Using statistics Postgraduate study skills

We see the results of statistical analysis every day. For example, when we read about crime rates in a newspaper or look at election predictions we are seeing the results of data interpreted by means of carefully chosen statistical tools.

Aim for an understanding of what the statistics are trying to achieve and how they are interpreted. You might want to work through the formulas as that helps you to understand the statistics. Be reassured that your study materials will fully explain what is expected.

Qualitative or quantitative data?

The first thing to understand is the difference between qualitative and quantitative data. These two methods can overlap and are not necessarily totally separate.

Qualitative

Evidence, data or information expressed in non-numerical terms. An example of qualitative data would be that gathered from a semi-structured interview, where the respondents can answer the questions how they like and do not have to tick a box.

Quantitative

Evidence, data or information expressed in numerical terms. An example of quantitative data would be that gathered from closed questions on a questionnaire. The respondents would 'tick a box' when giving their answer (i.e. 'A' 'B' 'C' or 'D'), and you could then count how many people said 'A' how many said 'B' etc. The data is numeric.

Data presentation

You can present your data using charts, tables and graphs. The benefit of this is that it makes the data easier to read and interpret - not just for you, but also for the people reading your work.

You have probably come across programs such as Excel and Access, but there are lots of others. SPSS is a great tool for in-depth data analysis and is often the program of choice when working with large amounts of complicated data.

If you are unfamiliar with presenting data, take a look at this course on Working with charts, graphs and tables on OpenLearn.

Data interpretation

When interpreting data (whether your own or someone else's) it is important to understand the context of the information, the methods used and exactly what type of information you have (e.g. percentages can be generated from the raw numerical data and included in tables for presentation).