Chapter 15: Analysing Quantitative Data

A. Questions for Keeping the Bigger Picture in Focus 
   1. Questions related to your own expectations
  • 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?
   2. Questions related to research question, aims and objectives
  • 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?
   3. Questions related to theory
  • 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?
   4. Questions related to methods
  • 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? 
B. Quantitative Data Management 
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. 
C. Steps in Quantitative Analysis: Stepping Your Way through Effective Quantitative Data Analysis
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.