August 2010 Archives

Urban canoeing: Cedar Lake to Minnehaha Falls and back

| No Comments

Chris Desjardins and I took another urban canoe trip yesterday. We originally planned to pick up where we left off last time, at Hidden Falls in Saint Paul, but we chose a different route to take advantage of recent rains that had swollen Minnehaha Creek and to try out my Wike Woody Wagon Canoe Trailer.

We left from Cedar Lake where my canoe, Scrappy, resides on a rack rented from the City of Minneapolis. After loading my bike, we paddled through Lake of the Isles to Lake Calhoun, portaged to Lake Harriet, and then portaged over to Minnehaha Creek. The creek was so high that we had to portage around or duck under several bridges. We used the bike trailer for the longer portages before the creek, and after reaching Minnehaha Falls, I transported Scrappy all the way back to Cedar Lake by bike. Click here for a map of our canoe route.

The entire trip took about seven hours, not counting a stop for dinner at Sea Salt Eatery. My bike took a beating from trees and a wall along the creek, the Wike trailer didn't perform as well as I had hoped, and I'm still exhausted the next day, but I thoroughly enjoyed the Cedar-Minnehaha-Cedar loop. I especially got a kick out of people's reactions to Scrappy hitched to my bike.

IMG_4091.jpg IMG_4097.jpg IMG_4109.jpg
IMG_4094.jpg IMG_4104.jpg
IMG_4106.jpg IMG_4114.jpg

Structural equation modeling for theory-driven evaluation

| No Comments

Just to show that I'm not the biggest slacker-blogger on the Web, I want to direct you to my guest-post on AEA365: A tip-a-day by and for evaluators. I chose my "tip" because I think structural equation modeling (SEM) and logic modeling would complement each other very well, but very few researchers have combined the two approaches. Those of us who use SEM know how important it is to have strong prior theory for model fit and valid conclusions. We could learn a lot from evaluators who are skilled at developing logic models. Conversely, theory-driven evaluators could improve their practice by carefully attending to statistical power, construct validity, attenuation due to measurement error, and the decomposition of total effects. I am very interested in hearing others' opinions on this issue, so please leave a comment here or at AEA365.

A logic model (left) operationalized as a partial mediation growth model (right)
AEA_Tip-a-Day_Logic_Model.png AEA_Tip-a-Day_Path_Diagram.png

I think I have settled on a dissertation topic: spatiotemporal piecewise regression evaluation. Spatiotemporal piecewise regression (SPR) refers to the analysis of longitudinal data from a spatial regression discontinuity (SRD) design with multiple pre-test observations. SRD offers a way to quasi-experimentally estimate local average treatment effects (LATEs) of geographically implemented programs or policies; SPR is a way to estimate change in LATEs over time. My dissertation will describe SPR methodology in detail, including validity threats, and demonstrate an SPR evaluation of an educational program. As discussed by Shadish, Cook, and Campbell, multiple pre-test observations will allow me to address validity questions.

I decided to conduct a preliminary SPR analysis of data from the educational program to determine if the topic would be feasible and to include some results in a conference paper proposal. As discussed in earlier posts (here, here, and here), Rlogo.jpg can be used to plot regression discontinuity fitted lines, showing the LATE at the treatment assignment cutoff point. The ggplot2 package can be used to plot SPR fitted lines and change in LATEs over time, but it's not easy. One reason is that ggplot2 has a steep learning curve and limits control over the legend's appearance, although its curve is not as steep as lattice's. Another reason SPR plots are difficult to produce is that they represent several dimensions: north/south/east/west (reduced to one-dimensional distance), time, and the outcome. The SPR plots below show that participation in the educational program is associated with an initially positive, small LATE that diminishes over time.

Spatiotemporal piecewise regression fitted line plots
spatiotemporal_piecewise_regression_LATEs.png spatiotemporal_piecewise_regression.png