There are 4 questions you must answer to choose an appropriate statistical analysis.
1. What is your Research Question?
2. What is the scale of measurement of the variables used to answer the research question?
3. What is the Design? (between subjects, within subjects, etc.)
4. Are there any data issues? (missing, censored, truncated, etc.)
If you have not already, read about these in more detail.
One of the most common situations in which researchers get stuck with statistics is choosing which statistical methodology is appropriate to analyze their data. If you start by asking the following four questions, you will be able to narrow things down considerably.
Even if you don’t know the implications of your answers, answering these questions will clarify issues for you. It will help you decide what information to seek, and it will make any conversations you have with statistical advisors more efficient and useful.
I first encountered the Great Likert Data Debate in 1992 in my first statistics class in my psychology graduate program.
My stats professor was a brilliant mathematical psychologist and taught the class unlike any psychology grad class I’ve ever seen since. Rather than learn ANOVA in SPSS, we derived the Method of Moments using Matlab. While I didn’t understand half of what was going on, this class roused my curiosity and led me to take more theoretical statistics classes. The rest is history.
A large section of the class was dedicated to the fact that Likert data was not interval and therefore not appropriate for statistics that assume normality such as ANOVA and regression. This was news to me. Meanwhile, most of the rest of the field either ignored or debated this assertion.
16 years later, the debate continues. A nice discussion of the debate is found on the Research Methodology blog by Hisham bin Md-Basir. It’s a nice blog with thoughtful entries that summarize methodological articles in the social and design sciences.
To be fair, though, this blog entry summarizes an article on the “Likert scales are not interval” side of the debate. For a balanced listing of references, see Can Likert Scale Data Ever Be Continuous?