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What statistical analysis should I use?

The following table shows general guidelines for choosing a statistical analysis. We emphasize that these are general guidelines and should not be construed as hard and fast rules.  Usually your data could be analyzed in multiple ways, each of which could yield legitimate answers. The table below covers a number of common analyses and helps you choose among them based on the number of dependent variables (sometimes referred to as outcome variables), the nature of your independent variables (sometimes referred to as predictors).  You also want to consider the nature of your dependent variable, namely whether it is an interval variable, ordinal or categorical variable, and whether it is normally distributed (see What is the difference between categorical, ordinal and interval variables? for more information on this).  The table then shows one or more statistical tests commonly used given these types of variables (but not necessarily the only type of test that could be used) and links showing how to do such tests using SAS, Stata and SPSS.

Journal of Planning Education and Research, Vol. 25, No. 1, 43-56 (2005)
DOI: 10.1177/0739456X04270244
© 2005 Association of Collegiate Schools of Planning
Spatial and Transportation Mismatch in Los Angeles
Paul M. Ong

UCLA's Ralph and Goldy Lewis Center for Regional Policy Studies

Douglas Miller

Institute for Social and Economic Research and Policy at Columbia University

This article compares the impacts of spatial mismatch (the geographic separation of workers and jobs) and transportation mismatch (the lack of access to a private automobile) on neighborhood employment-to-population ratios and unemployment rates. The study uses tract-level data for the Los Angeles metropolitan area. The analysis uses an instrumental-variable approach to correct for the simultaneity of employment and car ownership. Results indicate that transportation mismatch is the more important factor in generating poor labor-market outcomes, particularly for disadvantaged neighborhoods. Areas with relatively more jobs increase female employment rates but not male employment rates. On the other hand, lower car ownership rates significantly decrease the employment ratio and increase the unemployment rate for both sexes.

Key Words: employment • unemployment • poor neighborhoods

Are London-Style Traffic Charges
The Answer for U.S. Congestion?
January 18, 2007

Crammed roadways and rush-hour traffic once were only problems in major U.S. cities such as New York and Los Angeles, but traffic snarls are becoming a growing problem for more cities. The number of metro areas where rush-hour travelers spend more than 20 hours per year stewing in traffic grew from a mere five in 1982 to 51 in 2003, according to the most recent report from the Texas Transportation Institute.

Traffic policies have long focused on road building. But some now argue that opening toll-based express lanes or instituting extra fees for rush-hour drivers -- as London did in 2003 -- may drive people toward public transportation and make commutes more efficient. Are there unintended consequences of such policies? And how big a problem is congestion if -- in the end -- commuters still are willing brave the morning rush?

The Online Journal asked economists Peter Gordon, of the University of Southern California, and Matthew Kahn, of the University of California, Los Angeles, to discuss the costs of traffic congestion, the problem it poses -- or doesn't pose -- for cities and how policy options such as London's traffic congestion charges might play on this side of the pond.

Williams v Weisser, 273 Cal. App. 2d 726

     This case from 1969 is the second, and by far the most famous, common law decision to establish the concept of an ‘academic exception.’ 

     The case concerns B. J. Williams a professor at UCLA and Edwin Weisser a man who had a business selling class notes to UCLA students.  Weisser hired a student to attend Williams’ class and using the notes the student took created a product that he sold to other students.  Williams sued Weisser to stop him from doing this saying that as the owner of the lecture notes he had the right to decide when and how they were published.  Weisser disagreed saying that the notes were a work for hire and therefore the university owned the rights and Williams had no grounds to sue.  UCLA produced a letter they had sent to all professors saying that they did not make a claim to own any of the professor’s lecture notes.  The courts eventually ruled that Williams did own the rights to his notes and thus Weisser was in the wrong.

     This case is one of the clearest cases establishing the academic exception.  There is no other claim for Weisser other than that the notes are a work for hire.  When both the employer and the employee deny that and say that the very notion of a university having claim to the copyright of their employees lecture notes is unecessary, then they have established the idea of the academic exception very strongly.

     There are downsides to this case with regards the Mauro v Allentown case.  First, the case decided the academic exception at the university level and did not address the high school level.  However it does establish it for class notes and the Irish medley is a classroom aid similar to a set of class notes, produced by the instructor, and not necessarily essential to the teaching of a class, only beneficial.  Second, and more importantly, the case was decided prior to the implementation of the 1976 Copyright Act, which clearly defines the work for hire clause.  That definition seems to allow no wiggle room for the academic exception.  This means that other decisions would be needed to extend and further establish the academic exception.