European attitudes towards the governance of biotechnologies: … · 2019-12-18 · European...

European attitudes towards the governance ofbiotechnologies: Who trusts whom?

Nick Allum (University of Essex)Jouni Kuha (London School Of Economics)

Paul Stoneman (University of Southampton)Gemma Revuelta (Universitat Pompeu Fabra, Barcelona)

ESRA Conference, Lausanne21.7.2011

N. Allum and J. Kuha Confidence in biotechnology actors ESRA, 21.7.2011 1/22

Introduction

Two projects:

STEPE (Sensitive Technologies and European Public Ethics):Interested in attitudes and beliefs about biotechnology in Europe

LCAT (Latent Variable Models for Categorical Data - Tools forCross-National Research): Interested in cross-national measurement

Today’s paper concerns mapping patterns of trust or confidence amongstEuropean public, and methods for evaluating cross-national measurementequivalence


Attitudes to biotechnology and importance of confidence

Europe has recent history of opposition to agricultural biotechnology(GM crops)

Explanations include ‘yuck factor’, lack of knowledge, greenworldviews and lack of confidence in science/industry/government(the ’technoscientific’ complex)

2010 Eurobarometer (73.1) survey asked questions about 11biotechnology actors, which form basis of our analysis

Questions:

What are the patterns of trust in these actors across all of Europe?What kind of citizens adopt particular patterns of trust in these actors?Are the patterns of trust similar across the countries?


Analysing patterns of confidence in biotechnology actors

Data: Eurobarometer 73.1 (2010; 32 countries; N ≈ 30, 000)

Outline of analysis:

1 Latent class analysis (LCA) of 11 binary confidence items to segmentthe public

2 Predictive model of class membership

3 Evaluation of measurement equivalence across European states withmulti-group LCA models


Confidence items (shortened wording)

“For each of the following people and groups, do you think they are doinga good job for society or not doing a good job for society?”

(1) News media (7) Retailers(2) Industries (8) The European Union(3) University scientists (9) Ethics committees(4) Consumer organisations (10) Religious leaders(5) Environmental groups (11) Medical doctors(6) National government

Responses: “Good job” / “Not a good job”; DK treated as missing


Multiple-group latent class model

Items Yj are regarded as measures of the same construct, representedby a categorical latent variable X with K categories (“latent classes”)

Items are conditionally independent given latent class

Country (G) is included as a covariate which affects class X but not(yet!) items Yj directly

Probabilities for p items Y1, . . . ,Yp in country g are then

P(Y1 = y1, . . . ,Yp = yp|G = g)

=K∑

x=1

[P(Y1 = y1|X = x) · · · P(Yp = yp|X = x)] P(X = x |G = g)

Profile of response probabilities P(Yj = yj |X = x) describes the“meaning” of each class


Multi-country latent class model

LatentClass(X)

Country(G)

Item 2(Y2)

Item 3(Y3)

Item 1(Y1)


Probabilities of “Doing a good job”, 6-class model

Latent Class Confidence in: (1) 37%* (2) 23% (3) 8% (4) 20% (5) 7% (6) 4%1 Media 0.98 0.91 0.71 0.56 0.21 0.81 2 Industry 0.96 0.92 0.30 0.65 0.10 0.92 3 University Scientists 1.00 1.00 0.63 0.94 0.25 0.99 4 Consumer organisations 0.99 0.99 0.63 0.81 0.14 0.89 5 Environmental groups 0.99 0.98 0.81 0.69 0.20 0.12 6 Government 0.97 0.95 0.58 0.33 0.07 0.87 7 Retailers 0.96 0.83 0.60 0.42 0.17 0.84 8 European Union 0.99 0.96 0.63 0.53 0.08 0.85 9 Ethics committees 0.99 0.91 0.83 0.60 0.11 0.77 10 Religious leaders 0.94 0.05 0.66 0.11 0.09 0.11 11 Medical doctors 1.00 0.98 0.87 0.85 0.36 0.99

* Percentaged on top row are population-weighted class proportions, based on assigned classes.


Who trusts whom?

Each sample member was assigned to the class for which conditionalprobability given his/her responses is highest

What kinds of people are found as members of these classes, whichhave characteristically different patterns of confidence inbiotechnology actors?

Series of random effect binary logistic regressions, predictingmembership of each of the classes in turn, with country (n=32) asthe random effect

Idea of controlling for demographic compositional differences amongstcountries


Predictors of class membership

Class % Description Predictors

1 37 High confidence Female; Less education;in all actors No science in family; Belief in God

2 23 Low confidence Female; Age 35-44; More education;in religious leaders Science in family; High job status

No belief in God

3 8 Low confidence Lower job status; Belief in Godin industry

4 20 High confidence Male; More education; Self-employed;in scientists Science in family; No belief in God

5 7 Low confidence Male; Did not study science at univ.;in all actors Science in family; Refused education

question; DK to religion question

6 4 (Discussed later)


Are the questions cross-nationally equivalent?

Problem: Even the 6-class model does not fit very well

Adding more classes does not help much, and makes estimation andinterpretation harder

E.g. class 6 in the 6-class model

Another possibility: Examine the measurement equivalence of thequestions across countries

i.e. can we be confident that our latent classes ‘mean the same thing’in each country?and does it matter?


Examining measurement equivalence

Here we consider two approaches (three if time allows):

Separate models in each country

Non-equivalent measurement for some items

(Adding one country-specific class)


Country-specific models

Even a 4-class model fits well for almost all countries individually:

% Large 2-way residuals Across countriesMedian Min Max

6-class model jointly 30 16 514-class models by country 0.5 0 20

In most countries, these 4 classes are roughly like some 4 out of thefirst 5 in the joint model:

1. High confidence in all actors2. Low confidence in religious leaders3. Low confidence in industry4. High confidence in scientists5. Low confidence in all actors


Free measurement models for some items

Here we consider 5-class models

Consider “non-equivalence” models where response probabilitiesP(Yj |X ,G ) depend on country G for some items Yj :

LatentClass(X)

Country(G)

Item 2(Y2)

Item 3(Y3)

Item 1(Y1)


Free measurement models for some items

This improves the fit significantly for each of the 11 items individually

Biggest difference is for Environmental groups and Retailers:

% Large (>4)

2-way residualsacross countries:

AIC BIC Median Min Max

6-class equiv. model 4868 6745 30 16 515-class equiv. model 5962 7482 34 19 53Env. groups non-eq. 4812 7619 28 13 50Retailers non-eq. 4808 7614 31 10 45Env. groups &Retailers non-eq. 3712 7806 24 10 38


Probabilities of “Good job”, 5-class nonequivalence model

Latent class (1) 38% (2) 24% (3) 8% (4) 23% (5) 7%1 Media 0.98 0.91 0.71 0.56 0.212 Industry 0.96 0.92 0.30 0.68 0.103 University Scientists 1.00 1.00 0.62 0.95 0.264 Consumer organisations 0.99 0.98 0.63 0.81 0.166 Government 0.97 0.98 0.58 0.38 0.068 European Union 0.99 0.95 0.62 0.55 0.079 Ethics committees 0.99 0.90 0.82 0.61 0.1110 Religious leaders 0.93 0.04 0.64 0.12 0.0911 Medical doctors 1.00 0.98 0.86 0.87 0.36 5 Environmental groups(*) 0.95

- 1.00 0.41

- 0.980.56

- 1.000.35

- 0.830.00

- 0.387 Retailers(*) 0.91

- 0.99 0.65

- 1.000.36

- 0.920.13

- 0.670.00

- 0.34

(*) Range excluding smallest 2 and largest 2


Predictors of class membership, non-equivalence model

Class % Description Predictors

1 38 High confidence Female Less educationin all actors No science in family Belief in God

2 24 Low confidence (Female) (Age 35-44) More educationin religious leaders High job status Science in family

No belief in God

3 8 Low confidence Lower job status Belief in Godin industry

4 23 High confidence Male More education Self-employedin scientists Science in family No belief in God

5 7 Low confidence Male Did not study science at univ.in all actors Science in family Refused education

question DK to religion question


Adding a country-specific class

% Large (>4)

2-way residualsacross countries:

AIC BIC Median Min Max

6-clas equiv. model 4868 6745 30 16 51Env. groups &Retailers non-eq. 3712 7806 24 10 38Residual class model 2130 6481 6 3 35

This is a 5-class model where all the measurement probabilities acrosscountries are

the same for all items in classes 1–3 and 5different for every item in class 4

I.e. “class 4” has a different interpretation in every country14% - 59% (median 30%) of individuals in each country would beassigned to this class


Probabilities of “Good job”, residual class model

The 4 common classes:

Confidence in: (1) 37%* (2) 19% (3) 4% (4) 32% (5) 8% 1 Media 0.98 0.94 0.68 0.232 Industry 0.97 0.95 0.24 0.123 University Scientists 1.00 1.00 0.60 0.314 Consumer organisations 1.00 1.00 0.66 0.185 Environmental groups 0.99 0.91 0.83 0.236 Government 0.98 0.97 0.66 0.087 Retailers 0.97 0.88 0.65 0.188 European Union 0.99 0.98 0.68 0.099 Ethics committees 1.00 0.92 0.86 0.1310 Religious leaders 0.97 0.07 0.67 0.1011 Medical doctors 1.00 0.99 0.87 0.40

* Percentaged on top row are population-weighted class proportions, based on assigned classes.


Probabilities of “Good job”, residual class model

Country-specific class, for selected countries:

Confidence in: NOR47%

GER39%

ROM15%

EST47%

SPA26%

ICE68%

FIN33%

GRE8%

1 Media 0.59 0.61 0.87 0.51 0.49 0.79 0.80 0.642 Industry 0.76 0.50 0.80 0.76 0.56 0.86 0.72 0.003 University Scientists 0.94 0.90 0.75 0.98 0.98 1.00 0.97 0.554 Consumer organisations 0.90 0.95 0.65 0.74 0.78 0.89 0.72 0.285 Environmental groups 0.69 0.81 0.63 0.72 0.80 0.21 0.55 0.996 Government 0.40 0.29 0.39 0.28 0.60 0.82 0.83 0.007 Retailers 0.39 0.58 0.59 0.29 0.38 0.82 0.64 0.428 European Union 0.48 0.40 0.66 0.70 0.87 0.79 0.66 0.009 Ethics committees 0.78 0.66 0.50 0.63 0.47 0.90 0.81 0.9210 Religious leaders 0.04 0.11 0.47 0.18 0.10 0.04 0.07 0.7711 Medical doctors 0.89 0.88 0.93 0.89 0.98 0.99 0.97 0.83


Conclusions

There is much variation in patterns of confidence in differentbiotechnology actors across European countries

But there are also patterns that are common in all countries

Conclusions about predictors of who trusts whom are relativelyinsensitive to choice of measurement model for questions on trust


Confidence items (full wording)

“For each of the following people and groups, do you think they are doinga good job for society or not doing a good job for society?”

Newspapers, magazines and television which report on biotechnology

Industries which develop new products with biotechnology

University scientists who conduct research in biotechnology

Consumer organisations which test biotechnological products

Environmental groups who campaign about biotechnology

(Nationality) Government making laws about biotechnology

Retailers who ensure our food is safe

The European Union making laws about biotechnology for all EU Member States

Ethics committees who consider the moral and ethical aspects of biotechnology

Religious leaders who say what is right and wrong in the development ofbiotechnology

Medical doctors


European attitudes towards the governance of biotechnologies: … · 2019-12-18 · European...

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