Equalities analysis interactive report

Ofqual routinely analyses how attainment gaps for students with different characteristics (some of which are protected) and different socio-economic status vary over time. This interactive report presents the results of the analysis for the period 2018 to 2025 for A levels (referred to here as General Qualifications, GQ) and a subset of level 3 Vocational and Technical Qualifications (VTQ). We are unable to report on GCSEs and level 1, 1/2 and 2 VTQs as prior attainment data for students taking these qualifications is not available, but results of the equalities analyses for these qualifications carried out in 2024 are still accessible in this interactive report.

We compare results in 2025 with results in 2024. Grading continued as normal in summer 2025 and summer 2024 following the return to pre-pandemic standards in summer 2023.

Executive summary

Ofqual routinely carries out equalities analysis for qualification results. This report presents an analysis of how attainment gaps for students with different characteristics (some of which are protected) and socio-economic status have changed in 2025 compared to previous years. A difference between this year’s report compared to those published previously is that the analysis pertains only to A levels (referred to here as general qualifications (GQ)), and Applied Generals and Tech Levels qualifications (referred to collectively here as vocational and technical qualifications (VTQ)).

Analysis of level 1, level 1/2, and level 2 qualifications (including GCSEs) is not included this year because, for students taking these qualifications, crucial information on their prior attainment at Key Stage 2 is not available following the cancellation of Key Stage 2 assessments in 2020 and 2021 due to the COVID-19 pandemic. A key feature of Ofqual’s equalities analysis is the use of a multivariate analytical approach. This allows us to explore the impact on overall results of each student characteristic (for example, gender, or ethnicity) separately while controlling for other characteristics, that is, holding other characteristics constant. Prior attainment is key to this analysis as it is the strongest predictor of qualification results, and its inclusion allows for more accurate modelling of the impact of other student characteristics. Without prior attainment data in our analysis, differences would likely be wrongly attributed to other characteristics leading to a misleading picture. We therefore decided to carry out this year’s equalities analysis only for level 3 qualifications for which prior attainment data remains available. Note, however, that results of the analyses carried out in 2024 for GCSEs and level 1, 1/2 and 2 VTQs remain accessible in this interactive report.

As in previous analyses, in addition to presenting the descriptive analysis of the raw differences in results between groups of students, we used regression modelling to estimate differences in results for groups of students after controlling for other variables. The variables analysed were:

ethnicity
sex
special educational needs and disabilities (SEND) status
free school meal (FSM) eligibility (a measure of deprivation)
Income Deprivation Affecting Children Index (IDACI) score
prior attainment
first language (GQ only)
region (GQ only)
centre type according to JCQ categories (GQ only)

For most of the above variables, the largest group is used as a comparator, against which all other groups are compared. For example, for ethnicity, we use white British as the comparator as it is the largest group. We compare students in other ethnic groups with white British students of the same sex, SEND status, FSM eligibility, IDACI score, prior attainment, first language, region and centre type. We estimate the results of the ‘average’ student, that is, a student who falls into the comparator group for every variable, and can then examine the results of each of the other groups separately.

Since 2023, we have published our equalities analysis in an interactive format on Ofqual Analytics. This interactive tool allows users to explore how differences in results between groups of students with different characteristics and socioeconomic status have changed over time.

In this report, results for 2025 are presented alongside those for the period from 2018 to 2024 in the interactive charts and accompanying data tables. We compare results in 2025 with results in 2024. Grading continued as normal in summer 2025 and summer 2024 following the return to pre-pandemic standards in summer 2023.

General qualifications

For A levels, we analysed 3 outcome measures:

grade achieved
the probability of attaining grade A or above
the probability of attaining grade C or above

We used a set of criteria to identify changes we considered to be ‘notable’, those changes that we believe go beyond normal year-on-year fluctuations. These changes are drawn out in the interactive report.

A level

For A level in 2025, the modelling showed that the grade for an “average” student (a student who falls in the reference category for each variable) was between a grade B and grade C (3.53 on a numeric scale of 0 to 6, where 0 is ungraded and 6 is A*). The probability of an “average” student attaining grade A or above was 18.2%, and the probability of them attaining grade C or above was 83.4%. The “average” student’s grade and probabilities of attaining grade C or above and grade A or above in 2025 were broadly in line with 2024.

Of the many comparisons between groups of students presented in our modelling, the majority showed no notable change in 2025 with respect to 2024. No group showed notable changes on all 3 of the above outcome measures.

Vocational and technical qualifications

We focused on national qualifications taken alongside general qualifications in schools and colleges and included in the Department for Education’s (DfE’s) performance tables – specifically Level 3 Applied Generals and Tech Levels qualifications. Unlike GQs, these VTQs have different structures and grade scales. The analysis, therefore, looks at the probability of achieving the top grade, that is the highest grade that can be achieved in each qualification, and a set of criteria are used to identify ‘notable’ changes, that is, changes that we believe exceed usual year-to-year fluctuations.

For Applied Generals and Tech Levels qualifications, the modelling showed that the probability of an “average” student attaining the top grade was 5.2% and 10.6% respectively in 2025, which was broadly in line with 2024.

For Applied Generals, there was no notable change identified between 2024 and 2025 for any group.

For Tech Levels qualifications, all but one of the many comparisons between groups of students presented in our modelling showed no notable change in 2025 with respect to 2024.

The methodology used in this analysis allows us to identify and quantify any changes that have occurred. It is difficult, however, to identify the causes of any changes that are identified. There are likely to be multiple causal factors, including existing societal differences. Results for 2018 and 2019 show that differences in qualification results have existed across this period of time and can vary from year to year. Many of the between-year differences on individual variables reported here are likely to reflect normal fluctuations in outcomes from one year to the next. Also, given the exceptional circumstances under which grades were awarded in 2020 and 2021, and adaptations for qualifications and the package of support in place in 2022, any comparison with these years should be treated carefully.

For any feedback on these graphs, please contact [email protected].

Return to the Ofqual Analytics home page.

If you need an accessible version of this information to meet specific accessibility requirements, please email [email protected] with details of your request.

Introduction

Ofqual routinely carries out equalities analysis for qualification results to examine how attainment gaps for students with different characteristics (some of which are protected) and socio-economic status vary over time. We have repeated this analysis for 2025, for A levels (referred to here as general qualifications (GQ)) and a subset of level 3 vocational and technical qualifications (VTQ) included in Department for Education’s (DfE’s) performance tables.

It should be noted that differences in attainment between groups of students can have multiple and complex causes. It is not possible to disentangle causal factors such as the effects of teaching and learning from the impacts of the pandemic. Therefore, while we aim to report differences in attainment associated with student characteristics where they appear to exist, we do not speculate on their underlying causes. A degree of change is expected in any given year, and some of the findings reported here are likely to reflect normal fluctuations from one year to the next.

Background

Changes to grading due to the impact of the pandemic means that comparisons with certain years in this analysis should be treated with caution. In 2020 and 2021, exams were cancelled due to the pandemic, and arrangements for awarding were different. Grades for GQ were based on Centre Assessment Grades (CAGs) in 2020 and Teacher Assessed Grades (TAGs) in 2021. For VTQ, many assessments were adapted, delayed, or replaced by CAGs in summer 2020 or TAGs in summer 2021. Examinations and assessments were re-introduced in summer 2022, following disruption due to the pandemic in 2020 and 2021. A package of support for students, including adaptations to assessment and more lenient grading, were put in place for GQs and VTQs.

Following the return to pre-pandemic standards in summer 2023, grading continued as normal in summer 2024 and summer 2025. Further information is available in the Ofqual guide for schools and colleges and the Ofqual student guide 2025.

Analytical approach

Alongside descriptive statistics on the differences between groups of students, the report focuses on the result of multivariate modelling, which is more informative than simple descriptive breakdowns by different characteristics. Multivariate analyses allow the effect of a characteristic to be considered while controlling for the impacts of other variables. For example, we can compare the effect of being female vs being male on outcomes, while holding prior attainment, ethnicity, etc. constant. If the modelling shows no female vs male effect, we can conclude that the raw difference between male and female students was associated with other factors – such as a difference in prior attainment or ethnicity, etc. – and not their sex.

Looking at the differences between groups of students in 2018 and 2019 tells us that differences in exam outcomes existed in pre-pandemic times and can vary from year to year even in normal times. By comparing changes between 2025 and 2024 in these differences against changes between 2019 and 2018, we identified the ‘notable’ changes between 2025 and 2024 . We used a series of criteria (see Methodology tab) to identify changes that we considered to be practically significant while taking into consideration normal between-year fluctuations. In this paper, we use the word ‘notable’ to refer to between-group differences that met all of these criteria. The term ‘notable’ is not intended to convey any judgement of the importance of the change in question. (Note that in the analysis carried out in 2024, the same criteria were used to identify notable changes between 2023 and 2024.)

The data used in this analysis included data collected from awarding organisations by Ofqual as well as student-level background information from the DfE. Data was matched using students’ names, date of birth and sex. A set of explanatory variables were included in the models. For each variable, a reference category was chosen. This was usually the largest category (for example, white British is used for ethnicity as this group is the largest ethnic group) or the category that represents the middle of an ordered group (for example, medium is the reference category for prior attainment, the others being very low, low, high and very high). When comparisons are made in the results tabs, each category is compared to the reference category, after controlling for other explanatory variables. Some variables were used in the GQ analysis only due to differences in availability of data.

The variables included in the models, along with their reference categories in brackets, were:

ethnicity (white British)
sex (female)
special educational needs and disabilities status (no SEND)
free school meal (FSM) eligibility as a measure of deprivation (not eligible)
IDACI score (medium)
prior attainment (medium)
first language – included for GQ only (English)
region – included for GQ only (south-east)
centre type (the type of school or other educational setting in which the qualification was taken) – included for GQ only (academies)

We included a measure of prior attainment in this analysis. For A level and level 3 VTQ qualifications, GCSE prior attainment was used. Prior attainment is the stronger predictor of achievements in these qualifications and including it in the models gives more accurate estimates of the effect sizes of other variables. As the model quantifies the effect of each variable after controlling for prior attainment, among other variables, the effects relate to the differences that the variables would have made since candidates took their GCSEs, rather than the differences that the variables may introduce across an entire school career. (Note that in the analyses carried out in 2024 that remain accessible in this interactive report, for GCSEs, level 1, Level 1/2 and Level 2 VTQs, prior attainment was from Key Stage 2.)

A multilevel modelling approach was used to take account of the hierarchical structure of the data that results from clustering. Clustering means that, for example, the students within a school or college might be more likely to have similar results to each other compared to other students from the population, because of factors that are common to that particular school or college. Clustering within schools and colleges was accounted for in both the GQ and VTQ models. In addition, the GQ models took into account clustering within students (across multiple subjects) and within subject.

This analysis focuses on A levels and the subset of VTQs included in the DfE’s performance tables, specifically level 3 Applied Generals and Tech Levels qualifications. The scope of VTQ qualifications included in this analysis is slightly different to the analysis published before 2024 due to changes to the data collection. For this reason and due to improvements to methodology, the current report should be seen as the most up to date source of information for A levels and level 3 Applied Generals and Tech Levels qualifications. The analyses carried out in 2024 that remain accessible in this interactive report are the most up to date source for GCSEs and level 1, 1/2 and 2 VTQs.

General Qualifications Methodology

Data

Data collected from awarding organisations on students’ grades, sex, prior attainment, centre type and region was matched to extracts from the National Pupil Database (NPD) containing FSM status, SEND status, language, ethnicity and IDACI score from the year of awarding. As in last year’s analysis, in order to reduce the proportion of missing data, A level students were first matched to the NPD spring census extract in the awarding year, and those who could not be successfully matched were later matched to the NPD spring census from two years prior, that is, the year when those A level students were taking their GCSEs. (Note that A level students in 2020 and 2021 were matched to the NPD extract in the awarding year to obtain their IDACI19 scores and to the NPD extract two years prior to obtain their IDACI15 scores due to the change in the 2020 data onwards to the IDACI measure.)

Students who could not be uniquely matched or who could be uniquely matched but who had no relevant information in the NPD were marked as missing data on the relevant variable.

To ensure like-for-like comparisons, we built an equalities dataset from the matched data for A level consisting of data for:

• students who by 31 August of the respective year were at the target age of the qualification level of their entries (18 for A level)

• subjects in reform phase 1 and phase 2 that were first assessed in 2017 and 2018 and therefore had 8 years’ data

• schools and colleges whose self-declared centre type designation stayed the same throughout 2018 to 2025

• on a subject-by-subject basis, schools/colleges that had entries in the subject in each of the years 2018 to 2025

These inclusion criteria ensured that our analyses were carried out on successive cohorts that were as stable as possible . This was necessary for a meaningful equalities analysis as it minimises between-year changes in group differences being caused by between-year cohort changes.

Table 1 shows the number of entries by target-age candidates, centres and subjects in the resultant dataset for A level. Table 2 shows the same for the dataset for the GCSE analysis carried out in 2024 that remains accessible in this interactive report. Values have been rounded to the nearest 5.

The table in Appendix B shows the extent of missing data for each background variable used in our analysis. There was no missing data on the region and centre type variables and nearly none on sex, but data on prior attainment and other background variables were missing to varying degrees. We also observed that the extent of missing data varied by centre type.

Missing data on the background variables is most likely the result of schools and colleges not returning the DfE School census. Missing data on GCSE prior attainment most likely reflects the fact that some students did not take GCSEs at 16. (For the GCSE analysis carried out in 2024 that remains acccessible in this interactive report, missing data on KS2 prior attainment most likely reflects the fact that some students did not sit the KS2 tests for a variety of reasons, including being absent from school at the time of the tests, attending independent schools at KS2, or attending school outside of England at the time of the tests.) As with any analysis involving the merging of datasets, missing data can also be caused by data matching problems.

Missing data can be problematic, particularly where it is systematic rather than random. It raises questions about whether the findings from the modelling about the known categories apply to the whole student population, whether those findings would still hold if the unknown information were known, and whether the unknown categories could effectively be treated as proxies for some centre type(s) due to some centre types having much higher proportions of missing data.

Nonetheless, the comparisons of interest here concern not so much the between-group differences within each year, but rather any changes in between-group differences in 2025 compared with 2024. As is evident from the table in Appendix B, the missing data rates and patterns are comparable across the 8 years, so we can reasonably assume the subgroups are comparable in terms of ‘missingness’, although we note below some factors driving instability in the FSM eligibility measure below as well as a change in the outcome of one group with missing data. That is to say, while we might interpret the estimates of the differences between groups within each year cautiously, any change to those differences between years can be interpreted as a change in relative outcomes for different subgroups.

Where data was missing, we included the categories in the model as ‘unknown’ groups. The outcomes for the unknown categories are included in the charts and tables in the results and appendices. We did observe one notable difference (see notable difference methodology below) between 2025 and 2024 on one outcome measure for the unknown category for ethnicity, which could indicate some degree of systematic change underlying the missing group.

In terms of data limitations, it is also worth noting that the proportion of students eligible for FSM increased over the analysis period, probably due to an arrangement related to the roll-out of Universal Credit and, since 2020, the economic impact of the COVID-19 pandemic (Julius and Ghosh, 2022). This increase was reflected in the equalities dataset, with the proportion of entries by FSM-eligible students increasing year on year between 2018 and 2025. The subgroups under the FSM status variable were therefore less stable across the years than other variables included in the analysis.

Modelling overview

We carried out linear mixed-effects modelling on 3 performance measures:

• numeric grade (for A level, grades A* to E were converted to 6 to 1 respectively and U treated as 0)

• probability of attaining A level grade A or above

• probability of attaining A level grade C or above

Each analysis took exam entry as the unit of analysis and aimed to model the relationship, in a particular year, between an entry’s numeric grade or probability of attaining the grades listed above or higher, and background information about the student that the entry belonged to. The full model specification, along with a full list of model variables and their definitions, can be found in Appendix A. Interactions were not included in the models. (In the GCSE analysis carried out in 2024 that remains accessible in this interactive report, the performance measures analysed were numeric grade (9 to 1, and U treated as 0), probability of attaining grade 7 or above, and probability of attaining grade 4 or above.)

Using this modelling, we can estimate the result for an ‘average’ entry, that is, an entry by a student who was in the reference category of every one of the background variables. In numeric grade analyses, we estimate the grade an average entry would receive. In grade probability analyses, we estimate the probability that an average entry would be awarded the key grade or above in question.

We can also estimate the size of the difference in outcome between a particular group and the reference group after controlling for effects of other background variables. For example, in the analysis on the probability of attaining grade A or above in A level in 2018, on the sex variable, an estimate of 0.0337 for male suggests that after controlling for other background variables, in 2018 male students were, on average, 3.37 percentage points more likely to achieve a grade A or above than female students (the reference category of the sex variable). Variation of these group estimates from the models covering each of the 8 years tells us how that group’s results relative to the relevant reference group have changed across the 8 years. There is some degree of uncertainty present in any estimation arising from modelling, so to aid interpretation, 95% confidence intervals are presented in bar graphs. For further information of how the confidence intervals were generated, see Appendix E.

Notable change criteria

We used a multi-step method for evaluating changes in relative outcome differences between key years of comparison, with the aim of identifying practically significant changes while taking into consideration normal between-year fluctuations. We used this method to evaluate changes in relative outcome differences between 2025 and 2024. This is described below. (In the GCSE analysis carried out in 2024 that remains accessible in this interactive report, we used the same method to evaluate changes between 2024 and 2023.)

Step 1: identify subgroups whose relative outcome differences were not statistically significantly different from zero (at 5% level of significance) in any year and exclude them from further consideration. This step identifies the subgroups that showed no difference in any year relative to the reference group and those whose estimates consistently had large standard errors and confidence intervals, suggesting the groups were too small in size and/or had much variability within the subgroup, making it hard for their relative outcome difference to be estimated reasonably precisely.

Step 2: from the subgroups not excluded in Step 1, identify those whose 2024 to 2025 change in relative outcome difference in absolute value was larger than their 2018 to 2019 change in absolute value. This step considers how the relative outcome differences can change between 2 normal years, namely, 2018 and 2019, and identifies between-year changes that exceed normal between-year changes in magnitude.

Step 3: from the subgroups identified in Step 2, flag as ‘notable’ the subgroups whose 2024 to 2025 change exceeded an effect size criterion, which we set at 0.1 grade for the numeric grade measure and 1 percentage point for the grade probability measures.

To appreciate what is meant by a change between 2 years of 0.1 grade in relative outcome difference, we can consider a theoretical scenario comparing left- and right-handed students where we have 2 groups of 100 students and where students in one group are left-handed and students in the other group are right-handed. The 2 groups are otherwise perfectly matched in background, and all take one A level subject in one year. If in one year, all 100 right-handed students receive grade B, and in the left-handed group, 50 receive grade A and 50 receive grade B, the overall difference between the groups is half a grade (0.5 grade). If the following year, the right-handed students all receive grade B, but in the left-handed group, 60 get grade B and 40 get grade A, the overall difference between the groups is slightly less than half a grade (0.4 grade) and the change between the years is 0.1 grade. There are, however, many different ways to get the same size of overall change. In the example above, a change of 0.1 grade between the years would also occur if, in the left-handed group in the second year, 5 students got an A* , 50 got an A and 45 got a B.

Vocational and Technical Qualifications Methodology

Data

The analysis includes vocational and technical qualifications (VTQs) included in the Department for Education’s performance tables, specifically level 1, level 1/ 2 and level 2 Technical Awards and Technical Certificates, and level 3 Applied Generals and Tech Levels qualifications.

We evaluated the impact of each demographic and socioeconomic characteristic on students’ results this year, once other factors were controlled for. This allows us to examine the differences in attainment associated with sex, ethnicity and socioeconomic status, once other factors are controlled for, and how they have changed over several consecutive years from 2018 to 2025 for level 3 qualifications, and from 2018 to 2024 for level 1/2 and level 2 qualifications.

We focused on the students’ achievement of top grades rather than other points of the grading scale. The variety of grading scales used in these VTQs makes it difficult to select a meaningful and consistent midpoint. Additionally, if we were to break the analysis down by the different grading scales, the numbers would likely be too small to allow for meaningful statistical interpretation.

By ‘top grades’ we mean the single highest or best grade that can be achieved in each qualification, which will depend upon the particular grading structure that has been adopted.

We used data collected from 18 awarding organisations for 642 qualifications (not including any separate routes or pathways within those qualifications). We also obtained data on prior attainment from awarding organisations, and we used data from the Individualised Learner Record (ILR, maintained by the Department for Education (DfE)) and from the National Pupil Database (NPD, controlled by the DfE). Whilst DfE maintain the ILR and NPD data used in this analysis, they do not accept responsibility for any inferences or conclusions derived by us. We used student first name, last name, date of birth, sex, qualification number and/or Unique Learner Number (ULN) to match the awards records for each student with these additional datasets. For cases which could not be uniquely matched, and those which could be matched but had no relevant information available on student characteristics of interest, we included missing values in the corresponding fields of the combined datasets.

We analysed the qualification-level grades awarded to all students in centres in England who took assessments for these qualifications throughout the academic year.

For ethnicity, categorisations were harmonised between the NPD and ILR for consistency and to align with the Government Statistical Service guidance List of ethnic groups where possible. This meant some groups were merged with one of the ‘other’ groups, as numbers were too small to include as a separate category. For example, ‘Gypsy or Irish Traveller’ students were merged into the ‘Any Other White Background’ group.

Missing data rates are given in Appendix D. Data on prior attainment and the background variables are missing to varying degrees. The table in appendix D shows the extent of missing data for each background variable used in our analysis. There was little missing data on sex, but data on prior attainment and other background variables were missing to varying degrees. Where data was missing, we included the categories in the model as ‘unknown’ groups.

Modelling overview

We used a generalised linear model (mixed effect logistic regression) to examine the probability of attaining a top grade, given the information on the characteristics of students clustered across centres. The student characteristics described previously were treated as fixed effects, and the student’s centre was treated as a random effect. This is because there may be factors affecting results that only students from the same schools or college have in common, such as the teaching materials used. The full model specification is given in Appendix D.

Within the logistic regression model, the estimates of the fixed effects’ parameters are of most interest for our analyses. These estimates quantify the relationship between each demographic or socioeconomic characteristic as well as prior attainment and the probability of attaining a top grade. Prior attainment is a strong predictor of grades and should be analysed alongside the demographic and socioeconomic variables to facilitate more accurate estimation of the effect sizes of the variables.

Multi-way interactions between the demographic and socioeconomic variables listed above as well as prior attainment were not included in the analysis. This is because the small sample sizes would make the results less meaningful.

For each year separately, we show a measure of effect size of each variable on the probability of achieving the top grade. This is expressed in the form of the average marginal effect, representing an average difference in the probability of achieving top grades between two categories of a demographic or socioeconomic characteristic.

For example, the average marginal effect for sex shows how the average probability of achieving the top grade differs for male students (category of interest) compared to female students (reference category) when the remaining variables (other categories) are held constant. Through these measures, we can assess the difference in relative achievement of the top grade between different demographic groups.

Notable change criteria

In the 2025 level 3 analysis, for each group of qualifications, a logistic regression model was run for each year individually. We then evaluated different years’ outputs to identify which effects have changed between key years of comparison, considering both the statistical significance and the meaningfulness of the effects. We used a multi-step method (similar to that used for GQ analyses) to evaluate changes in effects between 2024 and 2025. The same method was carried out for the 2024 level 1/2 and level 2 analysis, evaluating the changes in effects between 2023 and 2024. The method consists of the following steps:

Step 1: identify which subgroups of students were statistically significantly more or less likely to achieve the top grade than the reference category in any year. That is, where the average marginal effects were statistically significantly different from zero (at 5% level of significance). Include the identified subgroups for consideration in subsequent steps. This step allows us to exclude from further investigation those subgroups that showed no difference in any year relative to the reference group and those whose estimates consistently had large standard errors and confidence intervals, suggesting the groups were too small in size and/or had much variability within the subgroup, making it hard for their effect difference to be estimated reasonably precisely.

Step 2: among the subgroups identified in Step 1, select those whose difference in the average marginal effects between 2024 and 2025 in absolute value was larger than the difference in average marginal effects between 2018 and 2019 in absolute value. This step considers how the size of the average marginal effects can change between two normal years, namely, 2018 and 2019, and identifies between-year changes that exceed normal between-year changes in magnitude.

Step 3: from the subgroups selected in Step 2, flag as ‘notable’ the subgroups whose 2024 to 2025 change in the average marginal effects was larger than plus or minus 5 percentage points.

The purpose of the method is to ensure that changes are only interpreted as being ‘notable’ if they appear to be larger than the normal fluctuations that might be expected in any year. However, smaller changes will also be discussed in the results tabs where they are apparent in the graphs.

For any feedback on these graphs, please contact [email protected].

Return to the Ofqual Analytics home page.

If you need an accessible version of this information to meet specific accessibility requirements, please email [email protected] with details of your request.

What do the charts show?

The analysis below shows how results for different groups of students have changed over time for A levels and GCSEs, when controlling for other variables. The charts, by default, display results gaps for students with a chosen characteristic when compared to students from a reference group.

Our analysis uses multivariate regression modelling, so we can measure the impact of each of the students’ characteristics considered once all others have been held constant. For example, we can compare the results of 2 different ethnic groups, without differences in their overall prior attainment or socio-economic make-up affecting the findings.

For most of the variables, the largest group is used as a comparator, against which all other groups are compared. For example, for ethnicity, we use white British as the comparator as it is the largest group. We compare students in other ethnic groups with white British students of the same sex, special educational needs status, free school meals eligibility, Income Deprivation Affecting Children Index score category, prior attainment category, first language, region and centre type.

For further detail on the methodology used, please see the background and methodology tab.

What do the different options mean?

Select from the different groups included in the analysis. You can also choose whether you would like to see outcomes in terms of numeric grade or probability of attaining a particular grade or higher. For the numeric grade measure, grades A* to E for A level were converted to 6 to 1 respectively and U treated as 0, and grades for GCSE were 9 to 1 on a numeric scale and U treated as 0.

You can then choose to display:

the outcomes from the statistical modelling, shown as bars, where results take into account other variables included in the analysis; and/or
raw outcomes shown as points, which are the averages, or the differences of the averages from the reference group, for each sub-group without controlling for other variables.

In addition, you can also choose whether to view:

• the differences relative to the reference group; or

• the overall absolute estimates.

Finally, the charts are scaled in a consistent way across all variables by default, so that it is easier to understand the relative size of the differences of different variables. However, you can choose to rescale the chart to show a closer view of any particular variable by selecting “Scaled”.

Qualification level:

Variable:

Difference from reference group:

Modelled (bars)

Raw (points)

Outcome measure:

Absolute or relative estimates:

Absolute

Relative

Scaling:

Fixed Scaled

Display

Chart Table

2024 to 2025 notable changes

2023 to 2024 notable changes

Where the subgroup is being used as the comparator, values are noted as “[z]” in the table

For any feedback on these graphs, please contact [email protected].

Return to the Ofqual Analytics home page.

If you need an accessible version of this information to meet specific accessibility requirements, please email [email protected] with details of your request.

What do the charts show?

The analysis below shows how results for different groups of students have changed over time for VTQs included in the Department for Education’s performance tables, specifically level 1, level 1/ 2 and level 2 Technical Awards and Technical Certificates, and level 3 Applied Generals and Tech Levels qualifications, when controlling for other variables. The charts, by default, display results gaps for students with a chosen characteristic when compared to students from a reference group in terms of average probabilities of achieving top grades.

For most of the variables, the largest group is used as a comparator, against which all other groups are compared. For example, for ethnicity, we use white British as the comparator as it is the largest group. We compare students in other ethnic groups with white British students of the same sex, special educational needs status, free school meal eligibility, Income Deprivation Affecting Children Index score and prior attainment.

For further detail on the methodology used, please see the background and methodology tab.

What do the different options mean?

Select from the drop-down menus the qualification group and the different subgroups you would like to see analysis for.

You can then choose which outcomes should be displayed. A default ‘Modelled’ option, shown as bars, shows results in which the students’ other characteristics are taken into account. The ‘Raw’ option, shown as points, will show outcomes which are the raw percentage point differences between each sub-group and the reference group without controlling for any other variables.

Qualification level:

Variable:

Qualification group:

Difference from reference group:

Modelled (bars)

Raw (points)

Display

Chart Table

2024 to 2025 notable changes

2023 to 2024 notable changes

Where the subgroup is being used as the comparator, values are noted as “[z]” in the table

For any feedback on these graphs, please contact [email protected].

Return to the Ofqual Analytics home page.

If you need an accessible version of this information to meet specific accessibility requirements, please email [email protected] with details of your request.

Appendices:

All models fitted can be expressed mathematically as:

γ_ijk = α + βχ_ijk + u_i + u_j + u_k + e_ijk

where

γ_ijk = [in numeric grade analysis] numeric grade awarded for exam entry by candidate i in centre j in subject k; [in grade probability analysis] exam entry by candidate i in centre j in subject k being awarded the target grade or not (1 or 0);

χ_ijk = a set of background variables about candidate i in centre j taking the exam entry in subject k;

u_i = random intercept of candidate i;

u_j = random intercept of centre j;

u_k = random intercept of subject k;

e_ijk = entry level residual.

All analyses included student, centre and subject as random effects, to take account of students taking multiple subjects and students clustering within centres. The subject random effects had the following categories:

A level: Art & Design: 3D Studies, Art & Design: Art, Craft and Design, Art & Design: Critical and Contextual Studies, Art & Design: Fine Art, Art & Design: Graphics, Art & Design: Photography, Art & Design: Textiles, Biology, Business Studies, Chemistry, Classical Greek, Computing, Dance, Drama & Theatre Studies, Economics, English Language, English Language & Literature, English Literature, French, Geography, German, History, Latin, Music, Physical Education, Physics, Psychology, Religious Studies, Sociology, Spanish
GCSE: Art & Design: 3D Studies, Art & Design: Art, Craft and Design, Art & Design: Critical and Contextual Studies, Art & Design: Fine Art, Art & Design: Graphics, Art & Design: Photography, Art & Design: Textiles, Biology, Chemistry, Citizenship Studies, Classical Greek, Combined Science, Computing, Dance, Drama, English Language, English Literature, Food Prep and Nutrition, French, Geography, German, History, Latin, Mathematics, Music, Physical Education, Physics, Religious Studies, Spanish

The fixed effects of the models were: (please refer to the tables available on the results page for keys to the acronyms and abbreviations)

Sex: male, female (reference category) (the unknown/neither category was omitted in the modelling because of the extremely small number of entries belonging to the category)
Ethnicity: ABAN, AIND, AOTH (subsuming ABRI [Asian British, a new category with few cases in our dataset], APKN, BAFR, BCRB, BOTH (subsuming BBRI [Black British, a new category with few cases in our dataset]), CHNE, MOTH, MWAS, MWBA, MWBC, WBRI (reference category), WIRI (only in GCSE analyses), WOTH (subsuming WIRT and WROM in A level analyses), WROM (only in GCSE analyses), unknown
Major language: English (reference category), NotEnglish, unknown
SEND status: NoSEND (reference category), SEND, unknown
FSM eligibility: NoFSM (reference category), FSM, unknown
Deprivation: very low, low, medium (reference category), high, very high, unknown. This measure is based on IDACI scores. (A level students in 2020 and 2021 each had a IDACI19- and a IDACI15-based categorisation. For the minority of students whose IDACI19 and IDACI15 categorisations were not identical, the IDACI19 categorisation was used, except when it was unknown, in which case the IDACI15 categorisation was used.)
Prior attainment: very low, low, medium (reference category), high, very high, unknown
Centre type: Acad (reference category), Free, FurE, Indp, Other, SecComp, SecMod, SecSel, Sixth. For years when Tert (tertiary) and FurE were separate categories, Tert was collapsed into FurE. For all years in the A level analysis carried out in 2025, some selective centres listed in other categories on the NCN register were moved to the SecSel category to improve the accuracy of this centre type.
Region: EM, EA, LD, NE, NW, SE (reference category), SW, WM, Y&H

For the grade probability measures, we also ran logistic regressions in which the dependent variable yijk was the natural logarithm of the odds of the exam entry by candidate i in centre j in subject k being awarded the target grade. The exponentials of the β coefficients of a fitted logistic regression model are the odds ratios between groups which can be interpreted as estimates of the likelihood of entries by students of a particular group being awarded the target grade relative to entries by students of the reference group after controlling for other variables. For the between-group comparisons examined in our modelling, the pattern of changes in odds ratio across the years in the logistic models was consistent with the pattern of changes in relative outcome difference across the years in the linear probability models. We present results of the linear models for the grade probability measures in this report, as probabilities are more intuitive to interpret than odds ratios, but a drawback of the linear models should be noted, which is that some high performing subgroups have modelled probability estimates and/or the upper limits of the 95% confidence intervals of those estimates larger than the theoretical maximum of 1.

An accessible version of this document is available here

A mixed effect modelling approach was also adopted for VTQ analysis, whereby the learner characteristics (including their prior attainment) were treated as fixed effects, and the learner’s centre was treated as a random effect. This is because there may be factors affecting results that only learners from the same centre have in common, such as teaching materials used at their school or college.

The logistic regression specification used for modelling takes the form :

log

P(Y_ij=1)

1 - P(Y_ij=1)

= α + βχ_i + γZ_i + c_j

where:

P(Y_ij=1) is the dependant variable. It determines the probability of achieving a top grade for candidate i at centre j

χ_i is a set of independent fixed effect variables summarising the demographic and socioeconomic characteristics of the candidate

Z_i is an independent fixed effect variable determining the highest level of prior attainment of candidate i. It is an additional variable that is controlled for in the logistic regression analysis

c_j is a random effect determined by centre-level characteristics. It is also an additional variable that is controlled for in the logistic regression analysis

α, β, γ are the regression coefficients

All β coefficients had associated standard errors (SEs), which quantified how precisely β coefficients had been estimated. The SEs were used to compute the 95% confidence intervals (CIs) of the β coefficients, using the formula: 95% CI = β ± 1.96*SE. βs (taken as estimates of relative outcome differences after controlling for other background variables). The same calculation was used for both GQ and VTQ analyses.

For any feedback on these graphs, please contact [email protected].

Return to the Ofqual Analytics home page.

If you need an accessible version of this information to meet specific accessibility requirements, please email [email protected] with details of your request.