This week marks a major transition period in our project. We are now shifting from surveying to analyzing. Now that we have collected multiple employee surveys we can begin the process of seeking out latent patterns for PATMA. In terms of people surveyed, we have not had as much success finding additional employers to interview this week. So far we only have three employers (plus one pending). We met with our project partner, Adina Levin, this evening to discuss next steps in our project and how best to prepare for the March 3rd TMA meeting. We mentioned the number of surveys we had conducted. After emphasized our concern over the dismal number of employers, Adina told us to keep trying but that given the time we have, we may need to stick with whatever data we obtained. Our focus now is to start analyzing the data.
During the survey process we discovered some surprising trends. Almost all of the employees who drive to work don’t pay for parking and must move their cars every two hours. Part of our final deliverable is to propose ideas for marketing to employees and employers. The two-hour parking dance can definitely win over some drivers since re-parking can be such a hassle. We also noticed that small business employers were least receptive to our survey. The most cited anecdotal evidence was that they didn’t feel like the program could make a difference to their employees. This insight surprised us a bit since the subsidy program is designed for smaller business who may not have the resources to offer transit subsidies. We made sure to discuss this surprising trend with Adina this evening. These are two of the major trends we have seen so far. Next week, once we delve deeply, we are sure to uncover some more.
We are making progress in the analysis of the Redwood City data. We have compared all of the Redwood City surveys and reports to the Downtown Palo Alto Mode Split Survey (DPAMSS) and catalogued what questions each survey addresses. There are a good number of similarities, as would be expected; common questions address preferred transportation mode, willingness to try alternatives (and in some cases, which would be the most likely options), commute distance or time, county or city of residence, and reasons for not taking alternative transportation. There are also notable differences, both in the questions themselves and in the way they are asked. For example, both the DPAMSS and one of the Redwood City surveys ask for usual commute mode in the form of a past-week recall, i.e. “how did you get to work each day last week?” while the Commute.org survey simply asked for a usual commute mode without addressing a specific week or window of time. The Commute.org survey also asked far more questions than any other survey, assessing the popularity and reasons for using or not using several specific commute modes (carpool, bike or walk, and transit), and collecting detailed demographic information.
In terms of deeper analysis of the Redwood City data, we are limited to the surveys for which we have the raw data, rather than a synthesis report. Therefore the only deep analysis we are doing is on a survey of city employees, and another survey, conducted by the same organization, of employees who work (but who may not live) in Redwood City. For this analysis, we have begun conducting cross-tabulations similar to those presented in the DPAMSS synthesis report. Not all cross-tabs done in that synthesis can be replicated with the Redwood City data, because some variables were not addressed, e.g. age, business size, and parental status. We will also be selective in which cross-tabs to replicate, and focus only on the most informative ones.
We will look at:
- Mode share crossed with
- Commute distance (already in the RWC report)
- Home location
- Work start time
- Incentives that may be effective/helpful
- Home location (by county) crossed with
- Reasons for not using alternatives
- Alternatives they would be willing to try
- Incentives that may be effective/helpful crossed with
- Reasons for driving
- Reasons for not using alternative transportation.
We also plan to make recommendations for similar analysis done with the raw data for the Commute.org survey, since it was the most detailed and reached the greatest number of people (1029, compared to a total of 240 for the combined Redwood City city employee and community surveys).
What We Observed and Learned
Our biggest obstacle this week was trying to contact employers to survey. None of the employers to whom we distributed our contact information (on a cover letter describing our project) contacted us for surveys, though several of them responded positively when we spoke in person. Calls back to businesses proved fruitless as well, even when scheduled ahead of time. Managers either were not available to talk or did not pick up the phone. We have decided to go ahead with our analysis of the data we have, and in our report acknowledge that we were unable to effectively reach employers for surveys.
When we met with Adina this afternoon, she said she was still waiting on the San Mateo County Health Department for the information regarding Clipper’s ability to partner with the TMA, and what sort of services they would be able to provide. This information is essential to the TMA’s plan, so we are hoping we will have the information by the time we have to present to the TMA on March 3rd. If not, we will incorporate it into our final presentation and report, whenever the information becomes available.
We asked Adina for suggestions on how to best prepare for the March 3rd meeting. Her biggest tip was that a consultant should always be ready to present not matter what stage the project is in, i.e. begin outlining a presentation as soon as you start and drop data and findings in as you gather them. We have already finished our data collection (aside from a few potential follow ups that she suggested) but we definitely need to begin our presentation this weekend. We are hoping to go through the draft with Wendy and Adina on the 1st so we can incorporate feedback before we present to the whole TMA. If we ever do another consulting-type project like this, we will heed Adina’s advice to always keep an up-to-date outline so a presentation can be easily assembled on short notice.
Adina also stressed a few things that we should include in our report even though they may seem obvious. One is that we must note the potential audience in employee categories not captured by our survey, including East Bay commuters, restaurant employees, and “invisible” employees who are not up front interacting with customers. Another point Adina wants us to include is that there should be two different marketing schemes for the program, one targeted at employees and one at employers. Employees might be receptive to messaging saying that taking transit can ease the pain of gridlocked rush-hour commutes, for example, while employers might need reminding that even if their business is small, if a hundred like them take part in the program it could take hundreds of cars off the roads.
Critical Analysis / Moving Forward
Our main focus for now will be creating a report for the TMA meeting on Thursday. We plan on finishing data analysis over the weekend and draft a report by Tuesday, when we will meet with Adina and Wendy to review the report. It would be best for us to start early on the data analysis so if we stumble upon anything that needs guidance from Adina or Wendy, we can address it before we meet on Tuesday. John and Jesus will be presenting at the TMA meeting, since Sophie has class during that time.
Given that we only have a handful of surveys, our data is by no means quantitatively valid, since it was not a random sample either. So our surveys will need to be analyzed more qualitatively. We will use the Palo Alto Mode Split Survey that was given to us by Adina to better analyze and figure out who will benefit from the subsidy program. (We didn’t survey anybody from the East Bay, but we can use PAMSS to derive a number of commuters). This quantitatively rigorous data will supplement our more qualitative survey data.