We Shall Die Before We Kill

So I have started reading The Moral Imagination by John Paul Lederach, and in the second chapter he tells the story of Colombian peasants caught in the middle of the conflict between guerrillas and the Colombian army.  Put in a position to join a side or risk death, they came together and decided, "We shall die before we kill."  And they developed the following principles by which their actions would be guided.  I read this and thought, "This is responding in the opposite spirit.  This is the way of Jesus."

1. Faced with individualization: solidarity.
2. Faced with the Law of Silence and Secrecy: Do everything publicly.  Speak loud and never hide anything.
3. Faced with fear: Sincerity and disposition to dialogue.  We shall understand those who do not understand us.
4. Faced with Violence: Talk and negotiate with everyone.  We do not have enemies.
5. Faced with exclusion: Find support in others.  Individually we are weak, but together we are strong.
6. Faced with the need for a strategy: Transparency.  We will tell every armed group exactly what we have talked about with other armed groups.  And we will tell it all to the community.

Peace Paradigms: DONE!

It's that time of the semester... the time when you conspicuously do not see grad students out on the weekends, and plans for social events are always accompanied by the caviat 'after the term paper/presentation/final.'  But today marks a significant day.  I turned in my paper for Peace Paradigms--the LAST assignment due in that class--meaning I have officially finished the work for my first grad school course!  This past week has been non-stop (well, in many ways, the past month has been non-stop), but today is a happy day.  I get a breather now.

Tonight I have my presentation in my Quant class (I was quite stressed that it was on the same day my term paper was due, but now that I've dealt with that, it's nice that the work is done!), and then I will be able to relax a bit this week.  The bulk of this semester's work is done.  One more term paper (not nearly as much work as the last one), one more book to read, a short reaction paper, and a final, and on December 9th, I will be celebrating the END of the semester.

Peace Paradigms: check.  One class down, two more to go... :-)

The Failure of '5 Frequent Failures'?

Granted, we never really got off to a strong start.  Two of the original five members of our YouTube channel never posted a video at all!  So you could say we failed right from the beginning.  But now, for the first time since our launch in August, the videos have ceased.  It looks as though '5 Frequent Failures' may have officially failed.

Many of you have been watching my weekly Monday videos faithfully, and may not have even realized they were part of a YouTube collaboration channel with my dear friends Derek and Becky.  What is a collaboration channel?  Well, the intention was that we would have 5 people each take a day of the week.  On your day, you would make a video, talk a little about your life, and ask a question of the other 4 people.  In addition, you would answer the 4 questions the others had asked.

Well, our channel has been through its ups and downs.  Right off the bat, we lost Steph and Rory (the previously mentioned two who never posted videos at all), leaving us without a Thursday and Friday.  We briefly gained Jen (Friday girl), but after only one week, she had multiple difficulties with video cams, YouTube, and the Internet, and eventually gave up and quit.  Derek, Becky and I, we pressed on, and continued posting videos while trying to recruit people to our channel (which proved surprisingly difficult).  But then disaster struck.  The Internet at FPU wouldn't allow Becky to post videos.  She tried for several weeks--even traveled off campus to post--but the burden proved too large, and after 3 weeks of no postings, it seems she has admitted defeat.  Derek and I, we tried to persevere.  We kept on recording videos for a few weeks; but in the end, it just wasn't enough momentum to keep us going.  No video from Derek last week meant no video from me this week... the channel has come to a halt.

We have discussed it.  We don't want to give up.  We love our channel.  But we are discouraged. Just the two of us, we can't do it alone.  And so it seems, we have failed.  Unless... unless you want to help us.  Do you want to see '5 Frequent Failures' succeed?  If you want to keep enjoying my Monday videos, consider becoming our Wednesday, Thursday, or Friday!  It would be grand!  Then we would not have to admit defeat, our channel would live on, and we would all have a ton of fun in the process. :-)

The Role of Apology in Reconciliation Processes

This morning I attended another event at the United States Institute of Peace (USIP) regarding the role of apology in reconciliation.  The event began with a screening of  the film, Apology, a documentary about 'Sorry Day' in Australia.  On February 13 of this year, the Australian Prime Minister issued a public apology to the aboriginals of Australia for the atrocities committed against them in the past.  Watch the trailer.  It's stunning.

If only there were more politicians like Kevin Rudd.  Politicians who aren't afraid of appearing 'weak' by offering apologies that are rightfully deserved.  Politicians who understand that the only way forward is to deal with the past.  Politicians who understand the power of history, who understand that history lives in the present.  Politicians who are humble enough to say, 'I'm sorry.'

I appreciate what the woman in the video said: Apology is not a panacea.  It's not the end, nor the total solution.  But it is a powerful symbol.  Good faith apologies are not just empty words, but acknowledgments of history, of wrongdoing, of regret.  Apology is a first, but necessary, step in the healing process.  Apology acknowledges truth, and in doing so, creates the potential for a release of forgiveness and mercy.  It cannot right the wrongs of the past, but it opens the door for shared responsibility for the future.

Apology is disarming.  It stops the cycle of defensiveness and justification.  It is a symbol that moves in the opposite spirit of anger, bitterness, competition, and self-righteousness.  It is appropriate at all levels--individual, community, intergroup, and interstate.  Apology sets us on the path to reconciliation, the key to sustainable peace.      

Nanny Lisa, AKA Mary Poppins without the Magic Carpetbag

Most days, I am thankful for my job.  I mean, how hard is it really to drive kids around and help them with homework?  20 hours of nanny work a week for free rent (& utilities!) is a steal, I know this.  But there are some days when I think: I will never have children.  The things that drive me up the wall:

1. Complaining.  I don't want to walk to the car.  I don't want to read.  I don't want to do my homework.  I'm bored.  I'm too tired to do that.  I don't want that for dinner.  Why did you take so long?  Why do we have to wait?  Why can't I have candy for my snack?  The constant, dissatisfied, whiny tone takes its toll.  
2. Uncooperativeness.  Having every request met with some argument, some rationale for why it doesn't have to be done.  When instructions are intentionally interpreted literally to avoid cooperation, so I have to give increasingly specific directions to avoid the argument 'I'm doing what you asked!'  Or, just a simple 'No.'  When every little thing is a struggle, I want to pull my hair out.
3. Bickering.  Siblings have a way of getting on each other's nerves that must grate on parents' nerves the most... or at least the nanny's.  I could handle a blow-out fight every once in a while, but the bickering and nagging is like nails on a chalkboard.    
4. Yelling.  Loud noises generally, expressing any emotion.  I don't like them.

The kids I nanny are good kids.  Really, they are.  Most of the time, we don't have problems, and even have fun!  But some days... Some days they are quite a handful, and I think, I'm not cut out for this.  Leave me to the world of rational adults, please!!

A Sucker for Stars

Historic!

I will never, ever forget November 4, 2008.  I am convinced I will never experience an election this monumental again in my lifetime.  It was spectacular.  Incredible.  I am so proud of America.  I am so proud that Barack Obama will be my president in 2 short months, the first African American president in our history.  

I know not everyone is as proud as I am, but regardless of your views, you have to admit: magic was in the air last night.  Magic, at least, was in the streets of DC--a city that went 93% to Obama.  For an Obama supporter, there wasn't a better place in the nation to be.

I went to an election watch party with some friends, and we eagerly watched and cheered as polls gradually closed from east to west across the country, and states were called.  Every state called for Obama was met with resounding shouts of "O-BA-MA!" and "YES WE CAN!" as people yelled and hugged and high fived and toasted.  The electoral votes went up: 69, 123, 207 as Obama garnered swing states like Ohio, Indiana, Florida, and Virginia.  The atmosphere was electric as we waited in anticipation for what we all hoped was the inevitable.

I don't think any of us expected it to happen as quickly as it did.  As 11pm approached, we counted down to polls closing on the West Coast.  "5... 4... 3... 2... 1...!" we shouted together. And then seconds later, it flashed across the television screen: "BARACK OBAMA ELECTED PRESIDENT OF THE UNITED STATES."  The place erupted with screams and shouts of elation!  We couldn't believe our eyes!  And then within minutes, McCain conceded and it was official.  History had been made.  Barack Obama had been elected president.  The District went wild.  On every street everywhere people were celebrating.  I laughed, I cried, I hugged, I texted, I called (YES, I called!  I called EVERYONE!  Thanks to those of you who answered and listened to me shout that WE DID IT!  WE WON!!).  Cheers of "Yes We Can" changed to "YES WE DID!"  Yes, I will remember November 4, 2008, for the rest of my life.  

Today I was talking to a classmate of mine from Egypt, who did not get to vote, but helped work the polls all day.  He told me, "America won yesterday.  Congratulations."  It made me so proud.  America, we won.  Yes, we did!

Hating on Happiness?

SIS 600: Quantitative Analysis

Statistics, essentially.  And sometimes, I feel, the bane of my existence this semester.  And if you talk to anyone else in my program, most of us agree on that point.  We are peace-loving, theory-loving, practical conflict resolution-loving people, not numbers-loving people.  We take this class because we have to, and most of us hate it the entire way through.  I admit I see the value in it, but I will not be sad when I can say I'm done with this class!!

Tonight is the midterm (actually kind of 2/3-term) and this past weekend my life has basically been devoted to studying for it.  On Saturday, I went to a cafe with free wireless internet and spent 8 hours working.  5 of those hours were spent on ONE, count them, ONE Quant homework assignment, which I, incidentally, did not even finish.  Sunday I studied more, this morning I continued, and I think I can say I am as prepared as I'm going to be.  

I know it's unlikely that this will be interesting to any of you.  But since the purpose of my blog is to give you a small window into my life, I thought I would subject you all to a little of the pain I've been feeling the last few days.  Thus, a portion of my statistics homework :-)  Enjoy!!

In your own words, define and explain each of the following terms and concepts:

I-R Measure of association/correlation  r statistics. Application, interpretation and limitations of the  r statistics The Pearson’s r measure of association is used to standardize the relationship between interval-ratio variables based on the computations from the least-squared regression line.  It measures the strength of the association between two variables on a scale of 0 to plus/minus 1.  A Pearson’s r value of 0-.3 shows a weak association, .3-.6 shows a moderate association, and .6-1 shows a strong association. If the value is positive, there is a positive relationship (the variables vary in the same direction).  If the value is negative, there is a negative relationship (the variables vary in opposite directions).  The interpretation of Pearson’s r is fairly arbitrary, so the coefficient of determination was developed, which is r squared.  This is a PRE statistic, meaning it tells us the percentage by which our error in predicting the dependent variable will be reduced taking into account the independent variable.  Pearson’s r is a very helpful and sophisticated statistic, but it can only be used with interval-ratio variables (though it is sometimes used with ordinal variables and can be adjusted to use rarely with nominal variables—but the results are less accurate).  Pearson’s r statistics also assume a linear relationship between the variables.

I-R Measure of association/correlation  R and R squared statistics. Application, interpretation and limitations of the R and  R squared statistics.  R and R squared statistics are used to show the combined effects of all independent variables on a dependent variable (measured at the interval-ratio level).  Because even independent variables are interrelated, we cannot simply add the r squared statistics together to determined the combined effect on the dependent variable.  R squared tells us the percentage of the total variance in the dependent variable that can be explained by the dependent variables combined.  R and R squared statistics can only most accurately be applied to interval-ratio variables.  These statistics are very powerful, but require high-quality data and assume that each independent variable has a linear relationship with the dependent variable.  They also assume no interaction among the variables in the equation and that the independent variables are uncorrelated with each other.  As these assumptions are violated, the statistics become less trustworthy.

Similarities and difference between, R,  R squared statistics and  r statistics. R statistics and r statistics are similar in that they both measure the association between variables at the interval-ratio level, and can provide us with the percentage of variance in the dependent variable that is explained by the independent variable(s).  The difference is that r statistics measure the effect of ONE independent variable on a dependent variable, and R statistics measure the effect of MULTIPLE independent variables on a dependent variable.

Ordinal measure Gamma. Aplication, interpretation and limitations of the  Gamma statistics Gamma is used to measure the strength of association and direction of two ordinal level variables.  Gamma is measured on a scale of 0 to plus/minus 1, with values from 0-.3 showing a weak relationship, .3-.6 a moderate one, and .6-1 a strong relationship.  If the value is positive, there is a positive relationship (the variables vary in the same direction).  If the value is negative, there is a negative relationship (the variables vary in opposite directions).  When evaluating the direction of relationship, it is important to pay attention to the way categories are coded.  Since these are ordinal level variables, categories can often be scored in two different ways, both of which are equally valid.

Ordinal measure Spearman’s Rho. Aplication, interpretation and limitations of Spearman’s Rho Spearman’s Rho is used as a measure of association between ordinal level variables when there is a broad range of scores and the researcher does not want to collapse them into categories that could be used to compute gamma from a bivariate table.  Spearman’s rho permits the retention of detail that can be lost when collapsing scores into categories such as “high” and “low.”  Instead of putting scores into categories, they variables are ranked in order from highest to lowest, and then the ranks for each case on each variable are compared with each other.  The computation of spearman’s rho is an index of the strength of association between the two variables on a scale of 0 to plus/minus 1, with values from 0-.3 showing a weak relationship, .3-.6 a moderate one, and .6-1 a strong relationship.  If the value is positive, there is a positive relationship (the variables vary in the same direction).  If the value is negative, there is a negative relationship (the variables vary in opposite directions).  If the value of Spearman’s rho is squared, it provides us with a PRE statistic.  Spearman’s rho can only be used with ordinal level variables that can be ranked from highest to lowest for each case.

Slope, Intercept, Least-Squared Regression Line. Interpretation of the regression line The slope (b) of a regression line tells us the unit change in Y (dependent variable) caused by a one-unit change in X (independent variable).  The Y-intercept (a) can be calculated once the slope has been calculated using the formula Y = a + bX.  This tells us the point at which the regression line crosses the Y-axis.  This formula is the formula for the least-squares regression line, the line that comes as close as possible to touching all conditional means of Y.  The regression line tells us the strength and the direction of the relationship between X and Y.  When all the cases are plotted on the graph (scattergram), we can see how closely they are clustered around the regression line.  The closer they are to the line, the stronger the relationship between X and Y.  If the regression line rises from left to right, the relationship is positive; if it rises from right to left, the relationship is negative.  The regression formula can also be used to predict the value of Y based on a value of X that was not included in the data.  The regression line can only be used with interval-ratio variables.

Multiple Least-Squared Regression Line. Partial slope. The least-squares multiple regression line is a modified least-squares regression line that includes more than one independent variable.  Partial slopes show the amount of change in Y for a one-unit change in an independent variable while controlling for the effects of the other independent variables.  They represent the direct effect of the associated indpendent variable on Y.  This regression line can also be used to predict the scores of the dependent variable based on scores of two or more independent variables.

Monday Video on Friday

Life has been crazy... that's why this is late.  At the moment I'm studying for my Quantitative Analysis midterm and trying to stay sane!