Francesco Capuzzi Ph.D. Candidate in Political Studies ......1 Francesco Capuzzi Ph.D. Candidate in...

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Francesco Capuzzi

Ph.D. Candidate in Political Studies - University of Milan

[email protected]

Understanding popular Euroscepticism

Paper prepared for presentation at the PSA 66th Annual International Conference,

panel “The EU-ro crises and the end of the Good Life? Competing national understandings and

visions of the EU in times of crisis”- March 21-23 (2016)

Abstract

In this article, I study the dimensionality of the concept of EU support, which is expected to be

constituted by two latent dimensions: one that concerns political integration, in terms of European vs

National governance of strategic policy areas, and another one related to the instrumental evaluation

of country’s EU membership based on cost-benefit analysis. To assess the validity of this theory, I

separately analyse cross-national data from the Intune project 2009 and the European Election Study

2014, applying latent class analysis (LCA) as a statistical method to address the research question.

Using the Intune 2009 dataset the theory holds in 13 out of the 15 EU countries included in the study,

whereas, the same analysis for the EES 2014 dataset leads to an inconclusive result. These two

dimensions of EU support are modelled as discrete-ordinal factors, which allow outlining a typology

formed by six types of attitudes. Furthermore, several predictors of the class membership are

separately tested, and supporting evidence is found for the theories on the effect of affective and

identitarian factors, institutional distrust, and cognitive mobilization on the attitudes towards the EU.

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Introduction

There is a general accordance among political scientists and public opinion scholars regarding the

remarkable change in the elite and popular attitudes toward the European integration following the

signing of the Maastricht Treaty (Eichenberg and Dalton 2007), an agreement which moved an

economic union toward a more political one (Garry and Tilley 2009). That moment ended the era of

a so-called permissive consensus (Lindberg and Scheingold 1970) on the European integration

project, opening a new period of constraining dissensus (Hooghe and Marks 2005) on the direction,

the spread and the contents of the process of Europeanisation. This shift meant the re-politicization

of the EU issue, opening a public debate on EU legitimacy and feasibility, the current structure of

multilevel governance, and the fundamental political principles that have driven the unification.

This emerging debate has been the object of several studies in the last twenty years (e.g. Leconte

2010; Mair and Thomassen 2013; Schmitt and Thomassen 1999), which have addressed the causes

and consequences of the constraining dissensus on the EU integration process. Much of the literature

on the European issue is constituted by studies on the public opinion toward the European integration,

which have their roots in the Inglehart’s Silent Revolution (1977). Recently, many scholars have

widely worked on assessing the role of anti-integration stances in driving the political behavior of

voters (e.g. Evans 1998; de Vries 2007, 2010; Hobolt et al. 2009; Lubbers 2008; Tillman 2004; de

Vreese and Boomgaarden 2005), and parties (Taggart 1998; Kopecky and Mudde 2002; Taggart and

Szczerbiak 2004, 2008a, 2008b), while others have studied the voters-party interactive influence on

developing such attitudes toward the EU (Ray 2003a, 2003b; Steenbergen et al. 2007).

This article attempts to make a step back from those researches, shedding a light on the structure

of the EU related attitudes and starting a preliminary analysis of the factors that influence the opinions

regarding the EU. The literature on the citizen support for the EU suggests that there is not only one

form of Euroscepticism or EU support, and two dimensions combine in defining the individual stance

on the EU issue (Lubbers and Scheepers 2005). The first one regards the political evaluation of the

process of EU policy integration; the second one indicates the perceived instrumental benefits of

being an EU member. Therefore, in accord with the reference theory, the leading hypothesis is that

the concept of EU support is multi-dimensional, composed by two distinct dimensions of political

and instrumental support. If empirically confirmed, this bi-dimensional structure allows outlining a

typology that can split – theoretically as well as empirically – the European electorates in different

groups according to their understanding of the EU and its future development.

To sum up, the purpose of this paper is to understand and explain popular Euroscepticism, studying

the dimensionality of the concept itself and assessing the individual level features that might affect

the development of specific sets of opinions regarding the EU. To accomplish these tasks, I separately

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analyse cross-national data from the Intune project 20091 and the European Election Study 20142,

applying latent class analysis (LCA) as a statistical method to validate the theory.

Anticipating the results, the theory is validated by data analysis from the Intune 2009 dataset,

which shows a bi-dimensional structure of EU support to be present in 13 out of the 15 EU countries

included in the study3. Both latent factors that represent these dimensions have been modelled as

discrete and ordinal factors. In details, the factor that refers to the instrumental evaluation of the EU

membership is a two-level factor (Positive vs Negative evaluation), whereas the other one related to

the preferred political arena for policy decisions is a three-level factor, whose levels are “National or

sub-National arena”, “Mixed combination” and “European arena”. Consequently, in these thirteen

countries it is possible to define a typology based on these two latent dimensions of EU support,

which permits creating six clusters of citizens4.

On the contrary, applying the same analysis to the EES 2014 dataset leads to an inconclusive result,

most likely due to a shortage of indicators in this dataset, where only four could be the appropriate

indicators of the latent concepts, while in the Intune dataset they are nine. Therefore, most of the

article is devoted to analyse the Intune 2009 dataset, and the analysis of the EES 2014 data is placed

in the appendix, in order to give the reader the possibility to review it.

Conceptualizing Euroscepticism

The term Euroscepticism is generically referred to the scepticism toward the project of European

integration (Taggart 1998; Kopecky and Mudde 2002; Taggart and Szcerbiak 2004, 2008a, 2008b;

Lubbers and Scheepers 2005; Sanders et al. 2012). This word express doubt or disbelief in Europe

(Hooghe and Marks 2007), a multilayer object that can be conceived as a set of national states, polities

and policies. The most used definition of Euroscepticism is the Taggart’s seminal description of this

attitude as a “contingent or qualified opposition, as well as [… an] unqualified opposition to the

process of European integration” (Taggart, 1998: 366).

In literature, the use of the adjective Eurosceptic is primarily associated with two actors, notably

public opinion and political parties. The seminal work of Taggart (1998) and, later on, Taggart and

Szczerbiak (2004, 2008a, 2008b) aim to disentangle the party-based dimension, and they propose a

distinction between hard and soft Euroscepticism. The last formulation of their conceptualization

1 Cotta, M., Isernia, P. & Bellucci, P. (2009) “IntUne Mass Survey Wave 2, 2009. ICPSR34272-v2”, Ann Arbor, MI:

Inter-university Consortium for Political and Social Research [distributor], 2013-04-22. 2 EES (2014), European Election Study 2014, Voter Study, Advance Release, 1/1/2015,

(http://eeshomepage.net/voter-study-2014/). 3 From the analysis I have excluded in advance Austria, because of a limited sample size (approximately 500 cases),

and Turkey and Serbia due to the fact that they are not EU members. Hence, this study includes citizens from

Belgium, Bulgaria, Denmark, Estonia, France, Germany, Greece, Hungary, Italy, Poland, Portugal, Slovakia,

Slovenia, Spain, and the UK. 4 See figure 1 at page 8.

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(2008a, 2008b) defines the hard type as a principled opposition to the project as such, against the

very idea of transferring powers to any supranational institution. In contrast, the soft type of

Euroscepticism defines a qualified opposition to the actual EU core policies or an aversion towards

the planned trajectory of the further extension of EU competencies. This typology is contested by

Kopecky and Mudde (2002), who criticize its theoretical precision, proposing their own classification

criteria, which resemble David Easton’s distinction between diffuse and specific support for political

regimes (Easton 1965). They identify a typology based on two dimensions: the diffuse support, as an

approval for the general idea of European integration, and the specific support, namely the support

for the EU’s current structure and the planned future evolution of the European integration (Kopecky

and Mudde 2002). The diffuse support separates Europhiles from Europhobes, while the specific one

differentiates between EU-optimists and EU-pessimists. According to their theory, there are four

ideal-type categories of party attitudes regarding the EU (ibidem):

1) the Euroenthusiasts that combine Europhile and EU-optimist positions, supporting both

general ideas and the integration process;

2) the Eurosceptics that merge Europhilism and EU-pessimism approving general ideas but not

their current application;

3) the Eurorejects that are both Europhobe and EU-pessimist;

4) the Europragmatists, which disapprove general ideas but support the current EU integration

anyway, taking pragmatically into account that even if they are ideologically against the

European integration, they benefit from it.

In the literature on parties, one can see an effort to develop a theory that can be both sufficiently

precise to be operationalised and become useful for empirical studies, and, at the same time,

adequately valid for different party systems. Although in this article I study the citizens’ level, such

typologies, if properly refined, may be employed also for the analysis of individuals. Most of the

individual level empirical studies on this topic define Euroscepticism as merely the overall judgment

on the EU, measured as a self-placement on the anti-pro integration scale (e.g. Evans 1998; de Vries

2007; Hobolt et al. 2009, Tillman 2004). Although this operationalization is quite useful and flexible,

I think it may fail in taking into account in a substantial way popular Eurosceptic or pro-EU stances.

My hypothesis is that, similarly to parties, at the level of individuals there may be not just one form

of Euroscepticism, nor one single dimension underlying the support or rejection of the EU. In line

with this hypothesis, Lubbers and Scheepers (2005) propose to split the concept in two dimensions,

labelled instrumental and political Euroscepticism. These scholars define the latter as an opposition

to transferring policy competencies to the supranational level, whereas the former is an opposition to

a country’s membership in the EU based on a cost-benefit calculus. They report (ibidem: 224) that

instrumental Euroscepticism is mainly measured through the questions ‘Did your country benefit

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from membership of the EU?’, and it is a conceptualization of Euroscepticism that derives from the

cost-benefit approach (e.g. Eichenberg and Dalton 1993; Anderson 1998; Gabel and Whitten 1997;

Gabel 1998a, 1998b). On the contrary, the political Euroscepticism indicates a dimension related to

the distribution of legislative powers among the EU multilevel governance (Lubbers and Scheepers

2005), which links this dimension to the debate on the national sovereignty and the political

legitimacy of European institutions to legislate in strategic policy areas. In their study, they find

evidence supporting their theory, and the aim of the present article is to further validate it, using more

recent data and applying a different statistical technique.

Method and Data

To test the dimensionality of the EU support I use Latent Class Analysis (LCA), more precisely

Latent Class Cluster analysis (LCCA) and Latent Class Factor analysis (LCFA) (Hagenaars and

McCutcheon 2002). The leading hypothesis is that some items included in the dataset are the manifest

part of two unobserved concepts, defined as instrumental and political EU support. These items are

categorical variables considered as the indicators of two latent categorical variables, which represent

the above-cited dimensions. Hence, the observed association among the indicators of each concept is

expected to be spurious, since those items are affected by the same latent variable. In this application,

latent class analysis is conceived as a measurement model, where a latent variable is an antecedent

variable that determines the indicators.

As remarked by Madigson and Vermunt (2004) latent class (LC) modeling was introduced by

Lazarsfeld and Henry (1968) “as a way of formulating latent attitudinal variables from dichotomous

survey items” (Magidson and Vermunt 2004: 3). A few years later, Goodman (1974a, 1974b)

extended LC modeling to nominal variables, while other scholars concluded the work proposing LC

analysis for ordinals (Clogg 1988; Uebersax 1993; Heinen 1996), Poisson counts (Wedel et al. 1993),

continuous variables (Wolfe 1970) and for mixed-mode data (Everitt 1988; Lawrence and

Krzanowski 1996), in which indicators are of different scale types.

Therefore, it is potentially possible to include any kind of variable in the LC model, but for the

aim of this article, I just use nominal and ordinal variables as indicators of the reference concepts.

The purpose of this work is twofold. On the one hand, I empirically test the possibility to separate

instrumental and political EU support, at the same time, I endeavour to classify citizens according to

their attitudes to the EU, comparing cross-country differences. Hence, the first step is to develop a

measurement model that can function in a second phase as a classification model. After having

classified survey respondents into latent groups, it is possible to start a preliminary analysis of the

exogenous variables that may serve as predictors of the levels of the latent factors.

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There are important differences between the traditional techniques used to perform these tasks,

and the one that I apply here. Most of the studies on the consistency of a hypothesized measurement

model employ Confirmatory Factor Analysis (CFA), in which both factors and indicators are formally

required to be continuous, and the assumption of multivariate normality is needed to justify linear

modelling (Magidson and Vermunt 2003). On the contrary, in this work, the latent factors are

supposed to be categorical, where the categories of these latent dimensions correspond to different

types of attitudes towards the EU, and the responses at the item level are just the product of these

antecedent dispositions. In addition, Magidson and Vermunt (2001) reported that Latent Class Factor

Analysis (LCFA) makes solutions “uniquely identified and interpretable without the need for a

rotation” (ibidem: 237), which is often required in traditional factor analysis.

LCFA is also applied to achieve the second purpose of the research, namely to classify survey

respondents into latent groups and to analyse the exogenous membership predictors. Traditional

clustering techniques (e.g. K-Means) lack in rigorous statistical tests to choice the cluster criterion,

and, like CFA, they formally permit the inclusion of only quantitative variables. Conversely, as stated

above, LC analysis does not pose limit to the variables selection, and it does provide statistical test to

evaluate the model fit (e.g. information criteria like BIC and AIC). Furthermore, it produces estimates

for misclassification rates, because classification into clusters is based on posterior membership

probabilities estimated by maximum likelihood (ML) methods (Magidson and Vermunt 2002). For

what concerns the inclusion of exogenous variables to describe differences among the created groups,

those predictors may be included in the model, allowing “both classification and cluster description

to be performed simultaneously using a single uniform ML estimation algorithm” (ibidem: 77).

In conclusion, latent class factor modelling seems to be the most appropriate statistical tool to deal

with the theoretical framework introduced above, and for achieving the presented purposes. I apply

this analysis using two cross-national datasets, the Intune project 2009 and the EES 2014. The

analysis of the Intune dataset is included in the following text, whereas the one pertaining to the EES

data is presented in the appendix.

Measurement and classification model

In the Intune 2009 dataset, we have nine categorical variables that are used as the indicators

(endogenous variables) of the latent factors. Six of them define the political EU support, since they

refer to the preferred level of governance (Regional, National or European) for strategic policy areas,

namely fighting unemployment, immigration, environment, fighting crime, health care, and

agriculture policies:

Q26a. In most European countries today, political decisions are made at three different levels of

government: at the regional level, at the national level, and at the level of the European Union. In

your opinion who should be responsible for each of the following policy areas:

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Fighting unemployment (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER) Immigration policy (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER) Environment policy (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER) Fight against crime (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER) Health care policy (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER)

Agriculture policy (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER)

In order to fit the theoretical framework of support of or opposition to the European integration, I

decided to merge the regional and national levels, creating a dichotomous variable with two

categories: “National or subnational level” and “European level”.

Regarding the survey questions that are used to study the instrumental dimension of the EU

support, they are three. Two of them ask an interviewee to assess his/her country’s utility, and the

third one refers to his/her perceived personal benefit of being an EU member:

Q7a. Generally speaking, do you think that (OUR COUNTRY)'s membership of the European Union

is...? (ONE ANSWER)

A good thing

A bad thing

Neither good nor bad

Q8a. Taking everything into consideration, would you say that (OUR COUNTRY) has on balance

benefited or not from being a member of the European Union? (ONE ANSWER)

Has benefited

Has not benefited

Q9a. And what about of people like you? Have people like you on balance benefited or not from

(OUR COUNTRY)'s EU membership? (ONE ANSWER)

Have benefited

Have not benefited

For what concerns the treatment of missing values, for the indicators of political EU support I use

listwise deletion, excluding from the analysis the cases that present a missing value in any of these

items. The responses “Not an area to be dealt with by any level of Government” and “More than one”,

which are recorded as spontaneous answers, are excluded from the analysis. The reason for this is

that the former has been cited by less than 0.01% of the sample, while the latter does not provide

information about which and how many levels should be involved in the policy decisions.

Quite differently, for the questions related to the instrumental evaluation of EU membership, those

who answered “Do not know” (DK) are considered as having neither a positive nor a negative

assessment. Hence, if one responds DK about the overall country’s membership, then it will be

considered as having “neither a good nor a bad” opinion on that issue. If DK refers to the country’s

or the personal benefit of being an EU member, then one will be treated as thinking that the country

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or the respondent has “neither benefitted nor not benefitted” from the EU membership. Any response

coded as “refuse to give the answer”, except the DKs, has been excluded from the analysis5.

In conclusion, we have three levels for each indicator of instrumental EU support:

Country’s membership: “A good thing”, “Neither good nor bad”, “A bad thing”;

Country’s benefit: “Has benefited”, “Neither benefited nor not benefited”, “Has not benefited”

Personal benefit: “Have benefited”, “Neither benefited nor not benefited”, “Have not

benefited”;

and two levels for those items referred to political EU support:

Fighting unemployment: “National or subnational level” or “European level”;

Immigration policy: “National or subnational level” or “European level”;

Environment policy: “National or subnational level” or “European level”;

Fight against crime: “National or subnational level” or “European level”;

Health care policy: “National or subnational level” or “European level”;

Agriculture policy: “National or subnational level” or “European level”;

The measurement model in figure 1 displays graphically the relationship between the latent factors

and the relative indicators. The model allows correlation between the latent dimensions, because it is

supposed a limited covariation exists amid the two.

Figure 1 - Measurement model

The separate test of the measurement model in each country shows that this structure with two

ordinal factors fits the data well in 13 out of the 15 countries included in the Intune 2009 dataset (the

results for each country are in the appendix). Both p-value and BIC statistics are considered in the

model selection, also taking into account whether the chosen model presents a limited classification

5 In the Intune project 2009 dataset, they treat missing values either as “Do not know” (DK) or “Refusals”, giving a

researcher the possibility to distinguish between different kinds of missing.

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error. The analysis confirms that the theorized model, with one factor modelled with two levels

(Positive vs Negative instrumental evaluation), and another factor with three levels (National or sub-

National, Mixed and European Level for policy decisions), fits the data best6. To explain the process

of model selection and to provide evidence to the reader, table 1 reports the analysis of Belgian

respondents.

Table 1 – Measurement model Belgium

The theorized model is number 13, and it is contrasted with several alternative models. Models

from number 1 to 6 are LC cluster models in which there is only one nominal latent variable with an

increasing number of categories (from one to six), whereas models from 7 to 13 are LC factor models.

Models 7-9 are with solely one discrete-ordinal factor and an increasing number of levels (two, three

and four); models 10-13 have two factors and different features. More in details, number 10 resembles

an exploratory factor analysis (Magidson and Vermunt 2003), in which all the items load on both the

two-level factors and the correlation between the factors is fixed to zero. On the contrary, models 11,

12 and 13 are strictly confirmatory factor analysis, since the relationship between indicators and

factors is theory driven, as displayed in figure 1. These last three models have the same factorial

design, but they differ from each other for the number of factor levels: two for each factor in model

11, three for each factor for model 12, whereas in model 13 each factor has a different number of

levels, namely two for the instrumental factor and three for the political factor.

6 This is the best fitting model in Belgium, Denmark, Estonia, France, Germany, Greece, Italy, Poland, Portugal,

Slovakia, Slovenia, Spain, and the UK, whereas in Bulgaria and Hungary the model does not fit. In these 13 countries

the model combines statistical significance (in LCA p-value greater than 0.05), goodness of fit (according to BIC

statistics) and low classification error. All the analysis are performed with Latent GOLD 5.0 (Vermunt & Magidson,

2013).

N. MODEL BIC(LL) p-value Class.Err.

1 1 CLUSTER 11283 0.00 0.00

2 2 CLUSTER 10565 0.00 0.09

3 3 CLUSTER 10365 0.00 0.13

4 4 CLUSTER 10259 0.66 0.16

5 5 CLUSTER 10280 0.91 0.19

6 6 CLUSTER 10311 0.98 0.21

7 EXP 1F2L 10565 0.00 0.09

8 EXP 1F3L 10477 0.00 0.17

9 EXP 1F4L 10476 0.00 0.24

10 EXP 2F2L 10208 0.59 0.06

11 2F2L 10204 0.22 0.06

12 2F3L 10137 0.87 0.21

13 2F 2L-3L 10144 0.79 0.06

BELGIUM

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According to BIC statistics, Model 12 appears to be slightly better than the theorized model (Model

13), but it presents a much higher classification error (21% compared to the 6% for Model 13). Hence,

considering that both are substantially significant (p-value much greater than 0.05), and that they have

very similar BIC statistics but considerably different classification error, Model 13 is identified as the

best fitting model in Belgium as well as in other twelve EU countries included in the dataset:

Denmark, Estonia, France, Germany, Greece, Italy, Poland, Portugal, Slovakia, Slovenia, Spain, and

the UK.

In order to give a substantial meaning to the factor levels, it is necessary to look at the profile table

displayed in table 27, which reports the conditional item response probabilities and the class

proportions. In Belgium, those who are identified as having the “Negative” factor level on the

Instrumental dimension (29% of Belgians) are much more likely to assess negatively the country’s

EU membership, and the country’s and personal utility to be EU member, compared with the

probability of having negative opinion for people who belong to the “Positive” group8 (71% of

Belgians). Regarding the Political factor, three groups are defined, which differ from one another in

the preferences about the appropriate level of governance for six strategic policy areas. Those with

“National” preferences (27% of Belgians) are far more likely to prefer the National level for all the

six policy areas, whereas those who have “European” inclinations tend to favour the European level.

In the middle position between the two, there are those with “Mixed” preferences (58%), namely

Belgians that do not have unilateral preferences, for the reason that they tend to favour the EU level

for some policy areas (Immigration, Environment and Crime) and the National or sub-national level

for other ones (Fighting unemployment, Health care and Agriculture policy)9. Finally, being classified

into one group over a certain dimension allow predicting the most likely pattern of answers on those

survey items.

7 In the appendix Intune 2009 there are the profile tables of all the 13 countries. 8 In order to label the factor it is necessary to look only at the conditional probabilities of the items that are influenced

by that factor, as displayed by figure 1. For the Instrumental factor those items are Country’s membership, Country’s

benefit and Personal benefit, whereas for the Political factor they are Fighting unemployment, Immigration policy,

Environment policy, Fight against crime, Health care policy and Agriculture policy. Item response probabilities for

items that are not directly affected by that factor vary because of the two factors are moderately correlated. 9 As already highlighted, this model with two factors and, respectively, two/three levels holds in 13 out of 15 analysed

countries. The only structural difference among the countries is the conditional item response probabilities for the

“Mixed” level of the Political factor. For example, Belgian citizens with “Mixed” preferences tend to prefer the EU

level for such policies areas, whereas in other countries the “Mixed” preferences group tend to favour that other

policy areas be decided at the EU level. However, this variance does not undermine the model because there is a

clear and substantial difference among the conditional response probabilities of the three groups. To review the

conditional response probabilities see Intune 2009 appendix.

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Table 2 – Profile table Belgium

After having tested the model country by country, the following step is to assess whether the

defined factors have the same meaning in each context10. Applying latent class analysis to the pooled

dataset of 13 countries11, namely doing a multigroup extension of the LC modelling developed so far,

it is possible to detect what level of measurement invariance is present in this cross-national survey

dataset. Kankaraš et al. (2012) summarized the possible outcomes that can occur in comparing latent

structure across groups, when “they may turn out to be completely different (heterogeneous model),

partially different (partially homogenous model), or completely the same (homogeneous model)”

(Kankaraš et al. 2012: 360). The level of homogeneity that the model reaches using the pooled dataset

allows different kinds of cross-country comparison. Figure 2 shows four models that are tested and

compared to assess the measurement invariance.

10 This procedure has already been done studying separately the profile tables, but with this further test it is possible

to statistically analyse the homogeneity of the probability structure. 11 The expected latent structure emerges in the 13 out of 15 countries.

Negative Positive National Mixed European

Size 29% 71% 27% 58% 16%

Overall evaluation of country

membership

A good thing 34% 97% 63% 82% 92%

Neither good nor bad 28% 3% 17% 9% 5%

A bad thing 38% 0% 20% 9% 3%

Evaluation of Country benefit

Has benefited 32% 97% 62% 82% 92%

Neither benefited nor not benefited 10% 2% 6% 4% 3%

Has not benefited 58% 1% 32% 15% 5%

Evaluation of Personal benefit

Has benefited 8% 73% 38% 57% 68%



Unemployment

National or subnational level 78% 61% 94% 66% 18%

European level 22% 39% 6% 34% 82%

Immigration


European level 42% 62% 17% 64% 94%

Environment


European level 40% 63% 8% 67% 98%

Crime


European level 39% 57% 16% 58% 91%

Health


European level 23% 37% 10% 34% 70%

Agriculture


European level 28% 49% 4% 46% 95%

BELGIUMInstrumental Political (Policy level)

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Figure 2 – Models with different measurement invariance

In the heterogeneity (or heterogeneous) model, the grouping variable has a direct effect on the

manifest variables, which means that “the group variable influences indicators independently of the

latent variable” (Kankaraš et al. 2012: 362), and, much more important, that latent factors (F1 – F2)

and the grouping variable (G) interact with each other in influencing the indicators of each factor (M).

This implies that the effect of the latent structure on the manifest side (indicators) is modified by

group membership, and, thus, this interaction does not allow making any across group comparisons.

Instead, in the partially homogenous model, there is no group-latent variable interaction, and, if

established, partial homogeneity allows comparing countries differences in LC factor memberships

(Kankaraš et al. 2012). Kankaraš et al. (2012) remark the importance of this kind of models, since

partial homogeneity is similar to the metric equivalence in Multigroup-CFA, where only factor

loadings, and not intercepts, are restricted to be equal across groups. In probabilistic terms, the values

of the conditional response probabilities are different in each country, because the group variable

directly influence the indicators, but the effect of the latent variable(s) is the same across groups. The

third model in figure 2 is the structural homogeneous model, where the grouping variable does not

have any direct effect on indicators, but only indirect effect through the latent factors. If this model

fits the data better than the previous two, then the complete measurement invariance is fulfilled. This

corresponds to the scalar equivalent model in Multigroup-CFA in which both intercepts and factor

loadings are the same across groups (Kankaraš et al. 2012). If structural homogeneity is satisfied, the

conditional response probabilities are the same in every country. Finally, the last model is a complete

homogeneity, where both factors and indicators are insensitive of group membership, namely the

measurement model is the same in any of the 13 country considered.

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These models have a hierarchical order, which means that the model with partial homogeneity is

tested after the heterogeneous model has been confirmed by the data analysis12, the structural

homogeneous model is tested only if the former holds, and the same logic is applied for the last one,

the model with complete homogeneity.

To sum up, the pooled dataset includes data from 13 EU countries: Belgium, Denmark, Estonia,

France, Germany, Greece, Italy, Poland, Portugal, Slovakia, Slovenia, Spain, and the UK.

Furthermore, this dataset was weighted in order to equate the number of cases for each country, and

after the weighting process, there are 840 valid cases for each country13. Table 3 reports the analysis

of measurement invariance for the pooled dataset at the scale level, where the partial homogenous

model turns out to be the best-fitting model, according to BIC statistics, which is the most

appropriated selection criterion when the sample size is large as in this case (Hagenaars and

McCutcheon 2002).

Table 3 – Test of the measurement invariance

In conclusion, because the model with partial homogeneity is the best fitting model, it is possible

to evaluate the country specific class proportions that are displayed in table 414.

12 Although the heterogeneity model provides just the same result of the country specific analysis, it serves as baseline

to assess the goodness of fit of the other models. 13 Because of the treatment of missing data, there are countries with approximately 900 cases and country with 600

cases. This weighting process was necessary, thus, to avoid having biased estimates due to interstate differences in

number of cases. 14 The country specific class sizes are slightly different from those included in the country’s profile table, since the

model tested with the pooled dataset include the country as an active covariate, and this addition modifies the overall

structure of probabilities.

POOLED DATASET

MODEL LL BIC(LL) Npar L² df p-value Class.Err.

Heterogeneous -54917 112744 313 9969 10607 1.00 0.04

Partial homogeneous -54954 111813 205 10042 10715 1.00 0.05

Structural homogenous -56392 113435 70 12919 10850 0.00 0.04

14

Table 4 – Class proportions in each country

As shown in the last row of the table, 31% of the sample has a negative opinion regarding the

utility to be a member of the European Union (instrumental EU support). The highest percentage of

negative assessment can be found in the UK and, the second highest, in Estonia. On the contrary,

southern European countries like Portugal, Italy, Spain and Greece have the lowest percentage of

negative evaluation (about 22%-25%). As far as the dimension of political integration is concerned,

the average percentages of pure Nationalists and pure Europeists are 37% and 11% correspondingly.

The remaining 53% is composed by people who think that some policy areas are better dealt with at

the National or sub-National level, while other ones at the European level. Hence, they are neither

strict Nationalists nor pure Europeists. These groups’ sizes widely vary country by country, but,

again, the UK and Estonia are the most anti-Europeist countries, since their percentages of people

who prefer the National policy level are well above the EU average (respectively 57% and 62%).

Although Denmark does not have a high percentage of Nationalists (34%), it has, together with the

former two, the lowest percentage of citizens who consider the European level as an appropriate arena

for policy decisions (6%). In contrast, Slovakia, Poland, Portugal, and Spain have the highest

percentage of political integration supporters (about 14%-17%). Poland seems to be a particular case,

because both the groups of Nationalists (43%) and Europeists (16%) are above the average, while

only the group with mixed preferences (41%) is below the mean (53%). Finally, France, Germany,

Belgium, Spain, Slovakia Republic and Denmark are the countries with the highest number of people

COUNTRY Negative Positive National Mixed European

UNITED KINGDOM 55% 45% 57% 36% 6%

ESTONIA 42% 58% 62% 32% 6%

GERMANY 35% 65% 27% 65% 9%

FRANCE 35% 65% 23% 69% 8%

POLAND 30% 70% 43% 41% 16%

SLOVENIA 30% 70% 40% 51% 9%

BELGIUM 29% 71% 25% 64% 11%

SLOVAKIA REPUBLIC 27% 73% 21% 62% 17%

DENMARK 26% 74% 34% 60% 6%

PORTUGAL 25% 75% 36% 48% 16%

ITALY 24% 76% 41% 49% 10%

SPAIN 23% 77% 23% 63% 14%

GREECE 22% 78% 46% 42% 12%

average 31% 69% 37% 53% 11%

Instrumental Political (Policy level)

15

with mixed preferences (from 69% to 60%), namely citizens who only support the Europeanisation

of some policies areas15.

With these two dimensions of EU support, it is possible to create a typology of six types of EU

citizens, which, to some extent, resembles the Kopecky and Mudde party based typology (2002).

Hence, there are six classes of EU citizens (figure 3):

1) the Eurorejects that have both a negative evaluation of the utility to be an EU member and a

preference for the nationalization of the policy governance;

2) the moderate Eurolosers, who evaluate negatively the EU membership, but suppose that some

policy areas may be better governed at the EU level;

3) the Eurorealists, who combine a negative evaluation of the membership with a pro-European

attitude for a EU level policy governance, which is considered by them, in any case, the most

appropriate level;

4) the Euroenthusiasts that merge a positive instrumental consideration with a preference for the

EU political integration;

5) the moderate Eurogainers, who believe that being an EU member is useful but not all policy

decisions should be taken at the EU level;

6) and the last type, the Europragmatists that are Europeist just because they gain from an EU

membership, but they reject the loss of national sovereignty.

Figure 3 – Typology of EU support

Instrumental

Negative Positive

Po

litic

al

National Eurorejects Europragmatists

Mixed Moderate Eurolosers Moderate Eurogainers

European Eurorealists Euroenthusiasts

Therefore, the model of EU support defined so far allows classifying the citizens of the thirteen

countries included in the pooled dataset according to their stances on these latent dimensions. Table

5 reports the percentages of each type of citizens in each country. The moderate Eurogainers type is

the biggest class in every country, except for the UK (21%) and Estonia (23%), and, on average, 39%

of the sample can be classified in this way. It is the group, concurrently with the Eurorejects, where

there is the uppermost range of variation among the countries, from the 21% of the UK up to the 50%

of Spain. Indeed, for what concerns the Eurorejects, the percentage in the pooled dataset is 17%, with

the highest percentages in the UK (37%) and Estonia (32%), and the lowest in Spain (9%), Slovakia

(10%) and Belgium (11%). The average percentage of moderate Eurolosers is 13%, with its peak in

15 See note 9.

16

France (21%) and Germany (20%), and its bottommost amount in Greece (7%). While the percentage

of Eurorealists is everywhere negligible (about 1-2%), the Europragmatists form a consistent group

almost in all countries (average of 20%), with the highest percentage in Greece (31%) and Italy (27%).

The Euroenthusiasts represent 10% of the sample, with the peak of 15% in Poland and Slovakia, 14%

in Portugal and 13% in Spain, and the lowest percentage in the UK, Estonia and Denmark (5%).

Table 5 – Size of the clusters in each country

To recap, different types of EU support are identified using LCFA, and two dimensions combine

in defining them. This typology permits classifying EU citizens and comparing national distributions.

Taking into account that this technique performs a probabilistic classification based on the

respondent’s pattern of answers, the identification of two separate latent dimensions of EU support

opens up questions about the antecedent factors that may determine these latent attitudes. A

preliminary analysis of those determinants is conducted in the next, final, section, where twelve

covariates are included in the model to predict the latent group membership.

Predictors of the class membership

The last part of this article aims to perform a preliminary test of the predictors of the class

membership. There are several theories in literature that address the mechanism of developing

attitudes towards the EU, and three kinds of theoretical perspectives are here considered. They

respectively look at the affective and identitarian factors, the institutional distrust, and the cognitive

mobilization. The first force that is alleged to affect EU attitudes is the sense of national identity,

which entails a refusal of the Europeanisation of the national polity seen as a threat to the national

Instrumental dimension Negative Negative Negative Positive Positive Positive

Political dimension National Mixed European National Mixed European

COUNTRY Eurorejects

Moderate

Eurolosers Eurorealists Europragmatists

Moderate

Eurogainers Euroenthusiasts

BELGIUM 11% 16% 1% 14% 48% 10%

DENMARK 13% 12% 1% 21% 48% 5%

GERMANY 14% 20% 1% 13% 45% 7%

GREECE 14% 7% 1% 31% 36% 11%

SPAIN 9% 13% 1% 14% 50% 13%

FRANCE 12% 21% 1% 11% 48% 7%

ITALY 15% 9% 1% 27% 40% 9%

PORTUGAL 14% 10% 1% 22% 39% 14%

UNITED KINGDOM 37% 16% 1% 20% 21% 5%

ESTONIA 32% 10% 1% 30% 23% 5%

POLAND 19% 10% 2% 24% 31% 15%

SLOVAKIA REPUBLIC 10% 16% 2% 12% 46% 15%

SLOVENIA 17% 12% 1% 23% 39% 8%

Average 17% 13% 1% 20% 39% 10%

17

cultural integrity (Carey 2002; McLaren 2002, 2004; Hooghe and Marks 2005; Lubbers 2008).

Nevertheless, although the EU is supposed to be a real danger, the effect of perceiving the EU as a

symbolic threat appears to be fairly limited (McLaren 2004). A very large part of Europeans perceives

this risk, but many of them still favour the European integration (ibidem). Therefore, Hooghe and

Marks (2004) and McLaren (2007) theorize a refinement of this theory, claiming that people who

conceptualize their identities exclusively in terms of national identity are likely to be against the EU

project, whereas those who have either multiple identities that include the European dimension or a

fully European identity are likely to support the EU (McLaren 2007). In addition, the Intune dataset

contains also a measurement of the affective attachment to the EU, which can be regarded as a proxy

for a general inclination towards Europe (ibidem), and for this reason it is included as a predictor of

the group membership.

The second theoretical perspective is the one that looks at the institutional distrust to explain EU

attitudes. It is well recognized that people often use heuristics when they deal with politics, especially

when they form opinions on a distant political institution such as the EU (Sanders et al. 2012). The

underlying logic is that very few European citizens have enough knowledge about the EU for

developing a real informed opinion about it. Hence, when they need to cope with this topic, be it in a

referendum or a survey, they use proxies, like, for instance, support for the current national

government, satisfaction with the national democracy or trust in national institutions (e.g. Anderson

1998; Gabel 1998a; McLaren 2004, 2007; Rohrscheneider 2002; Ray 2003a, 2003b; Sanchez-Cuenca

2000). The central point here is that they use information about something they know, that is the

national politics and the national institutional system, to make judgment regarding something they

know less (McLaren 2002). If they positively evaluate their national environment, they positively

assess the EU, transferring their opinion from one domain to the other. However, what has also been

theorized is the opposite mechanism, namely that people who perceive their own national-level

political institutions as corrupted or inefficient are likely to see positively the EU institutions, since

they can limit the power of such national institutions (Sanchez-Cuenca 2000). Similar to this last

rationale, the one applied by citizens who do not trust EU institutions or who perceive EU democratic

deficit (Leconte 2015) is quite straightforward: they should oppose the EU integration.

The last theory to be considered here is cognitive mobilization, which is grounded on Inglehart’s

theory of the Silent Revolution (Inglehart 1977), which states that the individual attitudes towards the

European integration are highly influenced by the level of political skills (Inglehart 1977; Inglehart

and Rabier 1978). Inglehart looks at the education and the cultural and political knowledge to explain

support for supranational integration. He theorizes that, due to the high level of abstraction that the

European project possesses, only citizens with an elevated amount of political skills are able to deal

with the complexity of those processes, understanding political discourses about it and developing

18

personal thoughts (Inglehart 1977). Inglehart supposes that having political skills is the antecedent

needed to produce positive attitudes toward Europe, since to higher skilled people the European

dimension is more familiar and less threatening than for poorer skilled ones (Jassen 1991). On the

contrary, those who do not have such skills should be more worried by the EU, since they are unaware

of what the EU actually is, which entails opposition to an EU membership (ibidem). Empirical

analyses have demonstrated that those who are better educated and frequently involved in political

discussions are more conscious of the EU and have more positive stances on the integration project

(ibidem).

From these theories, it is possible to draw the following exploratory hypotheses regarding the

relationship between those determinants and the latent class membership defined above:

Affective and identitarian factors: people who conceptualize their identities exclusively in terms

of national identity are likely to be against the EU project because it is perceived as a symbolic

threat; whereas, those who have an affective attachment to the EU are likely to have pro

integration attitudes;

Hp1: if a person identifies him/herself exclusively in terms of national identity, he/she is

expected to be against the EU and more political Europeanisation.

Hp2: if a person has an affective attachment to the EU, he/she is expected to be in favour

of the EU and more political Europeanisation.

Cueing rationality and institutional distrust: people often use heuristics when dealings with

opinions about EU issues. Here there are two potential alternative explanations, both involving

trust and satisfaction in national and European institutions. It is alleged that for the national

dimension:

Hp3a: if a person has positive levels of trust and satisfaction with national institutions and

the national democratic system, he/she is expected to be in favour of the EU and more

political Europeanisation.

On the other hand, the alternative mechanism is (see Sanchez-Cuenca 2000):

Hp3b: if a person has positive levels of trust and satisfaction with national institutions and

the national democratic system, he/she is expected to be against the EU and more political

Europeanisation.

Whereas for the European dimension the expected influence of trust and satisfaction is supposed

to be simply positive:

Hp4: if a person has positive levels of trust and satisfaction with European institutions and

the European democratic system, he/she is expected to be in favour of the EU and more


19

Cognitive mobilization: those who are able to understand complex political events are likely to

be more EU supportive;

Hp5: if a person has high levels of political knowledge and interest, he/she is expected to

be in favour of the EU and more political Europeanisation.

Hp6: The higher the personal education level, the more the favour towards the EU and


All the reported theories are based on individual level mechanisms, but individuals are nested in

different national contexts, and each context may modify the effect of such determinants. Therefore,

the results presented here refer to the average effect of each exogenous variables in the pooled dataset.

The aforementioned predictors are included in the model as inactive covariates16, with the addiction

of gender, age and left-right self-placement as supplementary potential predictors17. This latter

covariate is included as a proxy for party support (see Gabel 1998a), since it is assumed that self-

placement and party support are strongly correlated (see McLaren 2002).

The first two hypotheses (hp1 and hp2) regarding the effects of exclusive National identities and

attachment towards the EU seem to be confirmed by the analysis of the class predictors (tables 6 and

7). The levels of instrumental as well as political support vary in the hypothesized direction, because

the probability of having a defined stance on the latent dimensions is highly influenced by the

exogenous variable. For example, the probability to have a “Negative” stance on the Instrumental

factor is 19% if the respondent has a European identity, but, if one has an exclusive national identity,

the probability raises to 51%. The same mechanism works also for the other factor, where preference

for an exclusive National policymaking increases its probability to emerge in case of strict national

identity from 29% to 50%. Similarly, the increment of the attachment towards the EU produces a

dramatic decrease in the probability to have both a “Negative” stance and nationalistic preferences.

Table 6 - Identity

16 Using the inactive covariates method means “computing descriptive measures for the association between covariates

and the latent variable after estimating a model without covariates” (Vermunt and Magidson 2013: 25). 17 Missing values are excluded from the analysis. EU knowledge is an additive index that is created using two questions

that test a respondent’s real knowledge about how many and what states compose the EU. This index has three

levels, “None” for no correct answers, “Limited” for one correct answer, “Full” for both correct. ‘Do not know’ is

considered as an incorrect answer, and missing data are excluded from the analysis. In a similar way, interest in EU

politics is an additive index of “Generally interest in politics” and “How far do you feel that what happens to Europe

in general has important consequences for people like you?”. If both answers are positive then one has “Full interest”,

if only one is positive then “Limited interest”, and “No interest” when both are negative. However, even in this case,

‘Do not know’ is considered as a negative answer, and missing data are excluded from the analysis.

Identity Negative Positive National Mixed European

Also European 19% 81% 29% 58% 13%

Nationality only 51% 49% 50% 43% 8%


20

Table 7 – attachment to the EU

Hp3a regarding the transfer effect from the evaluation of the National democratic system is

confirmed by the analysis, while the alternative hypothesis 3b is rejected, since higher degrees of trust

in the National system tend to produce better evaluation of an EU membership and reduce the

probability of Nationalistic attitudes (tables 8-9-10-11). The effect of satisfaction with and trust in

the EU democratic system is in line with the expected result (hp4), that is those who believe that EU

democracy suffers from democratic deficit tend to prefer National or sub-national policy arena and

negatively evaluate the EU membership, compared to those with a positive opinion on the EU

democracy.

Table 8 – Satisfaction with national democracy

Table 9 – Trust in national Parliament

attachment to the EU Negative Positive National Mixed European

Not at all attached 71% 29% 57% 36% 7%

Not very attached 43% 57% 41% 51% 8%

Somewhat attached 22% 78% 33% 56% 11%

Very attached 13% 87% 28% 56% 16%


satisfaction_National_democracy Negative Positive National Mixed European

Very dissatisfied 49% 51% 44% 45% 12%

Somewhat dissatisfied 34% 66% 37% 52% 11%

Neither satisfied nor dissatisfied 42% 58% 47% 39% 14%

Somewhat satisfied 22% 78% 34% 56% 10%

Very satisfied 18% 82% 30% 59% 11%


Trust_National_Parliament Negative Positive National Mixed European

0-1 50% 50% 49% 39% 12%

2-3 37% 63% 41% 48% 12%

4-6 29% 71% 35% 55% 10%

7-8 17% 83% 30% 61% 9%

9-10 17% 83% 27% 60% 13%


21

Table 10 – Satisfaction with EU democracy

Table 11 – Trust in EU Parliament

The last hypothesis concerns cognitive mobilization (hp5), which states that the higher the personal

political skills, the better the evaluation of the EU. The effect of cognitive mobilization seems to be

confirmed looking at the conditional distributions of probabilities for different levels of EU

knowledge, EU interest and education (tables 12-13-14). In fact, except the invariance of the

preference for an exclusive European policy governance (European level for the Political factor), the

probabilities of having “Negative” or “Positive” instrumental evaluation and “National” or “Mixed”

preferences for policy decisions are highly affected by the antecedent levels of political skills and

educational attainments.

Table 12 – EU knowledge

Table 13 – EU interest

satisfaction_EU_democracy Negative Positive National Mixed European

Very dissatisfied 69% 31% 51% 40% 9%

Somewhat dissatisfied 46% 54% 39% 51% 10%

Neither satisfied nor dissatisfied 47% 53% 56% 38% 7%

Somewhat satisfied 20% 80% 34% 55% 11%

Very satisfied 12% 88% 27% 57% 16%


Trust_EU_Parliament Negative Positive National Mixed European

0-1 64% 36% 53% 38% 9%

2-3 48% 52% 42% 49% 10%

4-6 27% 73% 34% 54% 11%

7-8 13% 87% 29% 60% 11%

9-10 13% 87% 27% 57% 16%


EU_know Negative Positive National Mixed European

None 35% 65% 40% 50% 10%

Partial 25% 75% 32% 56% 12%

Good 17% 83% 26% 62% 13%


EU_interest Negative Positive National Mixed European

None 44% 56% 44% 47% 9%

Limited interest 35% 65% 39% 50% 11%

Full interest 24% 76% 33% 56% 11%


22

Table 14 - Education

Conversely, gender and left-right self-allocation do not determine a much different probability

distribution (tables 15 and 16), whereas the age of the respondent seems to be modestly influent, since

younger cohorts are more supportive of the EU and less nationally oriented than older cohorts, albeit

the dissimilarity in the predicted probabilities is not large (table 17).

Table 15 - Gender

Table 16 – Left-right self-placement

Table 17 - Age

To conclude, in the last part of the article I tested the above-cited hypotheses, although in a

preliminary way since considering the effect of each exogenous variable at time. I included inactive

covariates in the theorized model to predict the class membership and to profile the latent classes,

since covariates do not predict the manifest items but the level of the latent dimensions.

education Negative Positive National Mixed European

Low 40% 60% 42% 47% 12%

Mid 33% 67% 39% 50% 10%

High 22% 78% 30% 59% 11%


gender Negative Positive National Mixed European

Male 28% 72% 35% 54% 12%

Female 34% 66% 39% 51% 10%


Left_Right Negative Positive National Mixed European

0-1 (Far Left) 32% 68% 35% 51% 14%

2-3 (Left) 23% 77% 27% 61% 12%

4-6 (Centre) 32% 68% 37% 52% 10%

7-8 (Right) 25% 75% 35% 56% 9%

9-10 (Far Right) 32% 68% 42% 47% 10%


AGE_CLASSES Negative Positive National Mixed European

16-30 years 25% 75% 31% 58% 11%

31-45 years 30% 70% 36% 54% 10%

46-60 years 34% 66% 38% 50% 12%

61-75 years 35% 65% 42% 47% 11%

over 75 years 35% 65% 40% 49% 10%


23

Overall, it seems that the probability to be Europeanist for matters of policy governance varies less

than the other latent levels when controlling for these predictors. Few exceptions regard the

conditional probabilities for different degrees of attachment towards the EU, trust in and satisfaction

with EU democratic system. Quite differently, both the levels of Instrumental EU support, as well as

the “National” and the “Mixed” levels of Political EU support have a probability structure that

confirms the stated hypotheses18. This preliminary analysis allows supposing that in order to obtain

further support for the EU political integration it is needed to implement further improvement to solve

the perceived democratic deficit and enhance citizens’ political representation in the EU institutions.

Conclusions and further researches

The purposes of this research have been driven by the consideration that the concept of

Euroscepticism and, more generally of EU support, could be more complex than what is frequently

reported in empirical studies. Using Latent Class Analysis, it has been possible to establish the

bidimensionality of this concept, thus validating Lubbers & Scheepers’ theory (2005). These authors

theorized, and then proved using Confirmatory Factor Analysis on Eurobarometer data, that EU

support is a phenomenon that lays on two dimensions: one concerning the political integration – in

terms of European vs National governance of strategic policy areas – and another one related to the

instrumental evaluation of a country’s EU membership based on cost-benefit analysis (Lubbers and

Scheepers 2005). The present article aimed to further validate this theory, separately using two

datasets, namely the Intune project 2009 data the European Election Study 2014. The Intune data

strongly supports the bidimensionality of the concept in 13 out 15 EU countries, whereas with the

EES 2014 data this structure is, on the contrary, not confirmed, most likely due to the lack of

appropriate indicators in the dataset.

Therefore, the survey data provided by the Intune project allow us to elaborate a typology of the

EU support, grounded on these two dimensions, modelled as discrete-ordinal factors. In the thirteen

countries where the bidimensionality holds, citizens are classified in six types, according to their

attitudes towards the EU. The clusterization is based on the comparison between the respondent’s

pattern of answers and the predicted one for each type of EU support. Hence, combining the two

latent dimensions, six types of clusters are defined: the Eurorejects, the moderate Eurolosers, the

Eurorealists, the Euroenthusiasts, the moderate Eurogainers, and the Europragmatists.

Finally, several predictors of the class membership are separately tested, and supporting evidence

is found for the theories regarding the effect of affective and identitarian factors, institutional distrust,

and cognitive mobilization on the attitudes towards the EU. Further improvements of the analysis

should be performed in order to simultaneously test the effect of those predictors, using multinomial

18 Except the alternative hypothesis hp3b.

24

regression in the LCA framework, and, in addition, multiple imputation techniques may be used to

avoid losing information due to the missing values.

25

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Appendix – Intune 2009

Measurement models19

19 Each national sample is weighted with WMID2 (SD Weight - national weight) provided by the Intune 2009 project

dataset.

MODEL BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err.

1 CLUSTER 11283 0.00 0.00 10073 0.00 0.00 10107 0.00 0.00 6210 0.00 0.00 11354 0.00 0.00

2 CLUSTER 10565 0.00 0.09 9434 0.00 0.03 9160 0.00 0.03 5767 0.00 0.06 10691 0.00 0.08

3 CLUSTER 10365 0.00 0.13 8971 0.00 0.06 8959 0.39 0.10 5542 0.02 0.10 10538 0.83 0.09

4 CLUSTER 10259 0.66 0.16 8904 0.00 0.10 8940 0.95 0.09 5534 0.41 0.13 10470 1.00 0.16

5 CLUSTER 10280 0.91 0.19 8887 0.00 0.15 8925 1.00 0.12 5555 0.78 0.16 10508 1.00 0.18

6 CLUSTER 10311 0.98 0.21 8880 0.00 0.14 8945 1.00 0.13 5579 0.96 0.18 10555 1.00 0.21

EXP 1F2L 10565 0.00 0.09 9265 0.00 0.19 9160 0.00 0.03 5656 0.00 0.12 10624 0.02 0.22

EXP 1F3L 10477 0.00 0.17 9260 0.00 0.12 9034 0.00 0.12 5696 0.00 0.13 10624 0.01 0.16

EXP 1F4L 10476 0.00 0.24 9265 0.00 0.19 9003 0.01 0.10 5656 0.00 0.12 10624 0.02 0.22

EXP 2F2L 10208 0.59 0.06 8872 0.00 0.05 8894 0.91 0.06 5504 0.18 0.06 10448 1.00 0.06

2F2L 10204 0.22 0.06 8843 0.00 0.05 8846 0.91 0.02 5460 0.19 0.08 10441 0.99 0.05

2F3L 10137 0.87 0.21 8680 0.00 0.05 8727 1.00 0.07 5406 0.80 0.08 10371 1.00 0.22

2F 2L-3L 10144 0.79 0.06 8613 0.05 0.13 8782 1.00 0.02 5407 0.84 0.22 10372 1.00 0.05

ESTONIA FRANCEBULGARIA DENMARKBELGIUM


1 CLUSTER 10658 0.00 0.00 8550 0.00 0.00 11329 0.00 0.00 8926 0.00 0.00 9999 0.00 0.00

2 CLUSTER 10016 0.00 0.05 7700 0.00 0.06 10435 0.00 0.03 8199 0.00 0.08 8913 0.00 0.05

3 CLUSTER 9705 0.00 0.08 7445 0.05 0.08 10116 0.00 0.08 7964 0.00 0.10 8661 0.00 0.09

4 CLUSTER 9607 0.53 0.14 7372 0.96 0.10 9883 0.00 0.11 7928 0.12 0.14 8613 0.16 0.12

5 CLUSTER 9640 0.76 0.17 7359 1.00 0.14 9855 0.00 0.11 7911 0.77 0.15 8602 0.77 0.12

6 CLUSTER 9661 0.95 0.19 7379 1.00 0.15 9847 0.00 0.14 7940 0.93 0.15 8622 0.96 0.15

EXP 1F2L 10016 0.00 0.05 9003 0.01 0.10 10380 0.00 0.21 8075 0.00 0.19 8789 0.00 0.21

EXP 1F3L 9974 0.00 0.13 7594 0.00 0.12 10377 0.00 0.10 8094 0.00 0.12 8808 0.00 0.13

EXP 1F4L 9978 0.00 0.20 7567 0.00 0.21 10380 0.00 0.21 8075 0.00 0.19 8789 0.00 0.21

EXP 2F2L 9622 0.05 0.05 7360 0.70 0.06 9834 0.00 0.03 7888 0.06 0.09 8587 0.04 0.08

2F2L 9604 0.01 0.05 7338 0.45 0.04 9803 0.00 0.04 7884 0.01 0.05 8555 0.02 0.05

2F3L 9573 0.09 0.17 7236 1.00 0.19 9613 0.00 0.09 7743 0.90 0.11 8420 0.95 0.09

2F 2L-3L 9582 0.05 0.05 7237 1.00 0.04 9676 0.00 0.04 7771 0.66 0.05 8447 0.78 0.05

ITALY POLANDGERMANY GREECE HUNGARY


1 CLUSTER 10476 0.00 0.00 11234 0.00 0.00 8908 0.00 0.00 9795 0.00 0.00 9323 0.00 0.00

2 CLUSTER 9514 0.00 0.06 10508 0.00 0.07 8295 0.00 0.08 9241 0.00 0.10 8126 0.00 0.03

3 CLUSTER 9183 0.00 0.07 10096 0.00 0.09 8053 0.00 0.09 8933 0.51 0.11 7831 0.94 0.05

4 CLUSTER 9019 0.47 0.09 10012 0.00 0.11 7957 0.12 0.12 8837 1.00 0.12 7751 1.00 0.09

5 CLUSTER 8993 0.98 0.13 9981 0.20 0.13 7949 0.71 0.13 8841 1.00 0.17 7755 1.00 0.12

6 CLUSTER 9008 1.00 0.15 10001 0.53 0.16 7982 0.89 0.14 8860 1.00 0.18 7761 1.00 0.14

EXP 1F2L 9410 0.00 0.22 10344 0.00 0.15 8217 0.00 0.25 9137 0.00 0.14 7952 0.01 0.10

EXP 1F3L 9412 0.00 0.12 10346 0.00 0.09 8221 0.00 0.11 9171 0.00 0.18 7981 0.00 0.09

EXP 1F4L 9410 0.00 0.22 10344 0.00 0.15 8217 0.00 0.25 9137 0.00 0.14 7952 0.01 0.10

EXP 2F2L 8961 0.48 0.06 9968 0.00 0.04 7900 0.13 0.05 8790 1.00 0.11 7708 1.00 0.03

2F2L 8937 0.28 0.04 9957 0.00 0.04 7871 0.07 0.05 8777 1.00 0.03 7723 1.00 0.02

2F3L 8802 1.00 0.13 9786 0.58 0.11 7781 0.87 0.16 8704 1.00 0.20 7587 1.00 0.10

2F 2L-3L 8829 0.99 0.04 9825 0.20 0.04 7797 0.72 0.05 8706 1.00 0.03 7634 1.00 0.02

SPAIN UKPOLAND SLOVAKIA SLOVENIA

30

Profile tables


Size 26% 74% 35% 58% 7%


membership

A good thing 12% 95% 54% 83% 92%


A bad thing 79% 2% 40% 13% 4%


Has benefited 21% 98% 59% 87% 96%




Has benefited 5% 76% 41% 66% 74%



Unemployment


European level 10% 22% 3% 21% 69%

Immigration


European level 16% 35% 4% 38% 91%

Environment


European level 34% 60% 16% 70% 97%

Crime


European level 20% 39% 8% 43% 87%

Health


European level 6% 15% 2% 13% 57%

Agriculture


European level 19% 46% 1% 54% 99%

Political (Policy level)DENMARK

Instrumental


Size 42% 58% 61% 33% 6%


membership

A good thing 26% 90% 59% 69% 77%


A bad thing 23% 0% 11% 8% 5%


Has benefited 45% 97% 71% 80% 86%




Has benefited 11% 87% 50% 62% 71%



Unemployment


European level 10% 17% 2% 24% 87%

Immigration


European level 19% 27% 8% 42% 86%

Environment


European level 13% 21% 3% 33% 88%

Crime


European level 10% 18% 1% 27% 90%

Health



Agriculture



Instrumental Political (Policy level)ESTONIA


Size 32% 68% 23% 69% 8%


membership

A good thing 33% 92% 53% 78% 89%


A bad thing 59% 4% 40% 18% 7%


Has benefited 20% 96% 47% 78% 92%




Has benefited 5% 60% 24% 46% 57%



Unemployment


European level 19% 35% 2% 31% 91%

Immigration


European level 44% 65% 16% 68% 96%

Environment


European level 44% 67% 12% 71% 98%

Crime


European level 30% 50% 5% 50% 94%

Health


European level 18% 33% 3% 30% 86%

Agriculture


European level 27% 45% 6% 44% 90%

Instrumental Political (Policy level)FRANCE

Negative Positive European Mixed National

Size 35% 65% 36% 44% 20%


membership

A good thing 36% 97% 84% 75% 64%


A bad thing 52% 1% 12% 20% 29%


Has benefited 6% 87% 69% 57% 43%




Has benefited 5% 69% 55% 45% 34%



Unemployment


European level 19% 28% 50% 14% 3%

Immigration


European level 35% 47% 73% 34% 9%

Environment


European level 58% 70% 91% 64% 23%

Crime


European level 55% 67% 88% 60% 24%

Health


European level 17% 28% 52% 10% 1%

Agriculture


European level 32% 47% 77% 29% 5%

InstrumentalGERMANY

Political (Policy level)

31


Size 22% 78% 45% 43% 13%


membership

A good thing 30% 95% 76% 83% 88%


A bad thing 48% 1% 14% 9% 6%


Has benefited 25% 98% 77% 85% 90%




Has benefited 8% 75% 55% 62% 68%



Unemployment


European level 22% 34% 1% 44% 98%

Immigration


European level 33% 44% 11% 58% 94%

Environment


European level 31% 43% 8% 58% 95%

Crime


European level 20% 31% 3% 36% 90%

Health


European level 16% 26% 2% 28% 89%

Agriculture


European level 18% 27% 3% 30% 84%

GREECEInstrumental Political (Policy level)


Size 24% 76% 39% 51% 10%


membership

A good thing 33% 96% 73% 85% 92%


A bad thing 41% 0% 16% 8% 3%


Has benefited 22% 93% 66% 80% 88%




Has benefited 6% 68% 45% 57% 64%



Unemployment


European level 18% 33% 2% 38% 96%

Immigration


European level 40% 59% 13% 78% 99%

Environment


European level 27% 45% 5% 58% 97%

Crime


European level 22% 35% 6% 41% 87%

Health


European level 10% 21% 2% 19% 76%

Agriculture


European level 12% 24% 1% 23% 92%

ITALYInstrumental Political (Policy level)


Size 32% 68% 43% 41% 16%


membership

A good thing 28% 95% 66% 78% 87%


A bad thing 32% 0% 14% 8% 4%


Has benefited 27% 98% 66% 80% 89%




Has benefited 8% 64% 39% 49% 56%



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