For every class listed in the table below you will see certain readings listed. Please do these readings before the class for which they are listed, and come prepared to discuss them.
The citations for the readings are all listed below. In general, they can be grouped into the following categories:
For most of the basic materials in the class, we will be using two textbooks and four short monographs. Please note that the first two books cover much of the same material, but present it in very different ways for different types of audiences; depending on your interests and learning style you may find it helpful to rely on one as your "primary text" and just skim the other for additional depth, but the choice of which is up to you.
This is just what it sounds like: a straightforward text covering applied statistical methods from the point of view of a public or nonprofit agency, which is the closest thing the textbook world has to making a book specifically for planners. Sometimes the examples may seem more oriented to managers than planners, but it's basically the same sort of thing: how do we use statistics to help inform decision making? We will rely on this book for formulas and computational problems.
Unlike a standard statistics text, this book spends more time on (a) logic and reasoning and (b) problems with the way statistics and numbers get used in the media and the world of policy. It is decidedly skeptical in its approach; do not take this as the final word, but rather regard it as a helpful antidote to other overly-optimistic and uncritical approaches to the subject. It also has many examples drawn from the real world, which is something we are told from previous classes that you cannot get enough of.
This short book provides a classic introduction to the world of Exploratory Data Analysis (EDA), as pioneered by statistician John Tukey. Rather than rushing to statistical tests and regression analysis, EDA encourages the statistician to explore data first through descriptive summaries, plots, and other visualization techniques, to suggest both problems and relationships. This is a classic, used in many introductory courses.
Another monograph in the Sage series, this little book digs deep into a problem most others tend to avoid: teasing out what we mean when we talk about causality (as in "cause and effect"), and how to recognize it when we see it. (This book provides the best quotation in the whole course: "computers cannot substitute for sociologists in analyzing data, because computers do not know anything about the real world and sociologists do know a little bit.") Another classic.
This small pamphlet reprints chapter 2 from Tufte's larger work, Visual Explanations: Images and Quantities, Evidence and Narrative, so if you already have that, don't bother to buy this. Due to the importance of the visual quality of the material, we are including this as a "required book." It is a great intro to the endlessly-fascinating and graphically-stunning world of Edward R. Tufte.
The treatment of regression analysis in Meier et al. leaves something to be desired—it is more mechanical, and skips too quickly over questions of assumptions and interpretation of results. This short book was written for just this sort of class: a social science setting where students were not expected to learn everything about regression, but would need to know how to interpret work done by others and distinguish between meaningful results and junk science. Chapter five in particular gives a step-by-step critique of regressions presented in journal articles, very similar to what you will be asked to do (in part) in the second written assignment.
Articles and Individual Chapters
Beyond these books, most weeks include a few additional readings from other sources. Sometimes these will be a chapter in a book that covers a topic particularly well, or adds some interesting wrinkles to the standard treatment. Other times it will be an article or news item that demonstrates a particular concept, or gives a case for us to discuss. Remember: planners (and other professionals) spend very little time reading textbooks—most planning knowledge comes in the form of journal articles, case studies, publications of research findings, government reports, and other such sources. Typically these are less dry than text books, but they are also often written with a particular agenda or bias. Learning to read these sources in an open-minded but critical way is a real art, and an important part of a good planning.
This is a list of some recommended books that may be helpful. Some are more basic than the texts we are using, and some go into more depth on particular topics (e.g. graphs and charts; statistical software packages). Also many of the required readings represent individual chapters from longer books—these can be good sources for further study.
[Gonick] = Gonick, L., and W. Smith. The Cartoon Guide to Statistics. New York, NY: Collins Reference, 1993. ISBN: 9780062731029.
[Moore] = Moore, D. S., and W. I. Notz. Statistics: Concepts and Controversies. 7th ed. New York, NY: W. H. Freeman, 2008. ISBN: 9781429237024.
A really fun introduction at a slightly lower level than we needed.
[Rabe-Hesketh] = Rabe-Hesketh, S., and B. S. Everitt. A Handbook of Statistical Analysis Using Stata. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2006. ISBN: 9781584887560.
[Verzani] = Verzani, J. Using R for Introductory Statistics. Boca Raton, FL: Chapman & Hall/CRC, 2004. ISBN: 9781584884507.
For those students interested in using R instead on Stata.
The readings listed below in [square brackets] correspond to the books cited above.
|Week 1: Introduction|
|1||Course overview; epistemological foundations; math review|
|2||What's in a number?; basic numeracy; measurement|
[Meier], chapters 1 and 2.
[Horwitz], chapter 2.
Hodge, G. "Use and Mis-use of Measurement Scales in City Planning." J Am Plann Assoc29 (1963): 112-121.
Diamond, J. "How Cats Survive Falls from New York Skyscrapers." Nat Hist 8 (1989): 20-26.
"On Landing Like a Cat: It Is a Fact." New York Times, August 22, 1989.
|Week 2: Planning numbers; descriptive statistics|
|3||The use of numbers in planning|
[Horwitz], chapter 4.
Savas, E. S. "The political Properties of Crystalline H2O: Planning for Snow Emergencies in New York." Management Science 20 (1973):137-145.
|4||Variables; samples and populations; measures of central tendency|
[Horwitz], chapter 6.
[Meier], chapters 4 and 5.
Abrahamse, A. F. "Counting the Homeless: Sampling Difficult Populations." Chapter 3 in Public Policy and Statistics: Case Studies from RAND. Edited by S. Morton and J. Rolph. New York City, NY: Springer, 2000. ISBN: 9780387987774.
Chan, Sewell. "Remembering a Snowstorm That Paralyzed the City." New York Times, February 10, 2009.
|Week 3: Talking about distributions|
|5||Measures of variability|
[Horwitz], chapters 5 and 7.
[Meier], chapter 6.
|Week 4: Asking and answering questions with data|
|6||Exploratory data analysis and visualization|
[Hartwig], pages 5-31.
Start reading [Tufte]
Lewis, Michael. "The No-Stats All-Star." New York Times, February 13, 2009.
|7||Logic, experiment, and the scientific method|
[Horwitz], chapter 1.
[Meier], chapter 3.
|Week 5: Probability and the normal curve|
[Meier], chapters 7 and 8.
Robinson, W. "Ecological Correlations and the Behavior of Individuals." Am Sociol Rev 15 (1950): 351-357.
|9||The normal curve; sampling|
[Horwitz], chapters 8 and 9.
[Meier], chapter 9.
|Week 6: Inferential statistics|
|10||Estimates and confidence intervals|
[Meier], chapters 11, 12 (skim), and 13.
[Horwitz], chapters 10 and 12.
|11||The idea of a statistical test; non-parametric tests|
[Meier], chapters 12 and 14.
[Horwitz], chapter 11.
Zernike, Kate. "Do Polls Lie About Race?" New York Times, October 11, 2008.
|Week 7: Computers and data|
|13||Statistical software; data management||Zeisel, H. Say It With Figures. 6th ed. Harper & Row, 6th edition, 1985, chapter 4. ISBN: 9780061819827.|
|Week 8: Introduction to bivariate/multivariate data|
|14||Cross-tabulations; χ2 tests|
Start reading [Davis]
[Meier], chapters 15-16.
[Horwitz], pages 228-331 (review).
|15||Scatterplots; correlation; cause and effect; confounding variables|
[Meier], chapters 17-18.
[Horwitz], chapters 13-14.
[Hartwig], pages 31-79.
|Week 9: Simple regression|
Review readings from Session 14.
[Berry], chapter 1.
|17||The assumptions of regression analysis|
[Meier], chapter 19.
Chatterjee, S., M. S. Handcock, and J. S. Simonoff. "PCB Contamination of U.S. Bays and Estuaries." In A Casebook for a First Course in Statistics and Data Analysis. Hoboken, NJ: Wiley, 1994. ISBN: 9780471110309.
[Berry], Chapter 2.
|Week 10: Multivariate regression|
[Berry], chapters 3-5.
[Meier], chapters 21 and 23.
[Rabe-Hesketh], chapter 3.
|19||Review/slack; presentation and graphs|
[Horwitz], chapter 3.
[Tufte], chapter 2.
|Week 11: Particulars of planning data and questions|
MacDonald, H. "The American Community Survey: Warmer (More Current), but Fuzzier (Less Precise) than the Decennial Census." J Am Plann Assoc 72 (2006): 491-504.
Portney, K. E. "Taking Sustainable Cities Seriously. A Comparative Analysis of Twenty-four US Cities." Local Environment 7 (2002): 363-380.
Yang, Y. "A Tale of Two Cities: Physical Form and Neighborhood Satisfaction in Metropolitan Portland and Charlotte." J Am Plann Assoc 74 (2008): 307-323.
Baade, R. A., and R. F. Dye. "The Impact of Stadiums and Professional Sports on Metropolitan Area Development." Growth and Change 21 (2006): 1-14.
|Week 12: Dealing with dollars; making decisions|
|21||Talking about money|
Mohring, H. "Land Values and the Measurement of Highway Benefits." J Polit Econ 69 (1961): 236-249.
Muro, M. and R. Puentes. "Investing in a Better Future: A Review of the Fiscal and Competitive Advantages of Smarter Growth Development Patterns." Center on Urban and Metropolitan Policy, Brookings Institution, 2004. (PDF)
|22||Decision trees, expected utility, cost-benefit analysis|
[Meier], chapters 25-26.
Blogging the Stimulus, a blog by Steve Coll of The New Yorker on issues of public policy.
|Week 13: Review; slack; additional critical thinking|
|23||Predictions and uncertainty; representing risk|
Clery, D., and A. Cho. "Large Hadron Collider: Is the LHC a Doomsday Machine?" Science 321 (2008): 1291.
Moore, T. "The Use of Forecasts in Creating and Adopting Visions for Regional Growth." Chapter 2 in Engaging the Future: Forecasts, Scenarios, Plans, and Projects. Edited by L. Hopkins and M. Zapata. Cambridge, MA: Lincoln Institute of Land Policy, 2007. ISBN: 9781558441705.
Reamer, A. "In Dire Straits: The Urgent Need to Improve Economic Statistics." Brookings March 4, 2009.
|24||Return to research design; review||[Meier], chapter 3 (review).|