6 posts from April 2015

April 28, 2015

Extreme Inequality: Workers vs.CEOs

Peter kaufman 2014By Peter Kaufman  

Imagine you work full-time as a customer service representative at a call center for one of the giant telecommunication companies. Your job is to help customers deal with a whole array of problems they may have with their wireless devices from poor reception to billing miscalculations to hardware malfunctions. At times, you must talk with irate and agitated callers but you must deal with these customers quickly and expediently or else your job performance will suffer and you may miss out on the potential for year-end bonuses.  You have been working for this company for nearly two years and you make just under $25,000 per year.

 Given the work you do for the company and the salary you earn, how do you think your income should compare to the CEO of this company? Would it be fair that the CEO makes 10 times more than you? 50 times more? 100 times more? 500 times more?  How about 1000 times more than what you earn? This would actually be the reality for you if you worked for T-Mobile. In 2013, the CEO of T-Mobile, John J. Legere, made over 29 million dollars in total compensation—an amount that is greater than 1,100 times what you earned.

Continue reading "Extreme Inequality: Workers vs.CEOs" »

April 24, 2015

The “Starbucks Effect”: Correlation vs. Causation

Headshot 3.13 cropcompressBy Karen Sternheimer

Earlier this year, several news organizations reported on a study that found that homes near a Starbucks increased in value at a rate higher than others during a fifteen year period. Did Starbucks cause this larger rise in home values?

The headlines seemed to suggest it had: “What Starbucks Has Done to American Home Values,” “Living Near a Starbucks Might Double Your Home’s Value” and “Starbucks Increases Neighborhood, Home Values”—all imply that the presence of Starbucks led to the increase in home value.

This story caught my eye for a number of reasons. While I’m not a coffee drinker, a new Starbucks just opened down the street (okay, about two miles down the street, so not that close) but if its presence further increased our home values that would be a plus. But more to the point, it drew my attention as a sociologist. The headlines seem to confuse causation with correlation, assuming that one variable had a direct impact on the other, rather than coinciding with a number of other factors that come along with the decision to open a new Starbucks.

Continue reading "The “Starbucks Effect”: Correlation vs. Causation" »

April 20, 2015

The "Boy Problem" of the Twenty-first Century

Tigonzales Angel Gonzales head shotBy Teresa Irene Gonzales and Angel Rubiel Gonzalez

Gonzalez holds a Ph.D. in Education from UC Berkeley and is currently a social studies teacher in New York City

In 2013, Christina Hoff Sommers wrote an op-ed for the New York Times which discussed the growing educational gap between boys and girls within the U.S. Sommers blames much of this gap on what she terms “misguided policies” that perpetuate an educational gender inequity that favors girls over boys. In order to create more boy-friendly classrooms, Sommers advocates for increased play time (recess), single-sex classrooms, and male teachers.

Continue reading "The "Boy Problem" of the Twenty-first Century" »

April 15, 2015

Getting a Job with a Criminal Record

Headshot 3.13 cropcompressBy Karen Sternheimer

In a competitive job environment, having a criminal record might effectively exclude someone from legal employment. For some jobs, it makes sense to exclude people who have committed specific offenses in the past. No one wants their cable installer to be a convicted burglar, their child’s teacher to be a sex offender, or their accountant to have committed forgery.

But for many people who have past offenses, the charges have less to do with their character than the communities in which they live. Check out this clip from Last Week Tonight, which examines how municipal fines like speeding tickets, parking tickets, and loitering charges can cause low-income residents to end up in jail if they can’t pay the mounting fines:

Continue reading "Getting a Job with a Criminal Record" »

April 13, 2015

Seeing Others as Us

Peter kaufman 2014By Peter Kaufman

In 2012, there were over 1,000 documented hate groups in the United States. A hate group is pretty much what it sounds like: a collection of individuals who come together based on their shared animosity toward others. Whether they focus on race, religion, sexual orientation, or nationality, these organizations mobilize around a clearly defined difference that they perceive to have with other people. Groups such as the Ku Klux Klan, Aryan Brotherhood, Westboro Baptist Church, and the neo-Nazi National Socialist Movement, use these differences not only as a basis of their hatred, but also to justify acts of hostility, aggression, and violence against those they deem to be “outsiders.”

Although most of us would acknowledge that the attitudes and actions of these hate groups are extreme, few of us are immune to engaging in similar but less severe forms of selective separation.  An example that many young people can relate to is the scene in the movie Mean Girls when Cady (Lindsay Lohan) is introduced to the seating arrangement of the various “tribes” in the high school lunch room.  Cady quickly learns that everyone sits with people who are deemed to be just like them: preps, nerds, Asians, Blacks, wannabees, burnouts, band geeks, etc.

Continue reading "Seeing Others as Us" »

April 08, 2015

Measures of Central Tendency

RaskoffBy Sally Raskoff

Have you taken a statistics course? Don’t wait too long if you are procrastinating. Mastering that material helps with other classes and in life.

One of the key concepts within statistics is measures of central tendency: mean, median, and mode. Each one tells us about how the data, for one variable or concept, cluster together although each are calculated differently.

The mean is the numerical average. You’ve probably already been calculating means —also known as averages. Add up however many scores or values in your data and divide by how many you have.

Continue reading "Measures of Central Tendency" »

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