Delphi Tutu2 Ptapio 050218

P.Tapio TUTU 2, 18.2.2005

What to do with Delphi data?

Feedback and scenarios

Case: Traffic CO2 policy in Finland

Petri Tapio


What will you do with the gathered data?

• Think about it before you gather it!– Concentrate on statements or arguments?– Gathering of authentic statements/arguments

or content analysis?– Quantitative data? Think about scales!

• do not use 5-Likert (or other ordinal scale) scale unless you have to

• use rather interval scale or relative scale

• use rather only one or two scale types


Feedback from the first round may include

• Answers from the previous round + same questions again

• Separate report and better questions• Each participant’s own answers

related to others


Feedback in the case

The preferable futures of road traffic volume- Organisation X and other responses

0

10000

20000

30000

40000

50000

60000

70000

80000

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Time/yr

Road TrafficVolume/

106 vehicle km

Historia

org x VOL


Arguments in the case…

• Arguments for the upper curves– public transport should be supported without

restricting passenger car traffic– the freedom for private car use should not be

restricted

– the growth of CO2 emissions can be stopped with

technical development


…Arguments 2…

• Arguments for the middle curves– the growth potential of passenger car traffic

should be guided towards soft modes, public transport and telecommunications

– urban infill is preferable to reduce the need for traffic

– traffic is a mean, not an end itself, economy and communication should be handled with low need for traffic

– economic growth should be achieved by electronic industry and services, which would reduce the growth rate of freight transport in relation to GDP


…Arguments 3

• Arguments for the lower curves– telecommunications will and should substitute

physical traffic– local more non-material economy is preferable– railroads should be emphasised in both

passenger traffic and freight transport

– CO2 emissions should be reduced 60-80% from

today’s level to stop climate change, which is not possible without also reducing road traffic volume


Cluster analysis

• grouping the responses to clusters• preferable as well as probable futures of

the three key variables– GDP, road traffic volume, CO2 emissions from

road traffic

Qualitative content analysis • are the arguments for the responses within

a cluster similar?


Rescaled Distance Cluster Combine 0 5 10 15 20 25Organisation +---------+---------+--------+--------+--------+

pre=preferable sty pro -+-----+ pro=probable sty pre -+ | akt pro -------+-------+ dodo pro -----+-+ | rhk pro -----+ | ym pro ---+---+ +---------+ rhk pre ---+ | | | ytv* pro -+-+ | | | tl pro -+ +-+ +-------+ | lm pro -+-+ | | | lm pre -+ | | | lal pro ---+-+-+ +---------------+ al pro ---+ | | | akt pre -----+ | | lili pro ---+-+ | | al pre ---+ +---+ | | ene pro -----+ | | | lal pre ---+-+ +---------------+ | tl pre ---+ +-+ | | ym pre -----+ +-+ | ytv* pre -------+ | dodo pre ---+-------+ | ene pre ---+ +-----------------------------+ lili pre -----------+lili pre -----------+


0

5

10

15

20

1970 1996 C 1 C 2 C 3 C 4 C 5 C 6 AlternativeClusters for 2025

GDP index/1926=1,00;

CO2 Emissions

from Road Traffic

/106 tn

0

20

40

60

80 Road TrafficVolume /

109 vehicle km

GDP CO2 Emissions Road Traffic Volume

• Cluster 1– BAU plus

• Cluster 2– Ecological modernisation

• Cluster 3– Modest structural change

• Cluster 4– Strong structural change

• Cluster 5– Deep ecology

• Cluster 6Cluster 6– Steady state economySteady state economy

Probable: STY, AKT, DODO, RHKPreferable: STY

Probable: YM, YTV*, TL, LM, LAL, ALPreferable: LM, RHK, AKT

Probable: LILI, ENEPreferable: AL

Probable: -Preferable: LAL, TL, YM, YTV*

Probable: -Preferable: DODO, ENE

Probable: -Probable: -Preferable: LILIPreferable: LILI


Should you really buy our method?

Qualitative arguments

Quantitative statements

Similar Different

Similar Consistent Inconsistent

Contradictory Inconsistent Consistent

Different Non-systematic Non-systematic


Argumentative interviews

• The role of the interviewer may be

– Neutral

– Sympathetic

– Argumentative

• Argumentative interviews

– The researcher gives counterarguments and further

questions to the interviewee’s statements

– The aim is to produce deeper high quality arguments to

make the interviewees learn from each other

– …and to make the scenarios more precise and consistent

• What kind of problems does this approach generate?

• What are the ways to ameliorate the problems?


Argumentative interviews in round 2: Four ways to avoid bias

• Tell them your argumentative role

• Externalise yourself from the arguments– Rhetorically ”a counterargument has been posed that”

– Think hypothetically - do not try to prove anything

– Respect them and be curious - they may think differently but they do think

• Use systematically first round arguments– Present arguments for upper curves and lower curves

• Concentrate on rational arguments– Dismiss jokes or emotional statements

– ”Dig into their heads”


Examples of personal bias

• Some participants knew the interviewer’s

real opinions – ...or thought they knew

• Two interviewees were not dealt with severely enough– familiarity with the person– the interviewer did not manage to interrupt the

interviewee• Sometimes the interviewer did participate in

non-rational debate– jokes– being too enthusiasted in some arguments

• Concentration is essential!


Conclusions

• Cluster analysis surprises

– Who give similar and different responses?

• Similar quantitative statements sometimes have different qualitative arguments

– Ameliorate with logical analysis, empirical

knowledge and common sence

• Argumentative role of the interviewer induces more in-depth arguments

– Not applicabile to lay people?

• The method does not produce consistency from non-consistency…

– …but it reduces oversimplification and helps in

interpretation


Reference

• Tapio, P. 2003. Disaggregative Policy Delphi: Using cluster analysis as a tool for systematic scenario formation,

• Technological Forecasting and Social Change 70(1): 83-101.

Education

Delphi Tutu2 Ptapio 050218