You seem to be concerned with [reliability](http://en.wikipedia.org/wiki/Reliability_(psychometrics)), and more specifically [internal reliability](http://en.wikipedia.org/wiki/Internal_consistency). Internal reliability is the degree to which different questions are measuring the same construct. This concept is used often in psychology and is usually measured using [Cronbach's alpha](http://en.wikipedia.org/wiki/Cronbach%27s_alpha). However, it is typically used to measure the reliability of a *test*, and not the reliability of an *individual*.

As Jeromy Anglim points out, I think it's important to consider the goal here. Using a two question Likert scale is probably not good enough to reliably detect outliers: What if the respondent checked all '4s' on a 7-point Likert scale? Reversing the scale would have no effect.

One alternative approach is to employ an **instructional manipulation check** (Oppenheimer et al., 2009). The gist of the technique is to trap participants into answering a question in a *specific way* that they could only have done by reading the instructions carefully. Here is an example from a survey administered by Facebook:

![enter image description here][1]
 
While this technique may throw out a few good participants, it will almost certainly raise the signal-to-noise ratio of your data by only including participants who followed instructions and read questions before answering.

Another tried and true technique is to use a computer-administered test and look at reaction times. You may be able to throw out a few responses (or whole participants) by simply looking for outliers in response time that are below the mean.

> Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45(4), 867-872.


  [1]: https://i.sstatic.net/9qFm0.png