Our approach to analysing data draws together the crucial elements of data storytelling using a step-by-step approach that involves:

• Step I:

    Posing the right questions and hypotheses.

• Step II:

    Choose a sample and gather data.

• Step III:

    Organise, clean, and present the data,

• Step IV:

    Summarise, visualise and analyse the data.

• Step V:

    Interpret the findings from the analysis.

• Step VI:

    Communicate findings as part of School Story.

Questions are categorised into those requiring: a univariate analysis; exploration of relationships; comparison between distributions and data reduction. An aide-memoire is provided to outline the types of statistical tests required to answer questions typically asked by, and of, school leaders in each category.  A sample dataset is introduced, which is used in later modules to provide model solutions and worked examples that employ extracts of R-code provided to assist those new to this approach. An introduction to the use of the statistical package R is provided.

Detailed guidance on analysing data using the well-known statistical package R, follows that provided in Chapter 11 of the book. The aim is to up-skill school leaders in the use of statistics through the use of worked examples and associated R-code.

The development of leaders’ understanding and use of the main statistical techniques used to analyse data are best presented in six sections:

• Visualising data using tables and charts;
• Conducting univariate tests;
• Exploring relationships using chi square, correlation and multiple regression;
• Using factor analysis to discover strengths and weaknesses;
• Comparing two or more measures using t-tests and Analysis of Variance;
• Analysing qualitative data.

In addition to providing taught sessions on data analysis, we can be commissioned to gather, analyse and interpret school data using a wide range of multivariate statistical tests. We always adhere to BERA guidelines.