Analyzing Responses to Likert Items

I’m embedding a presentation I gave at a recent “Data Lunch” about how to analyze responses to Likert items. As I am not a stats expert in any respect, I learned a number of things while putting this together – one of the most important is that Likert isn’t actually pronounced “Like-ert”, it’s pronounced “Lick-ert”, which is still tough for me to remember to say. Anyways, hope you enjoy, I’ll include some summary below as well.

Here are some brief notes on the presentation (to avoid the inevitable TL;DR comments):

• Data used was from a study I ran on Mechanical Turk looking at whether the tool WikiDashboard helps people to make different judgments about the credibility of Wikipedia articles.
• Participants placed in 1 of 3 conditions: (WO = Wiki Only, WH = Wiki + the History Page, WD = Wiki + WikiDashboard)
• Articles varied with respect to presumed quality and presumed controversy.
• Using non-parametric tests was fairly straightforward, but none were all that powerful (able to help find interaction effects – one main hope of the study would be to find an interaction between group and quality).

Anyways, this presentation is not supposed to be an expert statistics guide – rather, it represents the results of my research in trying to solve this problem (again, I’m very much not a statistics expert). There are surely many other ways to address the problem, and I would appreciate hearing from others who have tried attacking Likert items for their studies. I am continuing to analyze the data and may post some results in the near future.