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Consider the evidence: Terminology


These notes are intended to assist teachers and facilitators using the resource – Consider the evidence. They are not intended to be definitions.


A detailed examination of data and other evidence intended to answer a question or reveal something.

This simplistic definition is intended to point out that data analysis is not just about crunching numbers – it’s about looking at data and other evidence in a purposeful way, applying logic, creativity, and critical thinking to see if you can find answers to your questions or reveal a need. For example, you can carry out a statistical analysis of national assessment results in the various strands of English across all classes at the same level. You could compare those results with attendance patterns. But you might also think about those results in relation to more subjective evidence – such as how each teacher rates their strengths in teaching the various strands.


A number of measures made into one.

This is a common and important concept in dealing with data. A single score for a test that contains more than one question is an aggregation – two or more results have been added to get a single result. Aggregation is useful when you have too few data to create a robust measure or you want to gain an overview of a situation. But aggregation can blur distinctions that could be informative. So you will often want to disaggregate some data – to take data apart to see what you can discover from the component parts. For example, a student may do moderately well across a whole subject, but you need to disaggregate the year’s result to see where their weaknesses lie.


Known facts or measurements, probably expressed in some systematic or symbolic way (for example: as numbers).

Data are codified evidence. (The word is used as a plural noun in this kit.) The concepts of validity and reliability apply to data. It helps to know where particular data came from; how data were collected and maybe processed before you received them. Some data (for example: attendance figures) will come from a known source that you have control of and feel you understand and can rely on. Other data (for example: standardised test results) come from a source you might not really understand; they may be subject to manipulation and predetermined criteria or processes (like standards or scaling). Some data (for example: personality profiles) may be presented as if they are sourced in an objective way but their reliability might be variable.


Data relating to characteristics of groups within the school’s population. Data that provides a profile of people at your school.

You will have the usual data relating to your students (gender, ethnicity and so on) and your staff (gender, ethnicity, years of experience and so on). Some schools collect other data, such as the residential distribution of students and parental occupations.


See Aggregation


Any process of reviewing or making a judgment about a process or situation.

In this resource, evaluation is used in two different but related ways. After you have analysed data and taken action to change a situation, you will carry out an evaluation to see how successful you have been – this is "summative evaluation". But you are also encouraged to evaluate at every step of the way – when you select data, when you decide on questions, when you consider the results of data analysis, when you decide what actions to take on the basis of the data – this is called "formative evaluation".


Any facts, circumstances or perceptions that can be used as an input for an analysis or decision.

For example, the way classes are compiled, how a timetable is structured, how classes are allocated to teachers, student portfolios of work, student opinions. These are not considered to be data, because they are not coded as numbers, but they can be factors in shaping teaching and learning and should be taken into account whenever you analyse data and when you decide on action that could improve student achievement.


Knowledge gained from analysing evidence and making meaning from evidence.

Information is knowledge (or understanding) that can inform your decisions. How certain you will be about this knowledge depends on a number of factors: where your data came from, how reliable it was, how rigorous your analysis was. So the information you get from analysing data could be a conclusion, a trend or a possibility.

Inter-subject analysis

A detailed examination of data and evidence gathered from more than one learning area.

Inter-subject analysis can answer questions or reveal trends about students or teaching practices that are common to more than one learning area. For example, analysing the results of students taking mathematics and physics subjects can indicate the extent to which achievements in physics are aided or impeded by the students’ mathematical skills.


Any action that is taken to change a situation, generally following an analysis of data and other evidence.

This term is useful as it emphasises that to change students’ achievement, you will have to change something about the situation that lies behind achievement or non-achievement. You will take action to interrupt the status quo.

Intra-subject analysis

A detailed examination of data and other evidence gathered from within a specific learning area.

Intra-subject analysis can answer questions or reveal trends about student achievement or teaching within a subject or learning area. For example, an analysis of assessment results for all students studying a particular subject in a school can reveal areas of strength and weakness in student achievement and/or in teaching practices, etc. Comparison of a school’s results in a subject with results in that subject in other schools is also intra-subject analysis.

Longitudinal analysis

A detailed examination of data and evidence to reveal trends over time.

Longitudinal analysis in education is generally used to reveal patterns in, for example, student achievement or behaviour, over a number of years. Results can reveal the relative impact of different learning environments, for example. In this resource, it is suggested that longitudinal analysis can be applied to teaching practice and school processes. For example, the impact of modified teaching practices in a subject over a number of years can be evaluated by analysing the achievements of successive cohorts of students.