• What do I expect to find, i.e. will my hypothesis bear out?
• What don’t I expect to find, and how can I look for it?
• Can my findings be interpreted in alternative ways? What are the implications?

• How should I treat my data in order to best address my research questions?
• How do my findings relate to my research questions, aims, and objectives?

• Are my findings confirming my theories? How? Why? Why not?
• Does my theory inform/help to explain my findings? In what ways?
• Can my unexpected findings link with alternative theories?

• Have my methods of data collection and/or analysis coloured my results? If so, in what ways?
• How might my methodological shortcomings be affecting my findings? 

Step 1: Familiarize yourself with appropriate software

Programs you are likely to come across include:

   - SPSS

   - SAS

   - Minitab 

   - Excel

   - R

Step 2: Log in your data

Data can come from a number of sources at various stages throughout the research process, so it is well worth keeping a record of your data as it is collected. 

Step 3: Screen your data for any potential problems

This includes a preliminary check to see if your data is legible and complete. If done early, you can uncover potential problems not picked up in your pilot and make improvements to your data collection protocols.

Step 4: Enter the data

There are two steps involved in data entry. The first is to define your variables. The second step is to systematically enter your data into a database. 

Step 5: Clean the data

This involves combing through the data to make sure any entry errors are found, and that the data set looks in order. 

Step 1: Manage the data

This involves familiarizing yourself with appropriate software; systematically logging in and screening your data: entering the data into a program; and finally, ‘cleaning’ your data.

Step 2: Understand variable types

Different data types demand discrete treatment, so it has important to be able to distinguish variables by both cause and effect (dependent or independent), and their measurement scales (nominal, ordinal, interval and ratio).

Step 3: Run descriptive statistics

These are used to summarize the basic features of a data set through measures of central tendency (mean, mode and median), dispersion (range, quartiles, variance and standard deviation), and distribution (skew ness and kurtosis).

Step 4: Run appropriate inferential statistics

This allows researchers to assess their ability to draw conclusions that extend beyond the immediate data. For example, if a sample represents the population; if there are differences between two or more groups; if there are changes over time; or if there is a relationship between two or more variables.

Step 5: Make sure you select the right statistical test 

This relies on knowing the nature of your variables; their scale of measurement; their distribution shape; and the types of question you want to ask.

Step 6: Look for statistical significance

This is generally captured through a ‘p-value’, which assesses the probability that your findings are more than coincidence. The lower the p-value, the more confident researchers can be that findings are genuine.