Te Kete Ipurangi Navigation:

Te Kete Ipurangi

Te Kete Ipurangi user options:

You are here:

Working with data

These webpages are for educators who want to be able to load or download data on to a spreadsheet, and use it for their own analysis.

They may want to go beyond the data reports from the digital assessment tools available, or from their student management systems, or analyse data from assessments which produce only raw data.

The topics in the section have advice on manipulating data on a spreadsheet to prepare it for analysis. There are basic instructions on how to clean, sort and move data, and how to make and read simple graphs.

Information on data reports from some assessment tools is available here.

Information on data reports from student management systems is available here.

Working with data concepts

Standards-based assessment

Standards-based assessment allows us to make judgments about the level of an individual's learning with respect to shared benchmarks of expected performance, supported by exemplars.

Reliability and validity

The reliability of an assessment tool is the extent to which it measures learning consistently. The validity of an assessment tool is the extent by which it measures what it was designed to measure.

Types of data

An important part of a well-designed analysis is to be aware of the types of data that are available, so that the appropriate analytic techniques are employed, and inappropriate ones avoided.

Mean, median, and standard deviation

Mean, median, and standard deviation

The mean and the median are both measures of central tendency. Standard deviation (SD) is a widely used measurement of variability used in statistics.

Percentages, percentiles, and stanines

In order to understand and analyse data from an assessment tool, you need to know the differences between the ways that different tools measure student achievement, and what that might mean for your analysis.


Norms are statistical representations of a population, for example PAT maths scores for year 6 males, or e-asTTle reading scores for year 9 Māori females.

Effect size

A good way of presenting differences between groups or changes over time in test scores or other measures is by ‘effect sizes’, which allow us to compare things happening in different classes, schools or subjects regardless of how they are measured.

Working with data topics

Loading or downloading data onto a spreadsheet

There are several ways by which quantitative data in the form of scores can be entered into a spreadsheet. Data can be downloaded from a digital assessment tool or student management system.

Cleaning and formatting data

When working with data to analyse results and draw conclusions, it is essential that the data with which you are working is ‘clean’. This means that it is consistent, accurate and complete.

Creating your own simple graphs

Graphs (also called charts) play an important role in data analysis. A graphic representation can make the relationship between sets of data much easier to understand.

Disaggregating data

Student achievement data is often reported for whole populations (for example: cohorts, year levels, whole class). This is called aggregate data.