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.

The data collected and the major points were fairly straightforward:

Participant Demographics:

• Age: Mean = 25.6, SD = 8.0
• Education: Almost all were mid-college or post-college (and about 1/6 post-graduate study).
• Usage: Most (56/78) reported specifically personal usage, and only 2 subjects reported specifically professional usage (14 indicated both, however).

Tools Used:

• Vast majority listed Facebook (61/78) – this was unsurprising (also, Facebook Stream listed as first example of an “activity stream” in survey instructions.)
• Wide Twitter usage (41/78) was surprising, however.  Past experiencing with polling for Twitter-related topics on MTurk had resulted in low yield.  Perhaps this is due to the crazy upswing in Twitter sign-ups over the past few months?
• Other than MySpace (16/78), tools such as LinkedIn, Yammer, FriendFeed, and others were barely listed, indicating either that these tools are not widely used or that people do not consider some of these to be activity streams.

Functions Served:

• Note: These responses were loosely categorized by me – this was not intended to be a rigorous academic study, but rather a glimpse into usage of these tools.
• Status(33/78), Communication(32/78), and Information(19/78) were listed as the most common functions served.
• Responses also demonstrated a wide variety of usage, however, including some less anticipated uses such as Journaling.  Perhaps this speaks somewhat to the flexibility of these tools and the ability that users have to adapt them to their own needs.

Feature Requests / Improvements:

• These are exact quotes from participants (again, loosely grouped into categories by me – no cross-coding was done).

As you can see, the “Summary” was really just a reminder about what people said regarding potential improvements, but I thought this was really the most interesting part.  It’s interesting that most of the things that people asked for were things that are either available or which could be easily made available by new activity stream client applications, so there may be a lot of low-hanging fruit out there for application developers.

I’d be curious to see if any of you has done (or seen) similar research regarding Twitter, Facebook, or other activity streams (whether on MTurk or otherwise) and if you found similar or different trends.  If you are interested in clarification, more details, or discussion about any of the points brought up here, the comments section awaits.