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The Data Never Lie. But, Do We? Los datos nunca mienten pero , ¿y nosotros ? Eric N. Schreffler , ESTC 13 May 2009 ECOMM 2009, San Sebastian, Spain. The Data Never Lie. But, Do We? The Politics and Policy Implications of Mobility Management Evaluation. Overview.

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the data never lie

The Data Never Lie

But, Do We?

Los datosnuncamienten

pero, ¿y nosotros?

Eric N. Schreffler, ESTC

13 May 2009

ECOMM 2009, San Sebastian, Spain

the data never lie1

The Data Never Lie

But, Do We?

The Politics and

Policy Implications of

Mobility Management

Evaluation

overview
Overview
  • Why Evaluate?
  • Why Do I Care?
  • How Can Evaluation

be Manipulated?

  • How Can We Avoid the “Dark Side?”
  • A Challenge
a disclaimer
A Disclaimer…
  • This is my opinion
  • I am being a bit harsh to make a point
  • Most people here do very good evaluations
  • We will learn a lot more this week
  • I am an American… we know it all
why evaluate
Why Evaluate?
  • Satisfy funders?
  • Satisfy policy-makers?
  • Sound management practice?
  • Measure progress against objectives?
  • Refine program or project?
  • Don’t you really want to know?
why are we scared to evaluate
Why are We Scared to Evaluate?
  • It costs money
  • It takes a lot of time
  • Need to plan before project starts
  • Behavior change takes a long time
  • We are not researchers or academics
why really are we scared to evaluate
Why Really Are We Scared to Evaluate?
  • What if the results are not favorable?
  • Will it make me look bad?
  • What if I FAIL?
why do i care
Why Do I Care?
  • I have been evaluating Mobility and Demand Management programs for almost 30 years
  • I have seen the good, the bad, and the ugly
  • I know MM gets marginalized
  • I believe in the overall effectiveness and cost effectiveness of MM/TDM
manipulating results
Manipulating Results

Can Evaluations be Manipulated?

How Can Evaluations be Manipulated?

How Are Evaluations Manipulated?

manipulating evaluation
Manipulating Evaluation
  • Focus only on “before” forecasts, not “after” results;

assuming forecasts = results

manipulating evaluation1
Manipulating Evaluation
  • Focus only on “before” forecasts, not “after” results
  • Use “rules of thumb” or expected results
manipulating evaluation2
Manipulating Evaluation
  • Focus only on “before” forecasts, not “after” results
  • Use “rules of thumb” or expected results
  • Focus data collection only on the “converted”
manipulating evaluation3
Manipulating Evaluation
  • Focus only on “before” forecasts, not “after” results
  • Use “rules of thumb” or expected results
  • Focus data collection only on the “converted”
  • Use anecdotal stories; qualitative findings
manipulating evaluation4
Manipulating Evaluation
  • Focus only on “before” forecasts, not “after” results
  • Use “rules of thumb” or expected results
  • Focus only on the “converted”
  • Use anecdotal stories; qualitative findings
  • Spin results
manipulating evaluation5
Manipulating Evaluation
  • Focus only on “before” forecasts, not “after” results
  • Use “rules of thumb” or expected results
  • Focus data collection only on the “converted”
  • Use anecdotal stories; qualitative findings
  • Spin results
  • Omit results
avoiding the dark side
Avoiding the Dark Side

Traffic

Engineer

Mobility

Manager

Funding Source

avoiding the dark side1
Avoiding the Dark Side
  • Build evaluation into funding process
  • Establish credibility through scientific rigor
  • Pool resources and results
  • Use guidance offered
  • Use local academics
advice know your weapon
Advice: know your weapon

Know your evaluation,

love your evaluation,

for one day, your evaluation just might save your life

the results may surprise you
The Results May Surprise You

According to Congressionally-mandated study of principle funding source used in US, TDM and MM are among the most cost effective strategies for reducing emissions

TRB Special Report 264

contact
contact

Eric N. Schreffer

Transport Consultant

San DiegoCalifornia

001.858.538.9430

[email protected]

muchas gracias parasuatenciōn

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