Method and variable documentation

1. Data sources

Skolkoll aggregates data from multiple open Swedish data sources. The table below summarises the main sources and their update frequency. A complete list of all Kolada KPIs and other data points is available on the data sources page.

2. Variable dictionary

The table below documents the key variables displayed on Skolkoll. Each variable is described with its unit, source and a brief explanation. A complete machine-readable data catalogue with all 80+ metrics is available in metric-definitions.json.

3. The SALSA method

What is SALSA?

SALSA stands for Skolverkets Arbetsverktyg för Lokala SambandsAnalyser (Skolverket's Tool for Local Correlation Analyses). It is a statistical model developed by Skolverket to put schools' results in relation to their pupil composition. The purpose is to give a fairer picture of schools' performance by controlling for background factors that the school itself cannot influence.

Model specification

SALSA is a multiple linear regression model that estimates expected merit value based on the following independent variables:

• Parents' education level — share of pupils whose parents lack upper-secondary education (%)
• Newly arrived pupils — share of pupils who immigrated to Sweden in the last four years (%)
• Gender — share of boys (%)

The model can be expressed as:

Merit value = β₀ + β₁ × (parents without upper-sec. ed. %) + β₂ × (newly arrived %) + β₃ × (share boys %) + ε

The coefficients (β values) are estimated by ordinary least squares on all compulsory schools with year 9 that report a sufficient pupil base.

The residual — what it means

The SALSA residual is the difference between the school's actual merit value and the model-predicted value:

Residual = Actual merit value − Expected merit value

• Positive residual — the school performs better than expected given its pupil composition. This may indicate effective teaching, good leadership or other success factors.
• Negative residual — the school performs below expectations. This may be due to resource shortages, high staff turnover or other challenges.
• Residual near zero — the school performs in line with what the model predicts.

On Skolkoll, a threshold of −15 points is used to flag schools with significant underperformance. Residuals below −25 points are marked with a red warning level.

Limitations

• SALSA applies only to compulsory school year 9 — there is no equivalent model for upper secondary or preschool.
• The model controls for a limited set of background factors. Factors such as residential segregation, mental health and the school's resource allocation are not captured.
• The residual is an average for the school — it says nothing about individual pupils' results.
• Small schools with few pupils get unstable residuals that can vary considerably between years.
• SALSA measures relative performance, not absolute quality. A school can have a positive residual but still have low merit values in absolute terms.

Reference: Skolverket's SALSA documentation

4. Data quality

All data shown on Skolkoll comes from official Swedish government agencies and open APIs. There are, however, important limitations to be aware of:

Confidentiality suppression

Skolverket does not publish statistics for school units where the pupil base is below 15 pupils for the relevant variable. This is to protect individual pupils' privacy. Affected variables are shown as "–" or are missing entirely on Skolkoll.

School survey response rate

Skolverket's school survey is based on voluntary participation. The response rate varies considerably between schools and year groups, which affects reliability. Results with low response rates should be interpreted with caution.

Preschools — GPS positions

Skolverket's API does not always contain coordinates for preschools. Skolkoll matches preschool addresses against SCB's geodata, Bolagsverket's address register and OpenStreetMap's Nominatim service. Approximately 85% of preschools have been matched with GPS positions; the rest are displayed without a map.

Municipality aggregation

Demographic data at DeSO level (Demographic Statistical Areas) is aggregated to municipality level. Absolute counts (population, employment, housing, etc.) are summed and shares are then calculated from the summed values. Metrics that are already averages or medians (e.g. economic standard) are population-weighted so that more populous areas have proportionally greater influence.

Grade data

Merit values in Skolverket's statistics refer to pupils who received grades in at least one subject. Pupils who received no grades in any subject (for example newly arrived pupils without a grading base) are not included in the average.

Time lag

Some data has a natural time lag. Grade data for a school year is typically published in the autumn of the same year. Kolada data can have up to six months' delay depending on the KPI. Update dates for each data source are shown on the data sources page.

Found an error in data or calculations? See our corrections policy for how to report it and how we handle corrections.

5. Citing Skolkoll

Data and analyses from Skolkoll may be freely cited with a source reference. Suggested citation format:

Skolkoll (2026). [Variable name]. Retrieved [date] from https://skolkoll.se/
Based on data from Skolverket, SCB and Kolada.

Example: Skolkoll (2026). Merit value year 9. Retrieved 2026-04-04 from https://skolkoll.se/en/school/example-school-12345678/. Based on data from Skolverket.

See also the versioning policy for information about archival, schema changes and licensing, and the method policy for how methodology is documented and changed.

6. Changelog

Important changes in data collection, calculation methods and variable definitions.