On normal and justice.

When I was in high school, I remember an evening at a friend’s house. This friend, and her family, were Good People(tm) by all definitions I knew at the time – they were kind and charitable, they attended and supported their church, they lived comfortable but not extravagant lives. This one evening I remember distinctly my friend’s father bursting out with a torrent of vitriolic hatred toward blacks – I have no memory of what prompted the outburst, only that the words he used to describe black people were deeply ignorant, dehumanizing, and offensive. I was shocked and afraid beyond words and lacked that knowledge or capacity at the time to respond at all, much less to counter his bigotry and hatred. But I don’t think I ever went back to their house, and I know I’ve never forgotten the hateful screed I witnessed that night.
It’s that memory that keeps coming to mind these past few months, these past few days. Racism, hatred, bigotry, and irrational fear are bred into the very fabric of our white society. It lurks in “normal”, in the tacit understandings of our neighborhoods, in our churches, in our schools, in our town halls and police forces. It’s “the way things work”. It’s “trust the system” and “if only they understood that blocking traffic doesn’t help their case.” It’s “they should protest by donating their money to make their lives better, not by disrespecting the flag.” It’s when black people’s lives are less important than white people’s inconvenience, when black people silently protesting violent, institutionalized racism causes white people’s discomfort and complaint.
I don’t have grand answers. The only answers I have are small, infinitesimal, grains of sand on the beach, teaspoons in the ocean. The only answers I have are these: Look at what you think is normal, at what you teach your kids and grandkids, nieces and nephews, chosen family, friends and strangers is normal. Look at what lies underneath that normal – how you shape it with your words, your actions, the choices you don’t consciously make. Understand that the difference between “them” and “us” is something we created to justify creating a normal that benefits us more than them. Maybe not consciously, not by everyone who worked to create and maintain our normal – racism and bigotry and hatred are not newly sophisticated and clean-cut – maybe not intentionally by you but absolutely, without doubt, the patterns of systemic racism have been incorporated into the foundations and fabric of our realities for hundreds of years. And whether or not we were complicit in creating the normal we enjoy, we benefit from it. Try to start seeing it. And when you do, when you start to recognize it and it starts to niggle at you, point it out to other people. Talk about it with your families, show them how maybe normal isn’t “just the way things are”. How normal isn’t just at all.

DAT Assignment 2: Chi Square

(For those of you looking for something more interesting, go check out the pictures I took walking around my neighborhood after the blizzard in DC this weekend.)

Following completion of the steps described above, create a blog entry where you submit syntax used to run a Chi-Square Test (copied and pasted from your program) along with corresponding output and a few sentences of interpretation.

The rest is past the jump.

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I was born and raised and spent a good chunk of my adult life living in the upper Midwest, where it snows regularly and not-infrequently in large quantities. Naturally, I have a number of friends and family who still live there, and unfortunately, apparently also naturally a fair number of them seem to think there’s something they have to feel smugly superior about in watching the mid-Atlantic react to this weekend’s snow. So.. as someone who’s lived in both places, allow me to clarify why that behavior is not only annoying, but also fundamentally flawed and illogical.

If you live somewhere where it snows frequently, it makes sense to build your infrastructure in ways that will withstand sometimes large volumes of snow. This means your city planners were likely intentional about building wider streets and highway shoulders – so that when it snows and the plows come through, there’s room for the resulting snow to be piled up without impinging on traffic. Similarly, your power lines are more likely to be underground, where accumulated ice and heavy snow would be less likely to cause outages. It also makes sense for your cities, counties, and states to invest in sufficient equipment to safely and quickly respond to snow – adequate vehicles to pre-treat all major and most minor roads, enough plows so that even during blizzards they can make passes through most streets multiple times. Because if you live somewhere where it snows frequently, *not* doing those things would be irresponsible, both in terms of health and human safety, but also in terms of economic impact on the community.

If you live somewhere where it does not snow frequently, somewhere where in the last three and a half years (as long as I’ve lived in DC) it’s snowed *at all* less than a dozen times and snowed a couple inches at once only one or two times, it does not make sense to build your infrastructure with snow removal in mind. It would be fiscally irresponsible, in fact, for cities, counties, and states to invest in large volumes of equipment for the once-every-half-decade snow storm where they might be justified. It would be more expensive – taking money out of public budgets that could and should be spent on other things more likely to have direct impact on health and safety and economic prosperity – to insist that all power lines be underground, or that all streets be wide enough to accommodate snow banks.

Therefore, when that once-every-half-decade snow storm happens, yes, those places where snow is not a regular occurrence will not be able to respond as efficiently as those places that know and regularly expect snow in large quantities every year. There’s nothing inferior about the preparation or governance of those places that do not regularly experience snow. The people who live there are not inherently stupid, though they may be ignorant of how to drive in so much snow or how to ensure that their home is prepared for what may be several days without the ability to restock *because* it’s not a normal occurrence. Their governments are not overreacting when they close things down for several days to give adequate time and less-trafficked roads to the crews who are working diligently to respond to an abnormal weather event; they’re being responsible and working to keep everyone safe.

Institutional Locus of Control

Those of you in the higher ed world will likely have heard of Florida governor Rick Scott’s invitation to the state’s colleges and universities to a meeting tomorrow with the intent to “challenge” them to 100% post-collegiate employment rates for their Psychology majors. (If you haven’t, Inside Higher Ed wrote it up today.) This prompted some lamenting in higher ed circles, which is not surprising. My thoughts (many of which were posted originally in response to an email about the article linked above on the ASSESS listserv) are below.

Rather than focus on the historical roots and purpose of higher education, I have to wonder if there’s not more of a role our business partners can play (or we could ask them to play) in helping change legislator perspectives about what they need for today’s jobs. It seems that there’s an unhealthy focus on major field of study as the be-all-end-all for determining a graduate’s career success or failure, when numerous surveys of employers tell us that’s not actually what employers care about. For example:

  • AAC&U released their latest update to their employer survey about a year ago and found that “[n]early all employers (91%) agree that for career success, ‘a candidate’s demonstrated capacity to think critically, communicate clearly, and solve complex problems is more important than his or her undergraduate major.’” (Emphasis in original.) (This finding was quoted again today, in fact, in an op-ed in Maine that crossed my Twitter feed just now.)
  • A similar finding was part of a recent (arguably – 2012) Chronicle of Higher Education employer survey (PDF): “Employers place more weight on experience, particularly internships and employment during school vs. academic credentials including GPA and college major when evaluating a recent graduate for employment.”

There are coalitions of business leaders becoming more active in advocating for better state support of higher education – there’s at least one in Florida called The Florida Council of 100 – though I’m not sure of the agenda of any of them and whether they’re intentional acting on behalf of institutions or higher education as a whole or not. Still, it seems like rather than higher education continuing to beat the same drum, it may be time for a change of tactics and partners to more directly respond to policymakers’ arguably well-meaning but ultimately ill-informed demands.

In addition to enlisting new partners, the academy as a whole could work to raise the visibility of work intended to broaden the focus of higher education outcomes beyond just employment and wages. I’m most familiar with the Post-Collegiate Outcomes (PCO) Initiative completed by the American Association of Community Colleges (AACC), the American Association of State Colleges and Universities (AASCU), and (my employer*) the Association of Public and Land-grant Universities (APLU) last year, which resulted in the PCO Framework and Toolkit designed to help institutions intentionally broaden the conversation about outcomes with their stakeholders. (Full disclosure: I was integrally involved in the PCO Initiative.) The Lumina Foundation recently (and without a lot of fanfare, unfortunately) released It’s Not Just the Money: The Benefits of College Education to Individuals and to Society, which uses data from national sources to document both the financial and human capital benefits to both individuals and their communities from a more highly education population. I also know that there are several institutions who are – as part of their mission – intentionally working to promote the civic engagement outcomes of higher education; James Madison University comes to mind as well as the institutions that are members of The New American Colleges & Universities, which held an event this morning at the National Press Club in DC presumably to engage policy-makers and DC-based think tanks more directly in promoting these outcomes.

There’s still undoubtedly a lot of progress to be made, but I think there are also a lot of potential partners to work with to push back on some of the too-narrowly-focused ideas we’re seeing.

* As always, the opinions expressed in the blog are my own and do not necessarily reflect those my employer.

DMV Assignment 3: Managing Data

Once you have written a successful program that manages your data, create a blog entry where you post your program and the results/output that displays at least 3 of your data managed variables as frequency distributions. Write a few sentences describing these frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.

More after the jump..  Continue reading

DAT Assignment 1: ANOVA

(For those following along, this is the second course I’m taking to learn SAS. It’s designed to be taken after the first course, but as I’m already familiar with the statistical concepts in both courses, I’m taking them concurrently. That does mean, however, that the order of posts will be somewhat jumpy as I’ll be posting assignments for ANOVA before completing the full set of descriptive analysis required for the first course.)

Create a blog entry where you submit syntax used to run an ANOVA (copied and pasted from your program) along with corresponding output and a few sentences of interpretation.
My responses after the cut.

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DMV Assignment 2: My first SAS Program!

Assignment instructions: Following completion of your first program, create a blog entry where you post 1) your program 2) the output that displays three of your variables as frequency tables and 3) a few sentences describing your frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.

(Apologies for those of you not interested in reading raw SAS code, but for those of you who are, I’m open to suggestions for how to improve. This is fairly basic code, but useful for someone who’s never used SAS.)

Behind the jump for all the details.. Continue reading