Quantitative reasoning & statistical methods for planners i

Lectures: 2 sessions / week, 1.5 hours / session

Recitations: 1 session / week, 1 hour / session

Planners use numbers, and planners use reasoning. The overarching goal of this class is to make sure that each and every student is comfortable and skilled at using quantitative information and sound reasoning to address the problems and questions they encounter in planning, design, and policy-making contexts. As with your other classes, students are expected to approach this course with the characteristic blend of ambition and skepticism that defines the Department of Urban Studies and Planning approach to planning: that is, we expect you to be energetic and creative in your application of the skills you will learn in quantitative reasoning, statistical methods, and the presentation and visualization of complex information, but also to be critical of these methods where appropriate, questioning whether the Modern Age's confidence in statistics—the prevailing faith in "hard numbers," "scientific accuracy," and "dispassionate logic"—may at times be overstated or unjustified.

Much is made of the distinction between quantitative and qualitative approaches. Fortunately, planning is a field where you will be able (and expected) to master both, so we don't need to waste a lot of space here with rhetorical debates about which is "truer," or better, or more persuasive. But for the time being, for this class, we need to concentrate on the quantitative aspects of the field (hence the "Q" in this particular "QR"); we will be concerned with things that can be measured, compared, and analyzed with regard to scale, size, variation, frequency and distribution, degree, and proportion. We will also be concentrating on the differences between observed samples and entire populations, and using statistical tools to distinguish between meaningful differences and random noise. That said, it may be worth meditating on the possibility that from an existential perspective, things in the world are not really either quantitative or qualitative—these words refer to ways we approach these things (or events, phenomena, ideas—whatever). To use a hackneyed old example, consider a tree in the forest: it is neither quantitative nor qualitative—it is just there, being tree-ey. These aspects of the tree only come out in relation (or perhaps in reaction) to our observation and discussion of it: "how tall is it?" "how old?" "is it healthy?" "is it pretty?" "what's it good for?" Depending on how we want to answer these questions, we may choose more quantitative or more qualitative methods. Interestingly, which approach will be more helpful for which questions is not always obvious.

Most of the implications of this line of thinking are far beyond our purposes here, but it does help to point to the artificiality of the distinction, and may help us see these two types of approaches more as a continuum and less as two unreconcilable world views.

The second half of "QR" stands for "Reasoning." As you begin to prepare your mind for the course, please remember that this is not just a statistics course. The quantitative part—dealing with gathering, analysis, and presentation of numbers—is certainly a key aspect of the class. But equally important will be developing skills in reasoning: making and critiquing arguments; stating and investigating hypotheses; avoiding bias in your own work and identifying it in the work of other challenges that have more to do with logic and clear-thinking than with numbers and data per se.

In the first few weeks of the class, we will touch on the difference between knowledge and belief: a belief may be true, but only when it is justified and explained can one be said to possess true knowledge. Importantly, the need to insist upon this higher standard is all the more crucial when we are working to develop knowledge of tools (such as logic, quantitative reasoning, research design, and statistical methods), as these can then form a foundation to build further (justified and explained) knowledge; if the foundations are shaky, you will never be able to trust the upper floors.

I would like to challenge all of you to develop actual knowledge concerning the material we cover—that is, to learn the methods that we think work for this or that purpose and to understand why we use them. Not everyone agrees with this level of intellectual rigor, and to be honest, others may be right: it might be a waste of your time, and you can probably get by fine in planning or even academic research by just treating quantitative methods as a series of recipes to use as dictated by the textbooks and other experts. (Of course, it is extremely difficult for an outside observer to know the difference between the two, as rote memorization of facts and blind acceptance of the formulas in the text will still get you the right answers; therefore, whether you choose to pursue true knowledge or just faith in the experts is largely up to you...)

Finally, I should mention that in addition to being useful towards meeting your professional goals, Quantitative Reasoning is also very fun, and can be meaningful from a philosophical perspective. We'll talk about this a little in class, and I hope you find yourself inspired you see the beauty and wonder in it all.

You need to:

Your grade for the class will be based on the following allocation:

Although it should go without saying, you are expected to attend lectures. Beyond this, you are expected to participate. Rarely (I hope never) will the entire hour-and-a-half be spent as a lecture—we will have a group discussion, pursue interesting or meaningful sidetracks, listen to guest speakers and student presentations, and even occasionally play games.

In addition to the two "lectures" each week, the course includes a one-hour weekly recitation session. These are mandatory (and your active participation will be even more essential in these smaller groups). Some weeks will be spent in review, especially as we get closer to the exams or when we are dealing with the more computational material; other weeks will be more discussion-oriented, as we drill down into closer readings of particular studies of articles.

Four times throughout the semester you will have lab sections instead of recitation sections. This will give you the opportunity to become familiar with a statistical software package (either Stata or R). We will use software to explore concepts and methods we covered in class a week or two earlier so you will already be familiar with the basics (and hopefully the introduction of the computers will illuminate your grasp of the fundamentals).

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