Guide to Data Analysis

The impact of an impairment and the barriers that persons with disability face can differ from city to city. Even the barriers present in a large city are not necessarily the same as those faced by the same sector in a rural community. Revisit your local plans and examine the recurring issues that perennially affect your community. What questions from the IDMS Tool are relevant to these issues? Does your organization have existing documents that can help you understand the problem further? Asking these questions is often a good way to start with data analysis. But the questions you really must be asking yourself when working with disability data are:

• What is happening? • Why is it happening?

Essentially, data analysis is providing answers to these questions and supporting them with more data. There are millions of ways of interpreting a given data which makes it difficult and confusing. Here’s a simple process in analyzing your data:7

Step 1: Think about what you want the data

for – what do you need to know? What is the overarching theme? This can be as big or as small as you like, for example, employability of persons with disability living in the community or number of accessible buildings.

Step 2: Come up with ideas. The aim is to try and find all possible measures of your chosen theme in Step 1. The reason behind this is to get your theme broken down into small manageable questions. For example if you try to look up ‘employability’ as a whole you will not be able to find anything.

7 Making Disability Data Work for You: A Community Data Toolkit (Part 1), Using disability data [website], https://www. gov.uk/government/publications/using-disability-data, (accessed 12 January 2021). However, if you split employability into ‘educational attainment’, ‘presence of educational institutions’ and ‘livelihood opportunities’ available, you may stumble upon important connections and get the answers you want.

Once you have identified the specific themes you need data for, the second phase is to decide what type of data you will be needing.

The indicators you choose and the summaries you generate from those indicators must satisfy the themes you listed down in Step 1.

50 40 30 20 10 0

Employment status

Self-employed - 8 Unemployed (retired) - 3 Unemployed (not seeking) - 42 Unemployed (actively seeking) - 9 Contractual - 5 Student - 16 Part-time - 19 Business owner - 2 Full-time - 1