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In the upper right hand corner you see your email address - click the link that says "account." This takes you to your profile and home pages. Click on "home" and under preferences select "never" or you will get email updates every time some edits the class wiki - which will be very irritating. Click on the "workspace" link to get back to the class wiki.
Please see the orientation video below and the Orientation Workshop page for details.
This wiki is the official course website for Comm 156I. It contains:
• course schedule
• syllabus
• workshops
• links to other resources
Connect to these pages via the navigation window >>>
Course Communication
If you need to contact the instructor use the course email address comm156@gmail.com. Please email the instructor ASAP so you can get access to the comments feature on this wiki.
Primary communication for this course is through the course Google Groups listserv. This is how I will send course information to you. Once you email me I will sign you up.
You can also make an appointment to see me during office hours (Th 1:30-3:00).
The Word: Homphily (love of the same). In research, this is the tendency to gravitate to those things we are the most familiar with and to questions and answers that reinforce our own likes, dislikes, culture, attitudes, tendencies, and bias. Social science is one method of inquiry that seeks to systemitize our research in such a way to blunt homophilia.
Subjectivity and Objectivity in Numbers
Statistics and other quantitative measures are often perceived as "objective," more "real," or more reliable and less "squishy" than qualitative analysis. However, what is measured, how it is measured, what types of questions are asked (or not), and how the data is interpreted are very subjective. The graphic (p. 46) below is based on U.S. Governent data and was created for the article Numbers Racket: Why the Economy is Worse than We Know for the May 2008 issue of Harpers. This part of the article discussed how the basis for unemployment statistics has been amended over time and is artificially low because of who is counted (or not) and how they are represented. The numbers look good (or at least better) if you choose to exclude certain people from being counted (those who who are long-term unemployed, or under-employed, and those who new workers who had been looking for work for less than year). The part of the graph in black would seem to tell a very straight forward story and would lead you to certain conclusions if you were unaware of the rest of the graph/data.
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