There are four kinds of data you encounter in an analysis. Nominal (Categorical), Ordinal, Interval, and Ratio.
The different types of data have different characteristics for mathematical operations.
|Frequency count, mode, chi-square||O||O||O||O|
|Add, subtract, mean, variance, correlation, regression||X||X||O||O|
|Geometric/harmonic mean, coefficient of variation, logarithms||X||X||X||O|
Thus, the ratio data allow you to do the most mathematical operations followed by interval, ordinal, and nominal data. It is better to design your experiment so that your dependent variable (what you measure) is ratio. It allows you to do a variety of analyses.
And you have two distinctive variables for statistical tests.
Let's say you are comparing the performance time of two interaction techniques (Technique A and Technique B). Your independent variable is techniques, which is nominal. You are comparing performance time against the techniques and there is no concept of ordering for the techniques. Your dependent variable is performance time (msec), which is ratio. You can order time and the millisecond is an equal unit. It is important to figure out which dependent variables and independent variable you use and what types of data they are before jumping into any kind of statistical tests. The types of data determine statistical methods you can use. Particularly, it is generally a good idea to make your dependent variable interval or ratio because it allows you to do a wider variety of statistical analyses than nominal or ordinal.
One thing you may need to consider is how to treat the data from your Likert-scale questions. If you can assume that the differences between any two options are equal, you can treat them as interval data. For instance, if your options are strongly agree, agree, neutral, disagree, and strongly disagree, you may be able to treat them as interval data. However, if your options are like use it everyday, use it once a week, use it once a month, use it once a year, and have never used it, it is probably safer to treat them as ordinal data.