by Christopher Wohn, building on the June 2019 Baltimore Education Research Consortium (BERC) study co-authored with Liepa Boberiene, Ph.D. Front-end HTML/JavaScript coding, interactive visualization engineering, and data pipeline implementation assisted by Google Gemini (Google, 2026).
Suggested Citation for This Website & Models
Wohn, C. (2026). Similar School Comparisons in Maryland: A New Perspective on the Maryland Report Card [Interactive web application and demographic regression models]. https://similarschoolsmd.org/
@misc{wohn2026similarschools,
author = {Wohn, Christopher},
title = {Similar School Comparisons in Maryland: A New Perspective on the Maryland Report Card},
year = {2026},
howpublished = {Interactive web application and demographic regression models},
url = {https://similarschoolsmd.org/}
}
Academic References Supporting Peer Comparison Accountability Models
- Berner, A., & Steiner, D. (2019). Quality and schools: Managing school closings to optimize student outcomes. Baltimore Education Research Consortium (BERC) & Johns Hopkins Institute for Education Policy. (Shows that closing low-rated schools rarely benefits low-income students unless they are redirected to much higher-performing schools.)
- Clotfelter, C. T., Ladd, H. F., Vigdor, J. L., & Diaz, R. A. (2004). Do school accountability systems make it more difficult for low-performing schools to attract and retain high-quality teachers? Journal of Policy Analysis and Management, 23(2), 251–271. (Demonstrates that labeling high-poverty schools as low-performing accelerates teacher turnover and increases the share of novice teachers.)
- Domina, T., Pharris-Ciurej, N., Penner, A. M., Penner, E. K., Brummet, Q., Porter, S. R., & Sanabria, T. (2018). Is free and reduced-price lunch a valid measure of educational disadvantage? Educational Researcher, 47(9), 539–555. (Evaluates how Direct Certification compares against IRS tax and Census records across diverse student populations.)
- Ehlert, M., Koedel, C., Parsons, E., & Podgursky, M. (2014). Selecting growth measures for use in school evaluation systems: Should proportionality matter? Educational Policy, 30(3), 465–500 (see also Education Next, 14(2), 66–71). (Shows that proportional peer-comparison models controlling for student disadvantage provide fairer, more accurate signals of instructional quality.)
- Fazlul, I., Koedel, C., & Parsons, E. (2023). Free and reduced-price meal eligibility does not measure student poverty: Evidence and policy significance. Economics of Education Review, 94, 102391. (Demonstrates how universal free-meal programs under the Community Eligibility Provision significantly degrade meal eligibility as an indicator of actual household income.)
- Koedel, C., & Parsons, E. (2021). The effect of the Community Eligibility Provision on the ability of free and reduced-price meal data to identify disadvantaged students. Educational Evaluation and Policy Analysis, 43(1), 3–31. (Documents how Direct Certification undercounts working-poor and immigrant families who are not enrolled in SNAP.)
- Feng, L., Figlio, D. N., & Sass, T. R. (2018). School accountability and teacher mobility. Journal of Urban Economics, 103, 1–17. (Finds that schools receiving bottom accountability grades experience a sharp departure of their most effective, highest value-added teachers.)
- Figlio, D. N., & Lucas, M. E. (2004). What's in a grade? School report cards and the housing market. American Economic Review, 94(3), 591–604. (Establishes that arbitrary school report card cutoffs trigger immediate disparities in neighborhood housing prices and family sorting.)
- Guarino, C. M., Reckase, M. D., Stacy, B. W., & Wooldridge, J. M. (2015). A comparison of student growth percentile and value-added models of teacher performance. Statistics and Public Policy, 2(1), 1–11. (Proves that Student Growth Percentiles omit demographic context and systematically underrate educators serving disadvantaged students.)
- Hasan, S., & Kumar, A. (2024). Who captures the value from organizational ratings?: Evidence from public schools. Strategy Science, 9(3), 248–266. (Shows that public school ratings accelerated affluent flight, widened housing value gaps, and increased racial and socioeconomic segregation across communities.)
- Parsons, E., Koedel, C., & Tan, L. (2019). Accounting for student disadvantage in value-added models. Journal of Educational and Behavioral Statistics, 44(2), 144–179. (Demonstrates that school-level aggregations of growth metrics retain substantial correlation with school poverty unless explicitly adjusted.)
- Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In G. J. Duncan & R. J. Murnane (Eds.), Whither Opportunity? (pp. 91–116). Russell Sage Foundation. (Foundational study documenting that the academic achievement gap between high- and low-income children grew by 30–40% over recent decades and is already present before kindergarten.)
- Reardon, S. F. (2019). Educational opportunity in early and middle childhood: Using full population administrative data to study variation by place and age. RSF: The Russell Sage Foundation Journal of the Social Sciences, 5(2), 40–68. (Stanford Education Data Archive study showing that status test scores reflect pre-kindergarten family wealth, whereas year-to-year school learning rates are far less tied to poverty and reveal many affluent schools with average or slow growth.)
- Maryland State Department of Education. (2026). 2025–26 Maryland Report Card: Accountability detail dataset [Data file #608]. MSDE Data Downloads. https://reportcard.msde.maryland.gov/DataDownloads/FileDownload/608. (Primary state administrative file containing official school-level measure earned points, possible points, academic achievement proficiency, student growth percentiles, attendance, graduation rates, and state star ratings.)
- Maryland State Department of Education. (2026). 2025–26 Maryland Report Card: Special services and student demographics dataset [Data file #593]. MSDE Data Downloads. https://reportcard.msde.maryland.gov/DataDownloads/FileDownload/593. (Primary state administrative file containing official school-level student population characteristics, including Economically Disadvantaged, FARMS, Students with Disabilities, Multilingual Learners, and Title I status.)
- Saunders, W. M., & Marcelletti, D. J. (2013). The gap that can't go away: The Catch-22 of reclassification in monitoring the progress of English learners. Educational Evaluation and Policy Analysis, 35(2), 139–156. (Explains why schools serving English Learners face a structural scoring penalty as proficient students exit the subgroup and newcomers enter.)
- Schwartz, A. E., Hopkins, B. G., & Stiefel, L. (2021). The effects of special education on the academic performance of students with learning disabilities. Journal of Policy Analysis and Management, 40(2), 480–520. (Documents the substantial baseline test score disparities faced by students with learning disabilities and the academic benefits of receiving specialized learning services.)