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To improve the quality and clarity of this publication, we intend to make the following changes to the data and publication from our next publication in November 2023.

Proposal 1: addition of new 鈥榮chool type鈥� category

We plan to add a new data field called 鈥榮chool type鈥� to each of the existing underlying datasets (D1a, D1b and D2) This will be used to categorise schools in the relevant aggregated data tables (replacing the 鈥榯ype of education鈥� field). 鈥楽chool type鈥� will split schools into the following 3 categories based on their type of education and faith grouping:

• 鈥榠ndependent special schools鈥�
• 鈥榠ndependent faith schools鈥�
• 鈥榦ther independent schools鈥� (that are not special and have not declared a faith ethos)

Proposal 2: providing revised underlying data for previous years

We plan to include an extra dataset (D3) containing 鈥榬evised鈥� in-year data for standard and additional inspections. Revised data is the previous years鈥� provisional data, plus all inspections from the previous year that were published after the cut-off deadline stated in our methodology. We will provide this data for all inspections from the Education Inspection Framework (EIF), that is, from 1 September 2019, up to a maximum of 5 academic years. We currently only provide this revised data in the following year鈥檚 publication at an aggregate level.

Proposal 3: include previous inspection outcomes in the in-year inspection data

We plan to add data fields that include the school鈥檚 previous standard inspection outcomes and associated data 鈥� such as previous inspection date 鈥� to the in-year inspections dataset (D1a). We currently include previous inspection details in our most recent inspections data (D2), but not the in-year data.

Proposed outcomes

These changes will allow:

• the proposed 鈥榮chool type鈥� categorisation for non-association independent schools to provide a better reflection of the schools inspected, and greater clarity to users
• more transparency by providing an additional dataset to include data about inspections that missed the publication cut-off for previous releases and the ability for users to easily replicate analysis from our statistics