An inconvenient truth or a reassuring lie

A segmentation study on the opinion of climate change among the European population and the underlying reasons why people belong to different segments -

Abstract

Following up on previous climate change segmentation studies, the main goal of this study is twofold. First,

this study tries to find what homogenous cross-country groups of people exist based on their beliefs

regarding climate change. Based on previous literature and using a multilevel latent class analysis (using

2016 European Social Survey data), four major groups can be distinguished: alarmed activists, concerned,

indecisive and sceptics. Second, as previous studies often neglect to explain why these groups exist, the

second part of this study focusses on how belonging to the different segments regarding belief of climate

change can be explained. Hypotheses are formed based on reflexive modernisation theory. Whereas most

hypotheses do not pass the test, lower educated were found to be more likely to be sceptic than to be part

of the alarmed activists. This was in part because of higher reflexive modern values, and a lower level of

institutional trust.

Keywords

Climate change, segmentation analyses, reflexive modernity

Master’s Thesis

Tom Welman

u294874

August, 21st, 2018

First reader: Prof. P. (Peter) Achterberg

Second reader: Prof. M. (Mariano) Torcal

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Introduction

There appear to be vast differences between groups of people regarding their belief if

climate change is human caused (Stokes, Wike, & Carle 2015). It is interesting for both

politicians as well as for policy makers to determine what different groups can be distinguished

in society according to their beliefs if climate change is due to human activity or not. Knowing

which groups in society are sceptic about man made climate change could cause a switch in

current policies, focussing specifically on the groups that need special attention (Schäfer,

Füchslin, Metag, Kristiansen, & Rauchfleisch, 2018). Most papers studying the public’s

opinion regarding climate change focus on segmenting the population into groups. While most

of these papers use similar segmentation analyses techniques, they often fail to produce similar

groups at first glance. Different countries appear to produce different segments, with only a few

overlaps in some countries. Recent data from 2016 by the European Social Survey proves to be

very useful to focus on climate change issues and specifically on latent class analyses.

Therefore, the first research question of this paper reads: what homogenous cross-country

groups of people exist based on their beliefs regarding climate change?

Previous research tried to focus on providing answers on this question by using an

inductive method. Segmentation analyses are often used for establishing distinctive groups.

However, relatively often, segmentation analyses are not based on specific theoretical

frameworks. Instead, researchers often opt for an ‘anything goes’ method. The majority of the

papers dealing with this form of research included variables in their analyses basically on the

reason that these concepts proved to work well in previous studies (Hine et al., 2014; Schäfer,

Füchslin, Metag, Kristiansen, & Rauchfleisch, 2018). Partially, this is probably due to the

inductive approach of these papers. Scholars use a set of variables that are intertwined in the

different classes without making it clear why people belong to these classes. The intention of

these segmentation papers’ is not to explain why people belong to a certain group. This

implicitly creates the idea that these groups naturally exist. Therefore, in this paper, after

answering the first question, a second question is put forward to answer why people belong to

a certain group. The 2016 wave data makes it also possible to perform a cross country analysis

in a multilevel form, which is missing from the literature as of yet. This leads to the second

research question of this paper: how can belonging to the different segments regarding belief of

climate change be explained?

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Literature review

The main idea behind segmentation analyses is to group people in society into mutually

exclusive, homogeneous groups (Hine et al., 2014). This form of analysis has been used to

cluster groups in different fields of interest, among them the public’s opinions on health,

politics, science, and climate change. With the prime focus being here on climate change, table

1 shows an overview of papers from the last ten years performing segmentation analyses about

people’s opinion regarding climate change. The table includes information about the authors,

the data that was used and information on what type of analyses was used.1 Noteworthy is that

only two studies opted for using a factor analysis. Lorenzoni and Hulme (2009) and Xue, Marks,

Hine, Phillips, and Zha (2018) both favoured this technique over the more conventional

segmentation analysis techniques. Based on the table and the literature review, four central

dimensions can be distinguished regarding the belief in climate change. This is elaborated

below.

As stated before, the number of papers that use segmentation analyses using a coherent

theoretical framework is relatively low. Quite some studies use an approach including a number

of variables that simply worked adequately in previous studies. This inductive approach and

the use of different data has led to a variety of descriptions being used to characterise the distinct

groups. However, these studies do provide some reasoning on why specific characteristics

should be included in these types of analyses. Even though the names of the aspects do not

always overlap, there appear to be four aspects that can be distinguished. The first one covers

the idea of the attitude of individuals towards climate change. A large portion of the studies

include a dimension that focusses on people who do or do not believe climate change is real. A

second dimension describes the level of concern people hold regarding climate change. The

third aspect deals with the people who hold reservations regarding the influence of mankind on

climate change. Fourthly, the studies often include an aspect of efficacy regarding climate

change. These four aspects will be used in the paper to cluster the data. These four dimensions

are further elaborated below.

1 Input for this table was found by looking for segmentation papers on climate change in the last ten years by

gathering information from google scholar and using a snowball method. This method was used to find other

relevant sources writing on segmentation analyses on climate change.

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Table 1: Overview of segmentation analyses regarding climate change opinions since 2008

Authors Data Country Type of analysis Number of classes and names

Ashworth,

Jeanneret,

Gardner, and

Shaw (2011)

Australian Bureau of Statistics (2006).

Census of Population and Housing

Australia Segmentation 4 (Engaged, Concerned and confused, Disengaged, Doubtful)

Bain, Hornsey,

Bongiorno, and

Jeffries (2012)

Data collected by authors (2011) Australia Grouped

according to

vignette

2 (Climate change believers, Climate change deniers)

Barnes and Toma

(2011)

Scottish Government (2009). June

Agricultural Census

Scotland Segmentation 6 (Regulation sceptic, Commercial ecologist, Innovator, Disengaged,

Negativist, Positivist)

Hine et al. (2013a) Cardiff University and Griffith

University (2011). Climate change

perceptions and behaviours data

Australia Segmentation 5 (Dismissive, Doubtful, Uncertain, Concerned, Alarmed)

Hine et al.

(2013b)

Cardiff University and Griffith

University (2011). Climate change

perceptions and behaviours data

Australia Segmentation 5 (Dismissive, Doubtful, Uncertain, Concerned, Alarmed)

Hine et al. (2016) Online Qualtrics Panel (2011) Australia Segmentation 3 (Alarmed, Uncommitted, Dismissive)

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Hobson and

Niemeyer (2011)

Qualitative analysis by authors (2010) Australia Q Methodology 5 (Emphatic Negation, Unperturbed Pragmatism, Proactive

Uncertainty, Earnest Acclimatisation, Noncommittal Consent)

Leiserowitz,

Maibach, Roser-

Renouf and Smith

(2011)

Data collected by authors (2008) United States

of America

Segmentation 6 (Alarmed, Concerned, Cautious, Disengaged, Doubtful, Dismissive)

Lorenzoni and

Hulme (2009)

Quantitative survey and qualitative

discussion collected by authors (2000-

2001)

Italy and

United

Kingdom

Factor analysis 4 (Denying, Doubting, Uninterested, Engaging)

Maibach,

Leiserowitz,

Roser-Renouf and

Mertz (2011)

Data collected by authors (2008) United States

of America

Segmentation 6 (Alarmed, Concerned, Cautious, Disengaged, Doubtful, Dismissive)

Mead et al. (2012) Data collected by authors (2009-2010) United States

of America

Segmentation 4 (Indifference, Proactive, Avoidance, Responsive)

Metag, Füchslin

and Schäfer

(2015)

Hamburg’s Federal Research Cluster of

Excellence (2011). Integrated Climate

System Analysis and Prediction

Germany Segmentation 5 (Alarmed, Concerned Activists, Cautious, Disengaged, Doubtful)

Milfront, Milojev,

Greaves and

Sibley (2015)

The University of Auckland (2009).

New Zealand Attitudes and Values

Study.

New Zealand Segmentation 5 (High belief, Climate believers, Undecided/Neutral, Anthropogenic

climate sceptics, Climate sceptics)

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Morrison, Duncan

and Parton (2013)

Online Research Unit (2012). Panel

data

Australia Segmentation 6 (Alarmed, Concerned, Cautious, Disengaged, Doubtful, Dismissive)

Morrison, Duncan

and Parton (2015)

Data collected by authors with help of

Online Research Unit (2015)

Australia Segmentation 6 (Alarmed, Concerned, Cautious, Disengaged, Doubtful, Dismissive)

Morrison,

Duncan, Sherley

and Parton (2013)

Online Research Unit (2012). Panel

data

Australia and

United States

of America

Segmentation 6 (Alarmed, Concerned, Cautious, Disengaged, Doubtful, Dismissive)

Myers, Nisbet,

Maibach, and

Leiserowitz (2012)

Data collected by authors (2012) United States

of America

Controlled

message

experiment

6 (Alarmed, Concerned, Cautious, Disengaged, Doubtful, Dismissive)

Thornton et al.

(2011)

TNS-BMRB (2009-2010). Data

collection on climate change

United

Kingdom

Segmentation 9 (Older, less mobile car owners; Less affluent urban young families;

Less affluent, older sceptics; Affluent empty nesters; Educated

Suburban Families; Town and rural heavy car use; Elderly without

cars; Young urbanites without cars; Urban low income without cars)

Waitt et al. (2012) Data collected by authors (2009) Australia Segmentation 3 (Strong, Modest, Limited)

Wilkins, Urioste-

Stone, Weiskittel

and Gabe (2018)

Data collected by authors with help of

State of Maine tourist centers (2015).

United States

of America

Segmentation 3 (Non-nature-based tourists, Nature-based generalists, Nature-

based specialists)

Xue, Marks, Hine,

Phillips and Zha

(2018)

Online Qualtrics Panel (2013) China Factor analysis 2 (Ecocentrism, Anthropocentrism)

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Research by Leiserowitz, Maibach, Roser-Renouf and Smith (2011) has been influential

among segmentation analyses covering climate change, with several studies afterwards making

use of the same, or similar classifications. The authors made a distinction between six different

groups: “Alarmed, “Concerned”, “Cautious”, “Disengaged’, “Doubtful”, and “Dismissive”.

While composing these groups the authors put a strong focus on the level of awareness on

climate change. Papers focussing on segmentation analysis of climate change, often stress the

importance of including a dimension on awareness. Following up on this American study,

Morrison, Duncan, Sherley & Parton (2013) used the same strategy, but focussed on an

Australian population. These authors also include a similar dimension in their study, focussing

on whether people do or do not believe in climate change. A study by Bain, Hornsey,

Bongiorno, and Jeffries (2012) focussed specifically on people who denied climate change.

There is a strong emphasis on the belief in the effects of climate change. This is one of the two

main distinguishers in their study. Hine et al. (2013a) also mention the importance of the belief

in the effects of climate change: those who were likely to believe in the effects of climate change

responded heavily on issues regarding the collective responsibility and local impacts. Hobson

and Niemeyer (2011) also focused on the belief in climate change, paying special attention to

scepticism, distinguishing five different groups of sceptics. Lorenzoni and Hulme’s (2009)

study focussed on both respondents from the United Kingdom and Italy in their analyses. While

they did not find any different cultural variables that were at play for either one of the countries,

they were able to construct four groups based on two scales. One of these scales dealt with the

question whether climate change is important or not.

The second dimension that can be distinguished in these segmentation studies is the

level of concern people feel regarding the changing climate. Leiserowitz, Maibach, Roser-

Renouf and Smith (2011) described besides the importance of awareness of climate change, the

role of concern about climate change. Something which Metag, Füchslin, & Schäfer (2015) also

did. While Ashworth, Jeanneret, Gardner, and Shaw (2011) also noted that knowledge of

climate change was important, they also stressed the importance of the level of concern people

have regarding the changing climate. Based on these two dimensions they distinguished four

groups. This paper basically builds on the work by Leiserowitz et al. (2011). Hine et al. (2013a)

also included the dimension concern, finding that people who were less inclined to believe in

the effects of climate change, responded more strongly to messages that held emotional

undertones and talked about the possibilities of losses people could experience on several levels

if no action was undertaken. According to a study by Myers, Nisbet, Maibach, and Leiserowitz

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(2012) some information presented, backfired among the least concerned and non-believers

regarding climate change. If information was presented in the form of national security, these

groups were more likely to respond with feelings of anger. While also testing the reactions on

risks to public health, the environment and benefits of mitigation, the authors found that

emotional response was most likely to be found when climate change was linked to public

health issues.

The third dimension deals with the belief if humanity has any influence on climate

change. While not as often mentioned as the previous two dimensions, the influence of mankind

is still included as a variable in quite some other studies. The aforementioned paper by Metag,

et al. (2015) included a measurement for ecological conservatism. Something which was also

included in the study by Hobson and Niemeyer (2011). In their segmentation analysis, they

noted a group that they labelled as “Earnest Acclimatisation”. Members of this group think that

climate change is a natural phenomenon, and not something mankind has any influence on.

Lorenzoni and Hulme (2009) made two scales as mentioned before. Their second scale focussed

on if human activities do affect the climate. The study by Xue et al. (2018) uses a scale

constructed from two variables, anthropocentric and ecocentric worldviews, to make a

distinction between two groups. The first group scores high on the first mentioned, and low on

the latter, indicating people who have a strong feeling that mankind should reign over nature.

This contrasts the other group that entails people who value nature more and are also more

fearful of the effects of global warning.

The final dimension deals with the efficacy of people regarding climate change. While

some people are actively restricting the emission of greenhouse gasses and are taking extra

measures to decrease climate change, others do not act differently. Leiserowitz et al. (2011)

distinguished not only awareness and concern, but also included a dimension on taking action.

Follow-up research by the same authors and by others found similar results (Maibach,

Leiserowitz, Roser-Renouf & Smith, 2011; Morrison et al., 2013). Metag, et al. (2015)

described besides the aforementioned aspects, two measurements that included efficacy:

actively refraining from car use or plane journeys, and political activism on issues relating to

energy. Bain et al. (2012) found that those who denied climate change are only prone to take

action when climate change policies are framed as development on social or economic levels.

Hobson and Niemeyer (2011) distinguished five different groups of sceptics, with acting on

climate change being an important differentiator among the groups. The first group, “Empathic

Negation”, entailed the individuals that believe that the current state of knowledge cannot state

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if climate change is real. This group stated that action should only be undertaken about climate

change if it maximised economic prosperity. The second group “Unperturbed Pragmatism” are

also vocal in their denial of climate change. However, they were less sceptical than the previous

group, mainly using a cost-benefit analysis for determining if there should be acted upon

climate change. The third group was called “Proactive Uncertainty”. Despite being sceptic like

the previous two groups, this group agreed that some action should be taken on both the

community and the individual level. Adger et al. (2008) specifically focussed on the level of

difficulty some societies experience in adapting and taking action against climate change. The

authors propose the idea that what might be limiting for adaptation in one society, may be

fruitful in another society. Also, they argue that not knowledge about unknown future impacts

on climate change is a reason for not adapting, but taking robust decisions will evade the need

for knowledge. Individual decision-making constrains eventual collective action. A study

(Barnes & Toma, 2011) focussed specifically on dairy farmers. This group is responsible for a

large part of the emission of greenhouse gas. Among the farmers there appeared to be quite

some backlash against acting in a more climate-friendly way. The most important predictor to

make a distinction in groups was found by including a variable that measured scepticism

regarding policies and regulations towards the environment. A study by Thornton et al. (2011)

used car ownership as the main divider to determine people’s opinions about climate change.

The main results showed that higher income groups were less environmental friendly, by using

the car more often. At the same time, the group of higher educated held more pro-environmental

values. While there is a lot of overlap in these two groups, the authors noted that their actual

behaviour was not perfectly in line with their beliefs. However, Waitt et al. (2012) found similar

results, while focussing on different household’s capabilities. They segmented households by

their level of pro-sustainable behaviour. In their study they found that pro-environmental

behaviour was most common among lower income groups, alongside women, and among

suburban households.

Interestingly, authors often follow the lead set out by Leiserowitz et al. (2011) regarding

the different segments that are created. For instance, Morrison, Duncan and Parton (2013) use

the exact same segments as the aforementioned authors. According to various measures they

used, seven to nine segments are qualified as superior. However, because more segments do

not substantially differ from six segments and of the impracticality of more segments for policy

makers, the authors opt for the same six segments. This is something that Hine et al. (2013a)

also noticed in the paper by Morrison et al. (2013). While this paper deals with segmentation

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for the use in policies, another paper by Morrison, Duncan and Parton (2015) uses the same

groups using newer data without explaining why they find these groups. Another reason

presented by Morrison et al. (2013) for utilising the same segments as Maibach et al. (2011) is

that they want to make comparisons between both studies. Metag et al. (2015) found similar

groups but excluded the dismissive category. This group was simply not found in Germany and

therefore the authors did not convulsively try to fit that group in the measurements. Myers et

al. (2012) simply state that they use the same methodology that Maibach et al. (2011) found.

Maibach et al.’s methods (2011) and Leiserowitz et al.’s approach (2011) do not differ. Both

papers deal with the same type of analyses. While the first paper is scientific, the latter one is

policy related. The same holds for the papers by Hine et al. (2013a) and Hine et al. (2013b),

respectively being policy related and scientific. While they found similar groups as Maibach et

al. (2011), the authors choose to compose one single central category (uncertain), based on their

analyses, instead of using the same six segments the other papers use.

Data and methods I

In order to answer the first research question, data from the 2016 wave of the European

Social Survey was used. A total of 40807 respondents were used in the analyses. This survey is

held biannually and gives an adequate overview of beliefs and values of European citizens from

22 countries. Besides this, it also includes demographic variables about the respondents.

Specific topics are included in each wave to deal with specific current issues. Data from this

year was chosen as it put an extra focus on climate change. This analysis will be performed on

the pooled dataset to see which groups exist in Europe. Afterwards, an overview will be given

to see what relative sizes the groups represent in each country.

Weighting had to be applied to take the likelihood into account of respondents being

part of the sample. Two different weights have been applied in the dataset before any analyses

were performed: 1. Sampling and non-response errors are corrected by applying post-

stratification weights. 2. Correcting for the fact that most countries have about the same size in

the dataset, but do not actually have the same size. According to information provided by the

European Social Survey (2014), the weights are applied by taking the product of the weighted

scores of the respective variables.

Before running a latent class analysis, a factor analysis was run to determine if the

dimensions are actually composed of the different variables that are presented below. All the

variables were recoded in the same vein, making sure all variables were coded in values ranging

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from 1 to 10. This was done as a way of standardising the values for the factor analysis.

Furthermore, for using the latent class analysis, all values had to be positive integers, making it

impossible to have any values below 1. The factors that come from this factor analysis will

finally be used in the latent class analysis. A generalized least squares model was used to

determine if there were any components that can be distinguished. By using the generalized

least squares model, the statistical program shows a goodness of fit table with a chi-square

statistic and degrees of freedom. Initially, the Kaiser’s criterion and a scree plot were used to

try to distinguish the number of different components. However, this plot did not show a clear

distinction in the number of factors, and therefore only the goodness of fit table was used. For

the factor analyses, a varimax rotation was chosen. In that way the scales remain orthogonal

and reduced the possibility of making correlations between the factors possible. Based on the

number of factors, multiple reliability analyses were used to determine the reliability of the

scales using Cronbach’s alpha. These analyses will be performed in the Statistical Program for

the Social Sciences (SPSS) by IBM.

Below the descriptive statistics for the variables are shown. Missing values in the dataset

for the variables of interest were recoded in a way that they were not taken into account in the

analyses. Some items were also measured contra-indicative, meaning that some variables

needed to be recoded. A full description of all variables is given at the end of this section.

Before running a latent class analysis, three separate multilevel regression models were

run. This was done in order to check whether a multilevel latent class analysis was needed to

be performed and can be seen as an extra verification. The three models respectively included

concern, influence, and efficacy as dependent variables. No independent variables were added

in order to calculate the intraclass correlation. This was done to find out if the variables concern,

influence, and efficacy could be clustered at country level. The intercept at the country level

was tested for significance by a Wald Z test. As all three variables proved to be significant, this

gave reason to perform a multilevel latent class analysis. Furthermore, the intraclass correlation

was calculated to determine the level of variability that can be explained by the country

clustering. These are presented after the results of the factor analyses.

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Table 2: descriptive statistics (variables used for multilevel latent class analysis and factor analyses)

Group Item N Minimum Maximum Mean Std. Deviation

Awareness towards climate change

World's climate is changing 50483 1 (definitely not changing)

10 (definitely changing)

3.445 0.741

Thought about climate change before today

51742 1 (not at all) 10 (a great deal) 3.006 1.112

Level of concern regarding climate change

Worried about climate change 48846 1 (not at all worried)

10 (extremely worried)

3.062 0.939

Worried if [country] is too dependent on fossil fuels

49673 1 (not at all worried)

10 (extremely worried)

3.030 1.021

Climate change good or bad impact across world

47351 1 (extremely good)

10 (extremely bad)

7.800 2.187

Influence of mankind on climate change

Feel personal responsibility to reduce climate change

48081 1 (not at all) 10 (a great deal) 6.566 2.708

Climate change caused by natural processes, human activity, or both

48038 1 (entirely by natural

processes)

10 (entirely by human activity)

3.423 0.826

Imagine large numbers of people limit energy use, how likely reduce climate change

47047 1 (not at all likely)

10 (extremely likely)

6.512 2.342

Efficacy regarding climate change

How likely, large numbers of people limit energy use

47432 1 (not at all likely)

10 (extremely likely)

5.045 2.151

Favour increase taxes on fossil fuels to reduce climate change

48711 1 (strongly against)

10 (strongly in favour)

2.723 1.195

Favour subsidise renewable energy to reduce climate change

49528 1 (strongly against)

10 (strongly in favour)

3.886 1.055

Favour ban sale of least energy efficient household appliances to reduce climate change

49048 1 (strongly against)

10 (strongly in favour)

3.552 1.141

Valid N 40807

Based on the aforementioned section, this paper will use a segmentation analyses to

distinguish the different clusters. A latent class analysis will be used to cluster these results by

the different dimensions. The advantage of this method is that it can use different measurements

of variables. The aim of a latent class analysis is to determine the smallest number of classes to

explain the associations among the manifest variables. A multilevel latent class analysis can be

presented as the following equation:

𝑃(𝑌𝑖) = ∏ ∑{

𝑇

𝑥=1

𝑛𝑖

𝑗=1

∏ 𝑃(𝑦𝑖𝑗𝑘|𝑥)

𝐾

𝑘=1

}𝑃(𝑥𝑗|𝑖).

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Here, let K denote the total number of variables. N equals the number of groups, while

the total number of respondents in a group are presented by 𝑛𝑖. The total number of items are

presented by K. 𝑦𝑖𝑗𝑘 indicates the response of a respondent j in group i of item k. 𝑋𝑗 shows the

individual level variable for the latent class (Vermunt, 2003).

For the latent class analysis two different programs were used. First, a regular latent

class analysis was performed in R, before moving on with the multilevel aspect in LatentGOLD.

First, the procedure in R will be discussed before moving on with the explanation of the

multilevel aspect in LatentGOLD. In R, the data was used that was prepared in SPSS. For

inclusion for the latent class analysis, positive integer values were needed. As the new variables

that were constructed in SPSS held values holding decimals, the numbers were rounded to the

closest positive integer. Initially, R does not provide a function to perform latent class analysis.

Therefore, the package poLCA was used (Linzer, & Lewis, 2011; Linzer & Lewis, 2013). To

determine the number of classes, the BIC values, log-likelihood and chi-square tests were

consulted. This led to an overview where it is possible to determine the total number of groups.

Based on the posterior probability scores of the three variables concern, influence and efficacy

a segmentation was made in different classes. The values of the posterior probability scores add

up to 1. Classes will be made distinctively based on posterior probability per classes that surpass

a threshold of .5. Therefore, some classes will spread their class membership over several values

per scale. The individual posterior probability scores and the corresponding class that are

presented in the models will be saved and will be used for the multilevel analysis.

For the multilevel latent class analyses the program LatentGOLD was used, as a similar

function is not incorporated in the poLCA package as of yet. To perform this analysis in

LatentGOLD, the step-3 approach developed by Bakk & Vermunt (n.d.) was used. This

program allowed the inclusion of country variance into the model (Vermunt, 2010). While the

latent class model was built with R, the external variable of interest (countries) can be included

in the model while correcting for classification errors. With use of the posterior probability

values for the respondents, it can be determined how the countries relate to one another in class

sizes. Tables are presented to give an overview of the group sizes in different countries.

Variable overview

World’s climate is changing. The original question in the dataset asked respondents: “do

you think the world’s climate is changing?” The choice for this variable was based on the

reasoning that this variable did not put any judgment of value in the question. Moreover, it does

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not ask whether people should act and whether the influence of the climate changing has any

positive or negative effect. This variable was originally coded from 1 (Definitely changing) to

4 (Definitely not changing). As the item was coded contra-indicative, the variable was recoded.

Finally, as mentioned above, all values were recoded from 1 to 10 to match up all variables.

Thought about climate change before today. “How much have you thought about

climate change before today?” was asked to all respondents in this dataset. This variable was

coded from 1 (Not at all) to 5 (A great deal). This variable was recoded in the same way as all

other variables, by creating a scale ranging from 1 to 10. Initially, there were two variables in

the dataset that dealt with how much people thought about climate change. This had to do with

answers respondents provided on a previous question. Respondents who answered the first

question about their thought on climate change, did not answer the second question and vice

versa. As the questions were asked in a similar way, with the same possible answer categories,

a variable was created storing the information of both variables.

Worried about climate change. This variable was included to measure the level of

concern people held towards climate change. Indeed, the question asked to the respondents was

“How worried are you about climate change?” This variable was coded from 1 (Not at all

worried) to 5 (Extremely worried). Preventing measurement issues, this variable was recoded

from 1 to 10.

Worried if [country] is too dependent on fossil fuels. The next dealt with the

respondent’s opinion about the dependency on fossil fuels: “How worried are you about

[country] being too dependent on using energy generated by fossil fuels such as oil, gas and

coal?” This item was included to measure the opinion people held regarding their concern about

the way society handles the earth. Answers were, like the previous variable, were coded from

1 (Not at all worried) to 5 (Extremely worried). In the analysis, the variable was coded with

values ranging from 1 to 10.

Climate change good or bad impact across world. The respondents were asked: “How

good or bad do you think the impact of climate change will be on people across the world?

Please choose a number from 0 to 10, where 0 is extremely bad and 10 is extremely good.” This

variable was measured contra-indicative and was therefore recoded in the opposite direction,

while creating a scale ranging from 1 to 10. This variable was included to measure the concern

people hold regarding climate change.

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Feel personal responsibility to reduce climate change. “To what extent do you feel a

personal responsibility to try to reduce climate change?” was asked to respondents in order to

determine the level of responsibility one had regarding reducing climate change. The variable

held values ranging from 0 (Not at all) to 10 (A great deal). This variable was recoded the same

as other variables.

Climate change caused by natural processes, human activity, or both. This variable was

also included to determine whether respondents held beliefs if they could have any influence

on the climate, or not. “Do you think that climate change is caused by natural processes, human

activity, or both?” The variable was originally coded from 1 (Entirely by natural processes) to

5 (Entirely by human activity), but was altered for the final analysis holding values ranging

from 1 to 10.

Imagine large numbers of people limit energy use, how likely to reduce climate change.

Respondents were asked: “Now imagine that large numbers of people limited their energy use.

How likely do you think it is that this would reduce climate change?” This item is included to

measure the influence people thought they could have on climate change. The original variable

was coded from 0 (Not at all likely) to 10 (Extremely likely), but was recoded from 1 to 10, just

like the other variables.

How likely, large numbers of people limit energy use. The first variable to measure the

level of efficacy people thought they or others could have to minimise climate change,

respondents were asked “How likely do you think it is that large numbers of people will limit

their energy use to try to reduce climate change?” This variable was originally coded from 0

(Not at all likely) to 10 (Extremely likely), but was recoded to hold values ranging from 1 to

10.

Favour increase taxes on fossil fuels to reduce climate change. This variable measured

the willingness of people to spend more money to discourage the use of products that harm the

environment. The original variable was coded from 1 (Strongly in favour) to 5 (Strongly

against). The variable was contra-indicative and therefore reversed and recoded to hold values

in the range from 1 to 10. The question that was asked to respondents read: “To what extent are

you in favour or against the following policies in [country] to reduce climate change? Increasing

taxes on fossil fuels, such as oil, gas and coal.”

Favour subsidise renewable energy to reduce climate change. Like the previous

variable, this item held information regarding efficacy. However, this question asked whether

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people think if subsidising climate friendly behaviour would be favoured. The original question

asked: “To what extent are you in favour or against the following policies in [country] to reduce

climate change? Using public money to subsidise renewable energy such as wind and solar

power.” The original values ranged from 1 (Strongly in favour) to 5 (Strongly against), but were

recoded contra-indicative and in a range from 1 to 10.

Favour ban sale of least energy efficient household appliances to reduce climate

change. The last item included was also to measure the level of efficacy: “To what extent are

you in favour or against the following policies in [country] to reduce climate change? A law

banning the sale of the least energy efficient household appliances.” The values were, just like

the previous two variables coded from 1 (Strongly in favour) to 5 (Strongly against), but were

recoded to hold values from 1 (Strongly against) to 10 (Strongly in favour).

Results factor analysis and cluster analysis

Factor analysis

A factor analysis is a dimension reduction technique that assumes that a causal model

exists underlying the constructs.

Table 3: structure matrix showing the results of the factor analysis for climate change (varimax rotation)

Factor

1 2 3

World's climate is changing .451 .217 -.058

Thought about climate change before today .643 .140 .061

Worried about climate change .763 .153 .188

Worried if [country] is too dependent on fossil fuels .438 .081 .090

Climate change good or bad impact across world .401 .239 -.208

Feel personal responsibility to reduce climate change .466 .229 .98

Climate change caused by natural processes, human activity, or both .346 .271 .050

Imagine large numbers of people limit energy use, how likely reduce climate change .199 .241 .551

How likely, large numbers of people limit energy use -.043 .010 .607

Favour increase taxes on fossil fuels to reduce climate change .098 .357 .139

Favour subsidise renewable energy to reduce climate change .131 .572 .011

Favour ban sale of least energy efficient household appliances to reduce climate change .166 .437 .076

The 12 items selected based on the literature study were subjected to a factor analysis

using a generalized least squares models. All models presented below held a Kaiser-Meyer-

Olkin measure value of .819, indicating that the data is suitable for the use of a factor analysis.

According to Cerny and Kaiser (1977) values between .8 and .9 are meritorious for a factor

analysis. Accordingly, Bartlett's Test of Sphericity also indicates that the correlation matrix is

not in fact an identity matrix (p-value < .001), and therefore suitable for a factor analysis.

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Initially five different models were run with each one using a different number of fixed

factors. The first model extracted only one factor, being able to explain 27.072% of the variance

in the variables. All but two variables (favour increase taxes on fossil fuels and how likely

people will limit energy use) load (higher than .3) on the factor matrix.

Another factor analysis was done, utilising two factors. The level of explained variance

rose with almost 12% to a total of 39.027% of explained variance. Three values loaded higher

than .3 on the second factor: The likelihood of reducing climate change because of limiting

energy use, the likelihood of large people limiting energy use, and the extent people feel

personal responsible to reduce climate change. This last variable also loaded positively on the

first factor. The chi-square test was still significant with a decrease of 5883.269 (df = 24, p-

value < .01). There was only a small positive correlation between both factors (r = .104).

The third factor analysis included three factors. By including a third factor the

proportion of explained variance of the three factors rose to 48.669%. The chi-square test was

still significant with a decrease of 3137.983 (df = 36, p-value < .01). World's climate is changing

(.451), thought about climate change before today (.643), worried about climate change (.763),

worried if [country] is too dependent on fossil fuels (.438), climate change good or bad impact

across world (.401), feel personal responsibility to reduce climate change (.466), climate change

caused by natural processes or human activity (.346) all load positively on the first factor. The

factor loadings of the following variables can be distinguished on the second factor: favour

increase taxes on fossil fuels (.357), favour subsidise renewable energy to reduce climate

change (.572), favour ban sale of least energy efficient household appliances to reduce climate

change (.437). Climate change caused by natural processes or human activity also loads on this

factor (.271). Imagine large numbers of people limit energy use, how likely to reduce climate

change (.551), how likely if large numbers of people limit energy use (.607), and the personal

responsibility for reducing climate change (.398) load positively on the third scale. The factor

loadings of the third factor analysis are presented in table 4. None of the factors correlate highly

with one another.

Another factor analysis was performed with four factors. The total variance explained

increased to 56.440% by these four factors. However, the chi square test proved not to be

significant (df = 48, p-value > .05). Therefore the choice was made to continue with three

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factors instead of four. This is also in line

with the eigenvalues as only the first three

factors held a value larger than 1.

According to the factor loadings,

awareness and the level of concern

regarding climate change are one

construct, while the second factor shows

the efficacy regarding climate change.

The third factor could distinguish the

influence of mankind on climate change.

To test if this is actually the case, several

reliability analyses was performed.

A reliability analysis with the

variables for the first seven items was

performed. The Cronbach's alpha

coefficient for this model had a value of

.728. This would create a reliable scale

(Brownlow, McMurray, & Cozens,

2004). No deletion of variables would

increase the Cronbach's alpha, nor is the

item-total correlation of any of one of the

variables lower than .3. The scale was

created according to classical test theory

by adding up the variables. First the variables were all recoded, so that all variables held the

same values with respect to their original codings. Then, the scale was recoded to hold values

ranging from 1 to 10.

A second reliability analysis was also performed to determine the reliability of a scale

regarding efficacy. This was done with the variables denoting the favour of increasing taxes on

fossil fuels, the favour to subsidise renewable energy, and to favour to ban the sale of least

energy efficient household appliances. The cronbach's alpha value was only .504, indicating a

scale that was moderately reliable (Brownlow et al., 2004). No items could be deleted according

to this analysis. Perhaps the low value of alpha comes from the inclusion of only three items,

Table 4: mean values of dimensions per country

Concern Influence Efficacy

Austria 6.524 6.333 6.962

Belgium 6.647 6.327 6.637

Czech Republic 5.789 4.755 6.022

Estonia 5.873 5.148 6.245

Finland 6.593 6.647 6.979

France 6.983 6.736 6.433

Germany 6.964 6.591 7.095

Hungary 6.214 5.370 6.849

Iceland 6.573 6.639 6.377

Ireland 6.350 6.360 6.234

Israel 6.204 6.053 6.306

Italy 6.576 6.034 6.544

Lithuania 5.992 5.593 6.145

Netherlands 6.434 6.151 6.782

Norway 6.395 6.450 6.928

Poland 6.078 6.114 6.319

Portugal 7.144 6.628 6.480

Russian Federation 5.801 4.860 5.960

Slovenia 6.573 6.027 6.992

Spain 7.222 6.466 6.551

Sweden 6.541 6.775 7.183

Switzerland 6.775 6.822 7.228

United Kingdom 6.529 6.336 6.510

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as cronbach’s alpha tends to artificially increase with the inclusion of more variables. Still, a

scale was created with these three variables. The new scale held values ranging from 1 to 10.

A final reliability analysis was performed to create a scale for the influence people have

on climate change. The alpha value for this scale was .520. As the scale was made up of only

two items, no further items could be deleted. According to Brownlow et al. (2004) this would

form a moderately reliable scale. Nevertheless, a scale was created with values ranging from 1

to 10.

Interesting to note is the mean values each variable holds per country. These values can

be found in table 4. Noteworthy is that the level of concern is highest in countries like Spain,

Portugal, France and Germany, while eastern European countries like Czech Republic, Russia

and Estonia have the lowest level of concern. For influence, the highest values are among

countries like Switzerland, Sweden and France. As well as with concern, the level is the lowest

for Czech Republic, Russia and Estonia. While finally looking at the level of efficacy, it is

noteworthy to see that both Switzerland and Sweden are at the top again, while Russia and

Czech Republic form the bottom group on these scores again. Generally, a distinction can be

made between Eastern and Western European countries. In the Western group of countries the

general level of concern, influence and efficacy is higher than in the Eastern European

countries. As discussed in the first data and methods section, the intraclass correlation was

calculated for the three different variables. For both concern and influence the intraclass

correlation was more than 10%. This indicated the percentage of both variables that could be

explained by country clustering. For efficacy this number was lower: only 4.785% could be

explained by country clustering. Still, the results were significant, indicating that some of the

variables can be explained through the clustering at the country level.

Latent class analysis

Latent class models are able to find patterns in the data that hold similar response

categories over different variables. The latent class analysis was performed using a model that

presented eight different models (see table 5). Each model presented a model with n classes,

ranging from 1 to 8.

Looking at the BIC values of the different models, a clear pattern can be seen. The

second model has a lower BIC than the first model, indicating that two classes are favoured

over one. The decline in BIC sets on until reaching its lowest value for four latent classes.

Afterwards, it starts to rise. According to the BIC scores a total number of four latent classes is

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favoured. Looking at the chi-square goodness of fit statistic, the same can be concluded. While

the values of chi-square keep decreasing with the inclusion of each new class, after including

four classes, the decrease in chi-square is not significant. Based on these two criteria it is safe

to assume that four latent classes are the preferred number of classes to use in the analysis.

Table 5: statistics for latent classes

Number of latent classes

1 2 3 4 5 6 7 8

Maximum log-likelihood -226194.1 -218167.7 -216004.3 -215391.0 -215247.7 -215164.5 -215125.7 -215113.3

AIC 452442.2 436445.5 432174.6 431004.1 430773.3 430662.9 430641.4 430672.6

BIC 452677.2 436924.0 432896.7 431969.9 431982.7 432116.0 432338.0 432612.9

Increase in BIC 0.0 -15753.2 -4027.3 -926.8 12.8 133.3 222.0 274.9

Chi-square goodness of fit 44788.5 11040.8 3143.9 1471.0 1141.4 957.4 855.0 838.4

Increase in chi-square 0.0 -33747.7*** -7896.9*** -1672.9*** -329.5 -184.1 -102.4 -16.6

*p. < .05; **p < .01; ***p < .001

Looking at the posterior probabilities per outcome variable (table 6), it is possible to

garner meaningful results about class membership. The first class can be seen as the "alarmed

activists". This group is actually worried about climate change and scores generally, relatively

high (8, 9) on the concern scale. They are also very likely to think they can have an influence

on the climate, with almost 90% of the posterior scores being around the values 7 to 10. Of all

groups, this class also presents itself as the most actively willing to act on climate change.

The second class is labelled as the “sceptics”. On all three variables they score lower

than any of the other three classes. Their level of concern is centred around 4 or 5, indicating

that they are not very much concerned about climate change, especially not when compared to

class 1. Their idea of influence that can be had on climate change is even lower, with more than

60% of the scores being around the values 2 to 4. Oddly enough, their level of efficacy is higher

than their level of influence, with the bulk of the scores being around the values 4 to 6.

The third and fourth group are relatively close to one another and are centred between

the first two classes. The third group could be called the “concerned”. Their level of concern is

slightly lower than the first class, with more than 95% of the scores being between the values 6

and 8. Their opinion about the influence they could have on climate change is close to the scores

on concern, with more than 50% being around the 6 and 7. The level of efficacy is spread out

between the values 6 and 9. Interestingly enough, the highest values can be found for the values

6 and 9.

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The fourth group will be called the “indecisive”. While not being as dismissive as the

sceptics, this group appears to be indecisive about their opinion regarding climate change. On

the level of concern they often score around a 5 or 6. This hints at some level of worry regarding

climate change, but are not inclined to hold a large apprehension around the topic. Their opinion

about the level of influence they appear to have is similar, with the bulk of the class scoring

around the 5 or 6. However, the scores of efficacy lie around the 6 or 7. A noteworthy difference

between class 3 and 4 deals with the level of efficacy. Whereas the third group holds values

between 6 and 9, the values for class 4 are slightly more spread out.

Table 6: Posterior probability values per scale

Variable values 1 2 3 4 5 6 7 8 9 10

Concern

class 1: .000 .000 .000 .004 .011 .010 .121 .462 .344 .047

class 2: .000 .004 .090 .383 .356 .112 .045 .010 .000 .000

class 3: .000 .000 .001 .000 .000 .206 .511 .259 .023 .000

class 4: .000 .000 .000 .032 .277 .496 .182 .012 .001 .000

Influence

class 1: .000 .000 .002 .006 .012 .087 .100 .279 .313 .202

class 2: .170 .172 .247 .235 .129 .045 .000 .002 .001 .000

class 3: .001 .003 .007 .010 .054 .264 .335 .245 .070 .011

class 4: .006 .020 .054 .112 .188 .429 .145 .041 .005 .001

Efficacy

class 1: .009 .003 .012 .022 .031 .130 .138 .137 .369 .150

class 2: .066 .030 .144 .134 .137 .310 .090 .046 .038 .006

class 3: .004 .002 .019 .031 .051 .261 .198 .197 .214 .025

class 4: .009 .008 .060 .071 .112 .388 .163 .110 .068 .012

Bold numbers indicate highest values per class, based on creating scores that add up to at least .5 per variable

The previous section describes the latent class analysis based around the pooled data of

all countries combined. Table 7 shows the scores for each class per country. The parameter

table shows the differences in class membership for each country. The reference country in this

table is Austria. This means that all countries are compared to Austria. At the same time all

classes are relative to class 1. For instance, comparing Austria to Belgium, this means compared

to Austrians, Belgians are more likely to be in class 3 than in class 1. The results from this table

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shows some overlap with table 4. When focussing on class 2, compared to Austria, people from

Czech Republic and from the Russian Federation are more likely to be in class 2, indicating a

high level of sceptics among these countries. Overall, Switzerland holds the smallest group of

sceptics. Interestingly, the largest differences can be found between classes 1 and 2.

Table 7: Parameter model per country (values for countries show probabilities of being in a class compared to reference country)

Class 1 Class 2 Class 3 Class 4

Intercept .000 -.307 (.139) 1.114 (.116) 1.259 (.096)

Covariates Alarmed activists Sceptics Concerned Indecisive

Country

Austria .000 .000 .000 .000

Belgium .000 -.539 (.243) .237 (.170) -.085 (.146)

Switzerland .000 -2.051 (.483) .071 (.160) -.671 (.143)

Czech Republic .000 2.927 (.231) .207 (.272) 1.766 (.202)

Germany .000 -1.319 (.220) -.086 (.139) -.873 (.123)

Estonia .000 2.555 (.240) .541 (.258) 1.426 (.212)

Spain .000 -.844 (.191) -.117 (.146) -1.036 (.132)

Finland .000 -1.231 (.293) .030 (.159) -.294 (.135)

France .000 -1.349 (.244) -.225 (.147) -.861 (.129)

United Kingdom .000 .151 (.194) -.090 (.167) .005 (.137)

Hungary .000 1.327 (.229) .457 (.227) 1.316 (.187)

Ireland .000 .601 (.187) .231 (.166) .569 (.137)

Israel .000 .662 (.170) .003 (.154) .209 (.126)

Iceland .000 -.333 (.295) .290 (.210) -.080 (.184)

Italy .000 .138 (.205) .474 (.167) .618 (.142)

Lithuania .000 1.470 (.194) .279 (.190) .971 (.155)

Netherlands .000 .215 (.231) .496 (.187) .450 (.161)

Norway .000 -.242 (.231) .116 (.174) -.039 (.148)

Poland .000 1.289 (.245) .833 (.232) 1.390 (.199)

Portugal .000 -1.226 (.212) -.895 (.161) -1.285 (.134)

Russian Federation .000 2.120 (.186) -.085 (.203) 1.404 (.149)

Sweden .000 -1.559 (.370) -.058 (.164) -.380 (.137)

Slovenia .000 .004 (.236) .338 (.194) .150 (.166)

Standard errors of the scores are noted between brackets.

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Table 7 shows the relative sizes of all four groups. The overall pooled class size gives

insight into the size of the overall relative sizes of the classes. Overall, the groups concerned

and indecisive are the largest, making up respectively 35% and 43% of the population. This

indicates that the population in Europe can primarily be found in the less polarised groups. The

alarmed activists compromise the smallest group with a value close to 11%. The sceptics are

slightly larger. Germany takes up the largest part in the group of alarmed activists: the

proportion of Germans in class 1 is .108. Again, Czech Republic compromises the smallest part

of this group. Contrary, Czech Republic holds the largest percentage in class 2, the sceptics.

Noteworthy is that the indecisive group is the largest in the Russian Federation.

Table 8: Profile scores per country (values for countries indicate proportions of people in classes)

Class 1 Class 2 Class 3 Class 4

Alarmed activists Sceptics Concerned Indecisive

Pooled class size .109 .113 .350 .429

Country

Austria .050 .036 .048 .045

Belgium .043 .018 .052 .035

Switzerland .051 .005 .052 .023

Czech Republic .012 .160 .014 .063

Germany .108 .021 .094 .040

Estonia .014 .126 .022 .051

Spain .077 .024 .065 .024

Finland .057 .012 .056 .038

France .084 .016 .063 .032

United Kingdom .050 .041 .043 .045

Hungary .015 .041 .023 .051

Ireland .046 .060 .055 .073

Israel .054 .074 .051 .059

Iceland .021 .011 .026 .017

Italy .041 .033 .062 .068

Lithuania .025 .078 .031 .059

Netherlands .028 .025 .044 .039

Norway .038 .021 .041 .033

Poland .014 .036 .031 .051

Portugal .076 .016 .030 .019

Russian Federation .021 .123 .018 .076

Sweden .050 .008 .045 .031

Slovenia .027 .019 .036 .028

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Theory

The results from the previous analysis section showed a clear division between four

distinct groups: alarmed activists, sceptics, concerned, and indecisive. While these groups are

composed based on three dimensions, it is interesting to get insight into why these groups exist.

As this lacks in most other segmentation papers, the next section will base itself around

theoretical explanations to gain insight into the differing opinions regarding climate change

believers.

Reflexive modernisation

A vital explanation of the differences regarding climate change is through holding

values concerned with reflexive modernity (Beck, Giddens & Lash, 1994). Reflexivity refers

to the way people view themselves compared to the context they live in. The term reflexive

modernity gained popularity in the late twentieth century, and can be viewed as a reality that is

perceived as opposed to as well as detached from a traditional society. One of the reasons why

this train of thought sparked, is due to increasing globalisation. This led to a revaluation of

contemporary society and the way society, as well as institutions should be structured. Societies

and its structures and institutions are not able to function properly in this new world.

Western society’s prosperity came (among other reasons) from fast economic growth

through use of fossil fuels for energy. Climate change is one of the side effects factors of the

use of fossil fuels (McCright, 2011). This leads to a critical revaluation of contemporary society

on several levels. Critique is often pointed towards institutions in society. The government is

often blamed for being incapable to deal with climate change, while at the same time retaining

economic prosperity. More often than not, environmental protection asks for political

intervention into the economic sphere. This is often mistakenly perceived to lead to a more

protectionist role from the government (Branger, & Quirion, 2014). Strong supporters of

conservatism are more likely to oppose regulations that are imposed by the government with

such effects (McCright, & Dunlap, 2011a). People favouring conservative policies are often

linked to deny climate change. This group of people usually belongs to the higher income

groups. The higher income and conservative groups have been found to state that the media

exaggerates the effects of global warming (McCright, & Dunlap, 2011b; Krange, Kaltenborn,

& Hultman, 2018). This could indicate that these higher income groups generally want to

preserve the current economic wealth they experience, and are frightened that climate

preservation policies will affect this wealth. As stated in the literature review, a study by

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Thornton et al. (2011) showed that higher income groups exercised more environmentally

unfriendly behaviour. Therefore the first hypothesis reads: People with higher incomes have a

higher likelihood to be a sceptic than to be an alarmed activist, because of adhering to reflexive

modern values and being economic conservative.

Science is often brought forward as being the main scapegoat of initiating a risky way

of prosperity (Beck, 1992). Not only has science delivered the possibility to exploit fossil fuels,

it has not proven successful in bringing the definite answer to tackle climate change. At the

same time, the fossil fuel industry and similar business endeavours have actively tried to debunk

the link that is often made between greenhouse gas emissions and climate change. This has led

to for instance research funded by companies that have monetary interest regarding the current

situation where fossil fuels are exploited (Lahsen, 2005; McCright & Dunlap, 2011a).

While higher educated often have taken preparatory scientific education, they have

shown to be critical towards scientific arguments and open to non-scientific pursuits. For

instance, higher educated groups have been linked to oppose vaccinations (Hak, Schönbeck, De

Melker, Van Essen & Sanders, 2005; Jones et al., 2012), are more likely to use alternative

medicine (Centraal Bureau voor de Statistiek, 2014), are more prone to be individualistic and

therefore more inclined to adhere to new age (Houtman, Mascini, & Gels, 2000). These are all

fields that do not hold linkages to scientific research. However, being linked to these

unscientific endeavours, does not directly mean that higher educated tend to disfavour belief in

climate change. McCright and Dunap (2011a) indeed found evidence that there is a positive

effect between education and the concern of global warming. According to Beck (1996, 1999)

contemporary risks are often due to technological advances. These are difficult to grasp, for

instance the pollution of the environment, or atomic radiation due to nuclear energy. However,

as Ekberg (2007) notes, these scientific problems are overcome, due to modern day science and

governments being able to give insights into what actually happens regarding these negative

technological advances. As these institutions are likely to articulate and show the dangers

regarding climate change at hand, higher educated are likely to trust the results stemming from

modern day science, being better able to understand this. According to Lee, Markowitz, Howe,

Ko, and Leiserowitz (2015) education is the single strongest predictor for explaining climate

change awareness. This indicates that lower educated people are more likely to be distrusting

towards climate change than higher educated people. Therefore, lower educated people have a

higher likelihood to be a sceptic than to be an alarmed activist, because of adhering to reflexive

modern values and having a lower level of institutional trust.

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In recent years, scholars have tried to grasp how media plays a role in the perception of

people regarding political issues. This sparked a wide variety of research, with the support for

climate change being one of them (Schuldt, Konrath, & Schwarz, 2011). According to research

by Hart and Nisbet (2012), the most used model to communicate about science issues, is

through the media and education. Often, facts are simply reproduced to increase the knowledge

of climate change and its dangers. This is labelled by Hart and Nisbet (2012) as the deficit

model. According to this model, people would be able to gather knowledge and form opinions

accordingly. However, this model appears to not work entirely due to motivated reasoning.

Belonging to a certain group can affect information that is presented to people. This could lead

to a so called boomerang effect (Hart & Nisbet, 2012), where the people holding a strong

opinion on an issue because of partisanship, creates a backlash against opinions that state

otherwise. For instance, a person who is a member of a sceptic political party could react

negatively against climate change policies induced by political parties that state otherwise.

While Hart and Nisbet (2012) link this motivated reasoning to partisanship, the same can be

done in a way with people adhering motives linked to reflexive modernity. People holding these

views are more likely to question a vast quantity of scientific evidence that is presented to them

as facts. That could lead to a certain backlash against these “facts”. Therefore, the possibility

exists that belief in climate change crumbles caused by reflexive values. Countries that impose

quite some regulations on people to constrain the emissions of greenhouse gasses could

therefore be reluctant to participate in restricting climate change. Myers et al. (2012) found

similar results in their study.

Looking at the first hypothesis, this would indicate that countries that implement a large

number of climate change policies, would only enhance the level of scepticism among this

group of people. If they are indeed frightened that climate preservation policies will affect their

wealth, this effect could only get stronger. Accordingly, the third hypothesis states: People with

higher incomes have a higher likelihood to be a sceptic than to be an alarmed activist, because

of adhering to reflexive modern values and being economic conservative. This effect is

positively moderated by the level of climate change policies imposed by the government of a

country.

In line with the second hypothesis, the higher educated tend to be less sceptic towards

climate change. Following this reasoning, the lower educated would be more likely to be among

the sceptics. This indeed appears to be the case (Reynolds, Bostrom, Read, & Morgan, 2010).

Therefore, the fourth hypothesis states: Lower educated people have a higher likelihood to be

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sceptic than to be an alarmed activist, because of adhering to reflexive modern values and

having a lower level of institutional trust. This effect is positively moderated by the level of

climate change policies imposed by the government of a country.

Figure 1: Conceptual model

Climate policy

performance of

country

+ +

Lower

educated

+ Reflexive

modernity

- Institutional

trust

-

Sceptics

Higher

income

+ Reflexive

modernity

+ Economic

conservatism

+

Data and methods II

To answer the second research question, the analyses are based on the data and analyses

provided in the first section of this paper. Again, the 2016 wave of the European Social Survey

was used. Based on the previous analyses, extra variables were added for climate class

membership. Also, an extra variable was incorporated into the dataset to measure the level of

performance of country’s governments on climate change policies. An overview of the

variables that were used and the full description of the variables can be found below.

All analyses for this part were performed in SPSS. First, the variable for the Climate

Change Performance Index (Burck, Marten, & Bals, 2016) was included in the existing dataset.

For the analysis, Israel is excluded as no Climate Change Performance Index (CCPI) value was

reported for this country. Before running the analysis, recodes were performed on several

variables to standardise them. After recoding the variables where deemed necessary, scale

construction techniques were used to create new scales. Again, a generalized least squares

model was used to determine if there were any components that can be distinguished. By using

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the generalized least squares model, the statistical program shows a goodness of fit table with

a chi-square statistic and degrees of freedom. The construction of these scales is noted below

at the specific descriptions of each variable.

The variables constructed from using the factor and reliability analyses were used in the

analyses, together with the original variables from the dataset. For the analyses, several

generalised linear mixed models were used in SPSS. Before these models were used, two

regression models were run. One regression was run to establish the relation of education on

trust with reflexive modernity included as a mediator. A second regression was included to get

insight into the relation of income on economic conservatism, with reflexive modernity

functioning as a mediator. A Sobel test (1982) was performed to determine if the effects are

truly mediated by reflexive modernity. Afterwards, two multilevel multinomial logistic

regressions were run. The first one compromised household’s income as the main independent

variable, while the latter one included educational level as the independent variable. Both

regressions held climate class membership as the dependent variable. Both these regressions

were hierarchical structured to garner insight into the mediating structure of the relations

between the variables. A pseudo -2 log likelihood and the BIC values were used to determine

the model fit. Incrementally, the first regression existed of eight models, additively including,

an empty model, household’s income, reflexive modernity, economic conservatism, CCPI, and

an interaction effect of CCPI on economic conservatism. The last two models are used for

control variables. The seventh model included the inclusion of a random intercept and random

effect for the country level variable, while the eight model included the control variables gender

and age. The second multinomial regression model is structured similarly, albeit with some

different variables. As stated above, the model included educational level as the main

independent variable. The first model was empty, while each successive model included extra

variables, namely educational level, reflexive modernity, institutional trust, CCPI, and an

interaction effect of CCPI on economic conservatism. The last two models are used for control

variables. Again, the seventh model included a random intercept and random effect for the

country level variable, while the final model included the control variables age and gender. The

tables that are presented will show logits and odds. In the text sometimes these values are

calculated to probabilities for easy interpretation and readability. These are calculated according

to the following formula: 𝑃 =𝑒𝑙𝑜𝑔𝑖𝑡

1+𝑒𝑙𝑜𝑔𝑖𝑡.

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Before running the models for the analyses, several assumptions had to be met. For the

multiple regression that is performed, the following assumptions were tested:

Normality. Assessing normality. The distribution of the variables used in the regression

have to be normal for the continuous variables. To assess this, the trimmed mean was studied

where 5% of the top and bottom extreme values were removed. This was compared to the actual

mean values of the variables. As the values did not differ vastly, this proves to be no problem

for the analyses. A Kolmogorov-Smirnov statistic was used to assess the normality. The null

hypothesis for these tests suggest that the variables are distributed normally. However, all

variables included were significant (p-value < .001), indicating a violation of this assumption.

However, this is common among the use of large datasets. The histograms for the variables

were studied and showed some skewness. The histogram for household's income showed that

each decile relatively held the same number of respondents. Reflexive modernity was slightly

skewed to the right. Economic conservatism was distributed fairly normal among the different

values, with a vast number of respondents among centred among the mean. This was also

indicated by the low standard deviation. The histogram for institutional trust was skewed to the

right, but was distributed fairly normal. However, the number of people among the lowest

possible score was large. As stated before, the Kolmogorov-Smirnov statistic indicated no

normality. However, as the dataset compromises a large number of respondents, and the

histograms did not indicate a large skewness among the variables, the analyses were performed

with the variables included.

Outliers. When studying the histograms of the variables, a check for outliers was also

performed. This assumption was not violated.

For the multilevel multinomial logistic model four different assumptions had to be met:

Case specific independent variable. This indicates that the different outcome variables

do not overlap in any possible way. Indeed, this is the case, as respondents can only be labelled

to either one of four different classes.

Independence of observational units. Indicating that the data does not stem from

repeated measurements, this assumption is met.

Multicollinearity. There should not be a too high relation between the different

independent variables. For testing this, a Pearson correlation was run including all independent

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variables. This assumption was met as no extremely high values were found between the

variables.

Large sample size. The rule of thumb for establishing the size of the sample, is that at

least 10 cases are needed with the least frequent outcome for every independent variable in the

model. In this case that would indicate that a sample size of at least 826 respondents is needed

(10∗9

.109). Here, 10 denotes the minimum number of cases, while 9 is the number of independent

variables that is used in the most complex model. .109 is the chance of belonging to the least

frequent outcome (in this case: alarmed activist). As the pooled dataset of the European Social

Survey is used, this number is easily met.

Table 9: descriptive statistics (variables used for multilevel latent class analysis and factor analyses)

N Minimum Maximum Mean Std. Deviation

Household's income 36445 1 (1st decile) 10 (10th decile) 5.190 2.734

Educational level (reference: ES-ISCED I)

ES-ISCED II 44258 0 1 .167

ES-ISCED IIIb 44258 0 1 .162

ES-ISCED IIIa 44258 0 1 .197

ES-ISCED IV 44258 0 1 .142

ES-ISCED V1 44258 0 1 .108

ES-ISCED V2 44258 0 1 .136

Reflexive modernity 36761 0 60 25.798 1.724

Economic conservativism 40056 0 30 14.992 6.268

Institutional trust 38849 0 70 33.157 13.796

Climate class (reference: Alarmed activists)

Sceptics 44387 0 1 .091

Concerned 44387 0 1 .326

Indecisive 44387 0 1 .490

Climate Change Performance Index 41830 44.340 70.130 58.503 6.731

Gender (reference: female) 44378 0 1 .474

Age 44232 15 100 49.140 18.613

Valid N (listwise) 32589

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Variable overview

Household’s income. This variable was used as the main independent variable for the

first regression. Household’s income was measured by using the following question from the

survey: “Using this card, please tell me which letter describes your household's total income,

after tax and compulsory deductions, from all sources?” Respondents were then shown a card

with different letters on it. Each letter was linked to a certain income group. Afterwards the

answers were recoded into 10 deciles from low to high. A control for random intercepts and

effects will be performed for this variable.

Educational level. The variable used for educational level held information based on the

ES-ISCED coding. Respondents were asked “What is the highest level of education you have

successfully completed?” These were then coded into 7 different categories, ranging from less

than lower secondary (ES-ISCED I), lower secondary (ES-ISCED II), lower tier upper

secondary (ES-ISCED IIIb), upper tier upper secondary (ES-ISCED IIIa), advanced vocational

(ES-ISCED IV), Bachelor’s degree level (ES-ISCED V1), to Master’s degree level (ES-ISCED

V2). In the first two multiple regressions the variable is included as six dummy variables, where

the group less than lower secondary educated functions as a reference category. A control for

random intercepts and effects will be performed for this variable.

Reflexive modernity. A scale was constructed for reflexive modernity. Six variables

were used in constructing this scale. These were:

- Important to follow traditions and customs. The question that was asked read “Now I

will briefly describe some people. Please listen to each description and tell me how

much each person is or is not like you. Use this card for your answer. Tradition is

important to her/him. She/he tries to follow the customs handed down by her/his religion

or her/his family.” This variable held 6 values (ranging from “very much like me” to

“not like me at all”) originally but was recoded to hold values ranging from 0 to 10 to

line up all variables for scale construction in the same manner.

- Refugees are not in real fear of persecution: “Some people come to this country and

apply for refugee status on the grounds that they fear persecution in their own country.

Using this card, please say how much you agree or disagree with the following

statements. Firstly... Most applicants for refugee status aren't in real fear of persecution

in their own countries.” This variable held 5 values (ranging from “agree strongly” to

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“disagree strongly”) originally but was recoded to hold values ranging from 0 to 10 to

line up all variables for scale construction in the same manner.

- Men have more right on jobs than women when jobs are scarce. To measure this

concept, the respondents were asked “Using this card, please say to what extent you

agree or disagree with each of the following statements. When jobs are scarce, men

should have more right to a job than women.” The values ranged from 1 (agree strongly)

to 5 (disagree strongly), but was recoded in line with the other variables.

- Gays and lesbians free to live life as they wish. Respondents were asked whether “Gay

men and lesbians should be free to live their own life as they wish”. The respondents

could answer in the range of 1 (agree strongly) to 5 (disagree strongly). This variable

was recoded in line with the other variables.

- Gay and lesbian couples have the right to adopt children. The respondents were asked

whether “Gay male and lesbian couples should have the same rights to adopt children

as straight couples.” The respondents could answer in the range of 1 (agree strongly) to

5 (disagree strongly). This variable was recoded in line with the other variables.

- Country's cultural life undermined or enriched by immigrants. To measure this concept,

the respondents were asked “And, using this card, would you say that [country]'s

cultural life is generally undermined or enriched by people coming to live here from

other countries?” Answers were coded from 0 (cultural life undermined) to 10 (cultural

life enriched). These were contra-indicative, so were reverse coded.

The choice for these variables was loosely based on the work by Achterberg, De Koster,

and Van Der Waal (2015), who were trying to measure the same concept. First, the contra-

indicative items were recoded. In that way the highest values indicated a high level of reflexive

modernity. A model was run extracting 1 factor. The Kaiser-Meyer-Olkin measure value of

.724 indicated that the data is workable for the use of a factor analysis. According to Cerny and

Kaiser (1977) values between .7 and .8 are middling for a factor analysis. Accordingly, Bartlett's

Test of Sphericity also

indicates that the

correlation matrix is not in

fact an identity matrix (p-

value < .001), and therefore

suitable for a factor

analysis. A scree plot was

Table 10: structure matrix showing the results of the factor analysis (no rotation)

Factor

1

Important to follow traditions and customs .374

Most refugee applicants not in real fear of persecution own countries .333

Men should have more right to job than women when jobs are scarce .449

Gays and lesbians free to live life as they wish .767

Gay and lesbian couples right to adopt children .773

Country's cultural life undermined or enriched by immigrants .464

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used and indicated that 1 factor could be extracted. With an eigenvalue of 2.349, the model was

able to explaining 39.153% of the variance in the variables. While the factor loadings were not

extremely high for some values, the model did prove to be reliable enough for creating a new

scale (Cronbach's alpha of .71). The scale was created by combing the variables into a new

scale. The final item held values ranging from 0 to 60.

Economic conservatism. For creating the scale for economic conservatism five items

were used in the generalized linear model initially. The Kaiser-Meyer-Olkin measure held a

value of .688, indicating that the variables that were chosen for inclusion in the factor analysis

were questionable. Indeed, the variables about child care and the basic income did not load

highly on the factor. The factor had an eigenvalue of 1.875, with an explained variance of

37.499%. The other three items loaded higher on the factor. Therefore, a second factor analysis

was performed with only three items. The Kaiser-Meyer-Olkin measure decreased to .651.

However, the Eigenvalue became 1.820, explaining 60.679%. According to the scree plot 1

factor could be extracted. The factor matrix of the model with no rotation is shown in table 11.

A reliability analysis was performed with five items initially, but were deleted as Cronbach's

Alpha would rise from .540 to .671 when the two same items were deleted. The three variables

that were used were:

- Social benefits/services place too great strain on economy. Respondents were asked

“Using this card please tell me to what extent you agree or disagree that social benefits

and services in [country].... ...place too great a strain on the economy?” Respondents

could then respond by giving an answer ranging from 1 (Agree strongly) to 5 (Disagree

strongly).

- Social benefits/services cost businesses too much in taxes/charges. Respondents were

asked the following question to measure this concept: “Using this card please tell me to

what extent you agree or disagree that social benefits and services in [country].... ...cost

businesses too much in taxes and charges?” Respondents could then respond by giving

an answer ranging from 1 (Agree strongly) to 5 (Disagree strongly).

- Social benefits/services make people lazy: Respondents could answer to the question

“And to what extent do you agree or disagree that social benefits and services in

[country] make people lazy?” by giving a response between 1 (Agree strongly) to 5

(Disagree strongly).

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All items were

contra-indicative and

therefore reverse coded and

coded from 0 to 10. The

scale for Economic

conservatism was created by adding up the variables according to classical test theory. The

scale used in the analyses holds a range from 0 to 30.

Institutional trust. Finally, a third generalized linear model was run for creating a scale

for institutional trust. The Kaiser-Meyer-Olkin measure indicated a value of .850, meaning that

the data was suitable for a factor analysis. Like the other two models, the Bartlett's Test of

Sphericity was significant (p-value < .001). The eigenvalue for this factor analysis was 4.421,

explaining 63.159% of the total variance. 1 factor could be extracted according to the scree plot.

All variables loaded highly on the factor, with all (except trust in the police) loading higher than

.7. A reliable scale was constructed (Cronbach's alpha of .901) by adding up the scores of the

variables with values ranging from 0 to 70, similarly to the previous scales. Respondents were

asked “Using this card, please tell me on a score of 0-10 how much you personally trust each

of the institutions I read out. 0 means you do not trust an institution at all, and 10 means you

have complete trust.” for the following variables:

- Trust in country's parliament

- Trust in the legal system

- Trust in the police

- Trust in politicians

- Trust in political parties

- Trust in the European Parliament

- Trust in the United Nations

All variables were measured in the

same way as stated above. Table 12 shows the structure matrix for this variable.

Climate class membership. This variable was not originally in the dataset, but was

constructed after the previous analyses section. All respondents were labelled as either one of

four different groups by the previous analysis in R: alarmed activists, sceptics, concerned, or

indecisive. A full description of the construction of this variable can be found in the first data

and methods and associated result section.

Table 11: structure matrix showing the results of the factor analysis (no rotation)

Factor

1

Social benefits/services place too great strain on economy: .731

Social benefits/services cost businesses too much in taxes/charges: .656

Social benefits/services make people lazy: .539

Table 12: structure matrix showing the results of the factor analysis (no rotation)

Factor

1

trstprl Trust in country's parliament .816

trstlgl Trust in the legal system .721

trstplc Trust in the police .555

trstplt Trust in politicians .921

trstprt Trust in political parties .905

trstep Trust in the European Parliament .714

trstun Trust in the United Nations .650

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Climate Change Performance Index. The variable for CCPI

was obtained through German Watch and Climate Action Network

Europe (Burck et al., 2016). All countries were scored on five

different aspects:

- Emissions level (30% weighting)

- Development of emissions (30% weighting)

- Renewable energies (10% weighting)

- Efficiency (10% weighting)

- Climate policy (20% weighting)

Then, this was coded into a scale with values ranging from 0 to

100. As no value was included for Israel, this country was excluded

from the analyses. Table 13 shows the CCPI for each country in

ascending order.

Gender. Gender is included as a control variable. The

variable is included as a dummy, with females functioning as the

reference category. This variable is included as males are generally

known to hold more conservative values on both economic and

climate change perspectives.

Age. Age is also included as a control variable. The variable is coded in years, and is

included as a control variable. Older people are generally known to hold more conservative

values regarding economic, cultural and climate change perspectives.

Results hypotheses

By testing the hypotheses derived from reflexive modernisation theory, it is possible to

get an answer on how belonging to the different segments regarding your perception on climate

change can be explained. Central to this, is to test whether the effects of education and income

are mediated by several variables as shown in the theory section of this paper.

Table 13: Climate Change Performance Index per country (in ascending order)

Country CCPI

Russian Federation 44,34

Estonia 47,24

Austria 50,69

Spain 52,63

Norway 54,65

Netherlands 54,84

Poland 56,09

Slovenia 56,87

Czech Republic 57,03

Iceland 57,25

Finland 58,27

Germany 58,39

Lithuania 58,65

Portugal 59,52

Hungary 60,76

Switzerland 62,09

Ireland 62,65

Italy 62,98

France 65,97

Belgium 68,73

Sweden 69,91

United Kingdom 70,13

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Multiple regression analysis

Before running the

multilevel multinomial

logistic regressions, two

regressions were run to

establish the relation between

education on institutional

trust, and income on

economic conservatism with

reflexive modernity

functioning as a mediator in

both regressions. The results

for these bivariate relations

are presented in tables 14 and

15. Focussing on table 14,

this shows the effect of

institutional trust on educational level and reflexive modernity. Model 1 shows that compared

to the reference category of less than lower secondary educated, the lower secondary educated

have a higher level institutional trust on average. Generally, the higher educated groups appear

to have a higher level of institutional trust than the lower educated groups. The two highest

educated groups, Bachelor's degree and Master's degree, respectively score 7.319 and 6.523

higher on institutional trust on average than the lowest educated group. This appears to be in

line with the conceptual model as presented in figure 1. In model 2 reflexive modernity is

included in the regression model. Reflexive modernity has a negative effect on institutional

trust, indicating that with every increase in reflexive modernity, on average institutional trust

decreases with .323, while holding all other variables constant. All values of educational level

decrease compared to the first model. This indicates that the effect of education on institutional

trust is partly mediated by reflexive modernity. A Sobel test was performed for each educational

effect. According to this test, all effects are mediated by reflexive modernity. So far, this is all

in line with hypothesis two.

A second regression was run to determine the relation between income groups and

economic conservatism, and if reflexive modernity functions as a mediator here as well. Model

1 depicts the relation between household's income and economic conservatism. The model

Table 14: Multiple regression of educational level and reflexive modernity (dependent variable: institutional trust)

Model 1 Model 2

Educational level

ES-ISCED II 1.665*** (.330) 1.241*** (.320)

ES-ISCED IIIb 2.501*** (.330) 1.849*** (.320)

ES-ISCED IIIa 2.468*** (.320) 1.768*** (.310)

ES-ISCED IV 3.782*** (.334) 2.813*** (.324)

ES-ISCED V1 7.319*** (.354) 4.848*** (.347)

ES-ISCED V2 6.523*** (.334) 4.482*** (.326)

Reflexive modernity

-.323*** (.007)

Intercept 3.339*** (-.271) 39.637*** (.331)

R2 .026 .087

N 31958 31958

* < .05; ** < .01; *** < .001

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indicates that with every increase in household's income, on average economic conservatism

decreases with .030. This indicates that people who have higher incomes in general are less

economic conservative. This is not in line with the model presented in figure 1. While the effect

is significant (p <. 05),

substantially speaking the

effect is quite low. However,

when in the second model

reflexive modernity is

included, the effect of

household's income turns

positive, with a regression

coefficient of .043 (p-value <

.01). This indicates that on

average, people who have a higher household's income are more economic conservative.

Because the effect changes from negative to positive, this indicates suppression by the newly

included variable. The effect of reflexive modernity (regression coefficient = .108, p-value <

.001) indicates that people with a higher level of reflexive modernity are more inclined to be

economic conservative. This is in line with the conceptual model as presented in figure 1.

Multilevel multinomial logistic regression analysis

Table 16 represents the multilevel multinomial logistic regression model with income

as an independent variable. For clarity purposes this table is divided into three separate tables.

The BIC and -2 log pseudo likelihood denotes the values for all three tables, but is only showed

in table 16c. All models show the likelihood of belonging to either of three classes (concerned,

indecisive, or sceptic) compared to belonging to the alarmed activists. The 0 model shows an

empty model with only an intercept. This model shows the likelihood of belonging to either one

of the climate classes. For instance, a person is less likely to be part of the sceptic class,

compared to the alarmed activists. Persons are in general more likely to belong to the concerned

group than to belong to the alarmed activists group. At the same time, people are even more

likely to belong to the indecisive group than to belong to the alarmed activists. This makes sense

as previous analyses showed that these were the two biggest groups.

The first model includes the variable for household’s income. Table 16c indicates that

people with higher incomes have a lower likelihood to be sceptic than to be alarmed activists.

Table 15: Multiple regression of household's income and reflexive modernity (dependent variable: economic conservatism)

Model 1 Model 2

Household's income -.030* (.014) .043** (.014)

Reflexive modernity

.108*** (.003)

Intercept 15.204*** (.083) 12.088*** (.129)

R2 .000 .033

N 30567 30567

* < .05; ** < .01; *** < .001

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The effect for the indecisive group is similar (table 16b), albeit less strong: people with higher

incomes have a lower likelihood to be indecisive than to be part of the alarmed activists. The

effect for the concerned group (table 16a) is also negative, indicating that people with higher

incomes are less likely to belong to the concerned group than to the alarmed activists. However,

this effect proves not to be significant. On average, people with higher income are more likely

to be alarmed activists than to be part of any of the other three groups. This is not in line with

the first hypothesis.

In the second model the variable for reflexive modernity is added. With the inclusion of

this variable, the significant effects for household’s income becomes less strong, indicating that

the effect is partially mediated by reflexive modernity. Comparing the group of sceptics to the

group of alarmed activists, the effect for reflexive modernity shows a positive logit of .119 (p-

value < .001). This means that with every increase in reflexive modernity, people will have a

higher likelihood to belong to the sceptic group versus belonging to the alarmed activists when

holding all other variables constant. The effect of reflexive modernity denotes a logit of .086

(p-value < .001), indicating that people who score higher on reflexive modernity are generally

more likely to belong to the indecisive group than the alarmed activists when holding all other

variables constant. The same holds for the group of concerned versus the alarmed activists.

Noteworthy, all effects tend to become closer to zero for each group that holds values closer to

the alarmed activists. For instance, the concerned seem to be closer to the alarmed activists than

the indecisive.

The third model also includes the variable for economic conservatism. All variables that

were included in the previous model tend to get a bit closer to zero. This again hints at partial

mediation of the effect of the independent variable on climate class membership. The effect of

economic conservatism is very small for the sceptic group and is not significant. The logit for

the indecisive group holds a value of .025 (p-value < .001), indicating that in general with every

increase of economic conservatism, a person becomes more likely to belong to the indecisive

group than to be part of the alarmed activists, when holding all other variables constant. A

similar effect (logit = .024; p-value <.001) can be found for the concerned group.

Model four includes the country level effect of CCPI. The logit for CCPI is negative (p-

value <.001) for sceptics, indicating that countries with a higher level of CCPI, tend to have a

lower likelihood to have sceptics among its population than to have alarmed activists, when

holding all other variables constant. The logit for CCPI for the indecisive group is similarly

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negative (p-value < .001), although a bit closer to zero. The effect is not significant for the

concerned class.

The fifth model includes the interaction effect of CCPI on the effect of conservatism.

While significant (p-value < .01) for sceptics, the logit shows a value of .002. This indicates

that CCPI indeed strengthens the effect of economic conservatism, meaning that with every

increase in CCPI, the logit effect of economic conservatism increases with .002. The effect is

similarly small for indecisive (logit = .001, p-value < .01). For the group of concerned the effect

is close to non-existing.

Model six allows for variance for both the intercept, as well as household’s income.

Table 16c shows a significant country variance of .599 (p-value < .01), meaning that the

intercept household’s income varies positively over countries for sceptics versus alarmed

activists. However, the slopes for household’s income are not significant or have any substantial

value, indicating that there is no variance in slopes. For the indecisive and the sceptic group the

random effect of household’s income did not vary either. The intercept for the indecisive and

the sceptic group did allow for some variance, although lower than for sceptics, with respective

values of .264 (p-value < .01) and .051 (p-value < .05).

The seventh model includes two control variables in age and gender. The inclusion of

these variables does not change the effects for the concerned and indecisive, but does so

drastically for the sceptic group. The effect for gender holds a logit of .408 (p-value < .001)

indicating that the likelihood of being part of the sceptic class versus the alarmed activists is

higher for men than for women, while holding all other variables constant. In terms of

probability, men have a 60% higher probability to be a sceptic than to be an alarmed activist.

Also the effects for household’s income (p-value < .001) and for CCPI (p-value < .01) become

negative.

This analysis indicates that the first hypothesis is refuted. On average, people with

higher incomes do not tend to be more sceptic than to be an alarmed activist. While the effect

for economic conservatism was positive and significant for the indecisive and the concerned,

the effect for sceptics was not. Still, reflexive modernity tends to have a positive effect in

general on belonging to the sceptic classes compared to the alarmed activists. When the first

hypothesis is refuted, this immediately means that the third hypothesis is so as well. Still, only

partially so, because indeed the interaction effect of CCPI is positive and significant. However,

this only holds for the concerned and indecisive groups.

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Noteworthy is that with every new model, the BIC values and -2 log pseudo likelihood

increase vastly. This indicates that the more parsimonious models are favoured over the latter

ones. However, in model six where the random effects are included the values drop, hinting at

a better fit of the model.

Table 17 represents the multilevel multinomial logistic regression model with

educational level as an independent variable. Just as with the previous table, for clarity purposes

this table is divided into three separate tables. The BIC and -2 log pseudo likelihood denotes

the values for all three tables, but is only showed in table 17c. All models show the likelihood

of belonging to either of three classes (concerned, indecisive, or sceptic) compared to belonging

to the alarmed activists. The 0 model includes no variables and shows the same information as

described for table 16.

The first model of table 17 includes the dummy variables for educational level. In this

model, the lowest educational level is used as the reference category. Table 17a shows the

effects for the concerned group compared to the alarmed activists. Compared to the reference

category for educational level, the highest educated are less likely to be part of the concerned

group than to be part of the alarmed activists. With a logit of -.661 (p-value < .05), the

probability of belonging to the highest educated and being part of the concerned, compared to

the alarmed activists is .341. This effect is only stronger when comparing the other two groups

with the alarmed activists. The highest educated group is even less likely to be part of the

indecisive group than the alarmed activists with a probability of .184. For the highest educated

group of people to be part of the sceptics is even less likely with a probability of .169. Further,

every educational dummy holds a negative logit indicating that every educational level higher

than the lowest level is less likely to be part of the respective class versus the alarmed activists.

The higher educational levels give even more negative logit values. Indeed, from the analysis

it can be stated that lower educated groups are more likely to be among the sceptic than to be

an alarmed activists. So far, this is in line with the second hypothesis.

The second model includes the variable for reflexive modernity. All effects for

education decrease in strength compared to the previous model, hinting at mediation.

Noteworthy is that the effect is positive for all three groups, indicating that with every increase

in reflexive modernity people are more likely to be part of any of the three groups when

compared to alarmed activists when holding all other variables constant. Interestingly, the value

is slightly stronger for the indecisive when comparing them to the concerned. Also, the effect

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is strongest for the sceptics when comparing them to the two other groups. Higher levels of

reflexive modernity seem to push people more in the direction of the more dismissive.

The following model also includes the effect for institutional trust. Interestingly, while

the previous models showed similar effects for all groups (albeit stronger for the more

dismissive groups), the effect of institutional trust differs for each group. Compared to alarmed

activists, every increase in institutional trust, increases the logit on average for the concerned

group while holding all other effects constant. This indicates that people with higher level of

institutional trust are more likely to be part of the concerned compared to being part of the

alarmed activists. However, with a logit of .010 (p-value < .001), this effect is very small. The

level of institutional trust did not yield a significant result for the indecisive group compared to

the alarmed activists. Table 17c shows the effect of institutional trust for the sceptics compared

to the alarmed activists. With a negative logit, this indicates that with every increase in

institutional trust, the likelihood of being part of the sceptics versus the alarmed activists

decreases, while holding every other variable constant. This is in line with the conceptual

model. While the effect for the educational dummies diminishes further with the inclusion of

institutional trust for the indecisive and sceptic classes, the educational effects tend to gain

strength again when looking at the effect for the concerned. This hints at suppression by the

institutional trust variable for the model where concerned is put against the alarmed activists.

Model four also includes the effects for the variable CCPI. While the effect proves not

to be significant for the concerned group compared to the alarmed activists, it is significant for

the two other classes compared to the alarmed activists. The CCPI logit holds a value of -.019

(p-value < .001) in table 17b. This indicates that with every one increase in CCPI score on a

country level, the logit of belonging to the indecisive group compared to the alarmed activists

decreases with .019 while holding the other variables constant. A similar result can be found

for the sceptic group, although stronger with a logit value of -.049 (p-value < .001). This

indicates that with every 1 increase in CCPI the logit of belonging to the sceptic class decreases

with .049 while holding all other variables constant. In general, this hints in the direction of

countries with more policies on limiting the effects of climate change, breed citizens who are

more worried about climate change.

The fifth model includes the interaction effect necessary for testing hypothesis four.

While not significant for the models for the concerned and indecisive classes, the effect was

small, but significant for the sceptic class. The logit held a value of -.001 (p-value < .001),

indicating that the effect of institutional trust decreases in strength with every increase of CCPI.

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42

This is a very small effect and not in line with the fourth hypothesis. Therefore, this hypothesis

is refuted.

Model six includes random intercepts and effects for the educational level variables. For

all three classes, the intercept varies positively across countries. For the concerned group, the

value for the random intercept was .043 (p-value < .05) compared to the alarmed activists. The

random intercept for the indecisive held a value of .324 (p-value < .01) compared to the alarmed

activists. The random intercept for the sceptics was even higher, with a value of .594 (p-value

< .05). This indicates that the intercept for the more dismissive groups showed more variance

compared to the alarmed activists. With the inclusion of the random intercepts, the interaction

effect in table 17c becomes insignificant. Speaking both substantially and significantly, none

of the random effects for educational level were significant.

The last model includes the control variables for age and gender. Only for the sceptic

class compared to the alarmed activists was the effect for gender significant. With a logit of

.328 (p-value < .01), this indicates that males compared to females tend to be more likely to be

part of the sceptic group compared to the alarmed activists.

The analysis indicates that the second hypothesis is accepted. Indeed lower educated

tend to be more likely to be among the sceptics regarding climate change, compared to the

alarmed activists. Moreover, compared to the alarmed activists each subsequent class that is

more dismissive of climate change appears to consist of more and more lower educated people.

For the sceptic group, the effect sizes show that the there is some form of mediation through

reflexive modernity and institutional trust. This does not appear to be the case with for the other

two classes when compared to the alarmed activists. The interaction effect as stated in

hypothesis four was not found. There only appeared to be a significant effect for the sceptic

group compared to the alarmed activists. However, this effect was negative. Therefore, this

hypothesis is refuted.

Table 16a: Hierarchical multilevel multinomial logistic regression: concerned (reference category = alarmed activists) group membership explained by household’s income (showing logits, standard errors (in brackets) and odds; maximum likelihood estimation; N = 32589)

Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds

Fixed effects

Household's income

-.012 (.007)

.989 .014 (.080)

1.014 .011 (.008)

1.011 .012 (.008)

1.012 .012 (.008)

1.012 .011 (.009)

1.011 .013

(.009) 1.013

Reflexive modernity

.041*** (.020)

1.042 .036*** (003)

1.037 .036*** (.003)

1.036 .036*** (.003)

1.037 .036*** (.004)

1.037 .034*** (.004)

1.035

Economic conservatism

.024*** (.004)

1.024 .024*** (.004)

1.024 .035 (.003)

1.036 .032 (.041)

1.033 .031

(.040) 1.031

CCPI

-.002 (.003)

.998 .002 (.008)

1.002 -.002 (.01)

.998 -.004 (.009)

.996

Economic conservatism*CCPI

-.000 (.001)

1.000 -.000 (.001)

1.000 -.000 (.001)

1.000

Age

.003 (.001)

1.003

Male

-.013 (.057)

.987

Intercept 1.248***

(.018) 3.483 1.290***

(.046) 3.632 .283***

(.071) 1.327 .055

(.081) 1.057 .169

(.207) 1.184 -.035

(.493) .965 .202

(.601) 1.224

.173 (.579)

1.189

Random effects

Household’s income

.000 (.000)

1.000 .000

(.000) 1.000

Intercept

.051* (020)

1.052 .052* (.021)

1.053

* < .05; ** < .01; *** < .001

Table 16b: Hierarchical multilevel multinomial logistic regression: indecisive (reference category = alarmed activists) group membership explained by household’s income (showing logits, standard errors (in brackets) and odds; maximum likelihood estimation; N = 32589)

Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds

Fixed effects

Household's income

-.089*** (.007) .915

-.028*** (.008) .972

-.027*** (.008) .973

-.028*** (.008) .972

-.028*** (.008) .972

-.033** (.010)

.967 -.034** (.010)

.967

Reflexive modernity

.086*** (.002) 1.09

.081*** (.003) 1.085

.078*** (.003) 1.081

.077*** (.003) 1.080

.072*** (.006)

1.075 .069*** (.005)

1.071

Economic conservatism

.025*** (.004) 1.025

.029*** (.004) 1.029

-.057 (.033) .945

-.017 (.060)

.983 -.021 (.059)

.979

CCPI

-.020*** (.003) .980

-.041*** (.008) .960

-.038* (.016)

.963 -.041* (.016)

.960

Economic conservatism*CCPI

.001** (.001) 1.001

.001 (.001)

1.001 .001

(.001) 1.001

Age

.004* (.002)

1.004

Male

.128 (.079)

1.137

Intercept 1.641***

(.017) 5.159 2.045***

(.044) 7.728 -.381***

(.071) .683 -.646***

(.081) .524 .578

(.208) 1.783 1.821***

(.495) 6.177 1.761

(1.027) 5.818

1.749 (1.037)

5.751

Random effects

Household’s income

.000 (.000)

1.000 .000

(.000) 1.000

Intercept

.264** (.093)

1.302 .269** (.095)

1.309

* < .05; ** < .01; *** < .001

Table 16c: Hierarchical multilevel multinomial logistic regression: sceptic (reference category = alarmed activists) group membership explained by household’s income (showing logits, standard errors (in brackets) and odds; maximum likelihood estimation; N = 32589)

Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds

Fixed effects

Household's income

-.158*** (.010) .854

-.080*** (.011) .923

-.078*** (.011) .925

-.084*** (.011) .919

-.084*** (.011) .919

.012 (.009) 1.012 -.110***

(.019) .896

Reflexive modernity

.119*** (.003) 1.126

.115*** (.003) 1.122

.105*** (.003) 1.111

.105*** (.003) 1.110

.036 ***(.004)

1.037 .091*** (.008)

1.096

Economic conservatism

.006 (.005) 1.006

.017** (.005) 1.017

-.079 (.043) .924

.033 (.041) 1.033 -.050 (.059) .951

CCPI

-.049*** (.004) .952

-.073*** (.011) .930

-.002 (.009) .998 -.058** (.022)

.944

Economic conservatism*CCPI

.002** (.001) 1.002

-.000 (.001) 1.000 .001 (.001) 1.001

Age

.005 (.003) 1.005 Male

.408*** (.110)

1.504

Intercept -.040

(.023) .961 .720*** (.055) 2.055

-2.740*** (.104) .065

-2.751 (.120) .064

.206 (.291) 1.228

1.596 (.668) 4.934

.204 (.597) 1.226 .566

(1.352) 1.761

Random effects

Household’s income

.003 (.002) 1.003 .003 (.002) 1.003

Intercept

.599** (.211)

1.820 .608** (.214)

1.837

BIC 62.766 351.656 11344.076 37266.852 60014.615 60016.65 346554,331 346476.733

-2 log pseudo likelihood 3.842 288.958 11251.354 37143.772 5986.765 59832.031 346492,795

346415.204

* < .05; ** < .01; *** < .001

Table 17a: Hierarchical multilevel multinomial logistic regression: concerned (reference category = alarmed activists) group membership explained by household's income (showing logits, standard errors (in brackets) and odds; maximum likelihood estimation; N = 32589)

Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds

Fixed effects

Educational level

ES-ISCED II

-.100 (.092) .905 -.016 (.100) .984

-.072 (.107) .931

-.066 (.107) .936

-.062 (.107) .940 -.170 (.090) .844 -.153 (.084) .858

ES-ISCED IIIb

-.092 (.092) .912 -.050 (.099) .951

-.075 (.105) .927

-.067 (.106) .935

-.066 (.106) .937 -.107 (.109) .899 -.104 (.107) .901

ES-ISCED IIIa

-.251** (.088) .778

-.182 (.095) .834

-.232* (.101) .793

-.221* (.101) .802

-.217* (.101) .805

-.310*** (.080) .734

-.295*** (.079) .745

ES-ISCED IV

-.216* (.092) .806 -.077 (.099) .926

-.142 (.105) .868

-.127 (.105) .881

-.122 (.105) .885 -.187 (.097) .830 -.179 (.095) .836

ES-ISCED V1

-.496***

(.091) .609 -.218* (.098) .804

-.305** (.104) .737

-.295** (.105) .745

-.290** (.105) .748

-.398** (.123) .672

-.389** (.119) .677

ES-ISCED V2

-.661***

(.087) .516 -.414***

(.094) .661 -.468***

(.100) .626 -.454***

(.101) .635 -.448***

(.101) .639 -.530***

(.085) .589 -.522***

(.083) .593 Reflexive

modernity .037*** (.002) 1.038

.041*** (.002) 1.042

.041*** (.002) 1.042

.041*** (.002) 1.042

.040*** (.003) 1.040 .039*** 1.040

Institutional trust

.010*** (.002) 1.01

.010*** (.002) 1.010 .024 (.014) 1.024 .014 (.013) 1.014 .015 (.013) 1.015

CCPI .001 (.003) 1.001 .009 (.009) 1.009 -.001 (.010) .999 -.001 (.010) .999 Institutional

trust*CCPI -.000 (.000) 1,000 .000 (.000) 1.000 -.000 (.000) 1.000

Age .001 (001) 1.001 Male -.010 (.050) .990

Intercept

1.248*** (.018) 3.483

1.549*** (.077) 4.708

.595*** (.098) 1.813 .226 (.122) 1.253 .154 (.237) 1.167

-.343 (.536) .710 .437 (.648) 1.549 .417 (.628) 1.518

Random effects ES-ISCED II .003 (.005) 1.003 .002 (.005) 1.002 ES-ISCED IIIb .001 (.008) 1.001 .003 (.008) 1.003 ES-ISCED IIIa .000 (.000) 1.000 .000 (.000) 1.000 ES-ISCED IV .000 (.000) 1.000 .000 (.000) 1.000 ES-ISCED V1 .000 (.000) 1.000 .000 (.000) 1.000 ES-ISCED V2 .000 (.000) 1.000 .000 (.000) 1.000 Intercept .043 (.021)* 1.044 .042 (.021) 1.043

* < .05; ** < .01; *** < .001

Table 17b: Hierarchical multilevel multinomial logistic regression: indecisive (reference category = alarmed activists) group membership explained by educational level (showing logits, standard errors (in brackets) and odds; maximum likelihood estimation; N = 32589)

Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds

Fixed effects

Educational level

ES-ISCED II

-.316***

(.088) .729 -.152 (.098) .859

-.146 (.105) .864

-.209* (.105) .812

-.204 (.105) .815

-.397*** (.079) .672

-.367*** (.081) .693

ES-ISCED IIIb

-.497***

(.088) .608 -.316** (.097) .729

-.277** (.104) .758

-.347** (.104) .707

-.345** (.104) .709

-.358*** (.126) .699

-.343** (.126) .710

ES-ISCED IIIa

-.602***

(.084) .548 -.391***

(.093) .676 -.374***

(.099) .688 -.427***

(.099) .653 -.423***

(.099) .655 -.704***

(.094) .494 -.675***

(.101) .509

ES-ISCED IV

-.653*** (.088) .520

-.334** (.097) .716

-.340** (.1030 .712

-.400*** (.104) .671

'-.394*** (.104) .674

-.651*** (.127) .522

-.636*** (.131) .529

ES-ISCED V1

-1.358***

(.087) .257 -.721***

(.097) .486 -.686***

(.103) .503 -.744***

(.104) .475 -.740***

(.104) .477 -1.035***

(.124) .355 -1.008***

(.123) .365

ES-ISCED V2

-1.487*** (.083) .226

-.962*** (.092) .382

-.941*** (.099) .39

-1.023*** (.100) .359

-1.017*** (.100) .352

-1.306*** (.082) .271

-1.289*** (.084) .276

Reflexive modernity

.083*** (.002) 1.086

.084*** (.002) 1.087

.081*** (.002) 1.084

.081*** (.002) 1.084

.071*** (.005) 1.073

.069*** (.005) 1.071

Institutional trust .002 (.002) 1.002 .001 (.002) 1.001 .018 (.014) 1.018 .009 (.022) 1.009 .010 (.022) 1.010

CCPI

-.019*** (.003) .981

-.009 (.008) .991 -.022 (.021) .978 -.023 (.022) .977

Institutional trust*CCPI

-.000 (.000) 1,000 -.000 (.000) 1.000 -.000 (.000) 1.000

Age .002 (.002) 1.002 Male .087 (.075) 1.091

Intercept

1.641*** (.017) 5.159

2.376*** (.074) 1.757 .016 (.096) 1.016

-.099 (.120) .905

1.152 (.236) 3.163 .574 (.528) 1.775

1.998 (1.332) 7.372

1.892 (1.314) 6.632

Random effects ES-ISCED II .000 (.000) 1.000 .000 (.000) 1.000 ES-ISCED IIIb .004 (.008) 1.004 .006 (.008) 1.006 ES-ISCED IIIa .000 (.000) 1.000 .000 (.000) 1.000 ES-ISCED IV .013 (.009) 1.031 .013 (.009) 1.013 ES-ISCED V1 .000 (.000) 1.000 .000 (.000) 1.000 ES-ISCED V2 .014 (.009) 1.014 .014 (.009) 1.014

Intercept .324

(.117)** 1.383 .332** (.120) 1.394

* < .05; ** < .01; *** < .001

Table 17c: Hierarchical multilevel multinomial logistic regression: sceptic (reference category = alarmed activists) group membership explained by household's income (showing logits, standard errors (in brackets) and odds; maximum likelihood estimation; N = 32589)

Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds Logit Odds

Fixed effects

Educational level

ES-ISCED II

-.270** (.103) .763

-.119 (.117) .888

-.066 (.127) .936

-.249 (.127) .780

-.237 (.127) .789

-.461*** (.084) .630 -.422*** (.081) .656

ES-ISCED IIIb

-.589***

(.104) .555 -.380** (.117) .684

-.286* (.127) .751

-.483*** (.128) .617

-.475*** (.128) .622

-.530*** (.126) .589 -.523*** (.123) .593

ES-ISCED IIIa

-.650***

(.100) .522 -.427***

(.112) .653 -.373** (.121) .689

-.568*** (.122) .567

-.553*** (.122) .575

-.990*** (.095) .371 -.947*** (.338) .388

ES-ISCED IV

-.779***

(.105) .459 -.504***

(.119) .604 -.430** (.129) .651

-.692*** (.130) .501

-.668*** (.131) .513

-1.059*** (.122) .347

-1.032*** (.127) .356

ES-ISCED V1

-1.832***

(.117) .16 -1.055***

(.131) .348 -.881***

(.140) .414 -1.081***

(.141) .339 -1.063***

(.141) .345 -1.428***

(.138) .240 -1.381***

(.128) .251

ES-ISCED V2

-1.593*** (.104) .203

-.988*** (.117) .372

-.858*** (.126) .424

-1.171*** (.128) .310

-1.147*** (.129) .318

-1.553*** (.134) .212

-1.528*** (.124) .217

Reflexive modernity

.112*** (.003) 1.119

.109*** (.003) 1.115

.100*** (.003) 1.106

.101*** (.003) 1.106

.089*** (.007) 1.093 .084*** (.007) 1.088

Institutional trust

-.015***

(.002) .985 -.015***

(.002) .985 .033 (.018) 1.034 .013 (.024) 1.013 .014 (.024) 1.015

CCPI

-.049*** (.004) .952

-.023* (.011) .977 -.038 (.030) .963 -.040 (.031) .961

Institutional trust*CCPI

-.001** (.000) .999 -.001 (.000) .999 -.001 (000) .999

Age .004 (.002) 1.004 Male .328** (.102) 1.388

Intercept -.040 (.023) .961

.772*** (.085) 2.163

-2.483*** (.123) .083

-2.054*** (.159) .128

1.240*** (.321) 3.454

-.245 (.657) .783

1.381 (1.894) 3.978 1.202 (1.908) 3.326

Random effects ES-ISCED II .018 (.019) 1.018 .015 (.018) 1.015 ES-ISCED IIIb .000 (.011) 1.000 .001 (.012) 1.001 ES-ISCED IIIa .000 (.000) 1.000 .000 (.000) 1.000 ES-ISCED IV .003 (.013) 1.003 .004 (.013) 1.004 ES-ISCED V1 .065 (.044) 1.067 .056 (.042) 1.058 ES-ISCED V2 .057 (.044) 1.059 .049 (.040) 1.050 Intercept .594 (.247)* 1.812 .634* (.256) 1.885 BIC 62.766 396.872 9344.215 56741.144 68905.327 68927.035 392409.031 391970.748 -2 log pseudo

likelihood 3.842 173.465 9093.217 56461.094 68905.327 68584.752 392191.237 3917523996

* < .05; ** < .01; *** < .001

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Conclusion

The main goal of this study was to answer two separate, but interlinked research

questions: what homogenous cross-country groups of people exist based on their beliefs

regarding climate change? - and - how can belonging to the different segments regarding belief

of climate change be explained? Whereas previous studies regularly focus on the first part,

authors often neglect to explain the membership of these different segments. Because of these

questions, this paper can roughly be distinguished in two parts. The first part of this paper

focusses on the first question, trying to get insight into what different groups can be

distinguished regarding the opinions of people on climate change. According to the literature

study, four different dimensions could be distinguished on which people could be segmented

into different groups. These dimensions were: the level of awareness, the level of concern, the

influence mankind could have on climate change, and the level of efficacy. Through inspection

of 2016 European Social Survey data, only three different dimensions could be distinguished.

The level of awareness and concern were labelled as being the same dimension. Based on these

three dimensions, four major cross-country groups could be distinguished by utilising a

multilevel latent class analysis approach: the alarmed activists, the concerned, the indecisive,

and the sceptics. Each class compromises people based on their level of concern, the level of

influence on climate change thought possible, and the level of efficacy of people to tackle

climate change.

The second part of the paper tries to answer the second question. Having established

four different classes, further theoretical and statistical research was needed to determine why

people belong to either of these groups. Hypotheses derived from reflexive modernisation

theory were formed and tested through multiple regressions and multilevel multinomial logistic

regression models. The theory proved to be partially capable of explaining differences in the

classes. Reasoned was that lower educated tend to be more likely to be among the sceptics.

Indeed this seemed to be the case, where the effect was mediated by reflexive modern values

and a lower level of institutional trust. However, reasoning that higher income groups would be

more likely to be among the sceptic group was refuted. The idea that this is due to people being

more sceptic because of reflexive modern values and holding economic conservative values did

not appear to be true. Further analyses based on the ideas by Hart and Nisbet (2012) tried to

point out if a high input of the government on tackling climate changes problems and associated

coverage on climate change would lead to a backlash among the more sceptic people. This

turned out to be not the case.

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When applying weights to the initial dataset, the choice was made to include two

weights. Another possible weight could have been applied in the dataset as a preparation. Some

groups or regions have a higher possibility to be included in the dataset. The current dataset of

the 2016 wave of the European Social Survey does supply a weight to adjust this error.

However, this weight was not applied as it is not meaningful to include both a weight for post-

stratification (the one that is used in this study) and a design weight. This is in line with what

The European Social Survey states, who advises to apply the post-stratification weight instead

of the design weight.

The initial proposed four dimensions were not found in the factor analysis. Instead, the

analyses showed that three different dimension could be distinguished. Although not the

approach and not the intention of this study, a deductive approach could help with forming a

better reasoned collection of dimensions. This could further lead to a better construction of

classes.

Both scales for influence and efficacy were only moderately reliable. The choice was

made to continue with these scales based on the fact that the variables measured similar

concepts on face value. Besides this, there were no other better options available to measure the

constructs. Also, the scale for influence focused primarily on the effect of limiting energy use.

It would have been better to measure the concept if a wider variety of options was available

regarding the influence of mankind on climate change. Before performing the latent class

analysis, the values of the scales for concern, influence and efficacy had to be rounded to the

closest positive integer. This led to the loss of some variance in these variables. Therefore,

caution is needed before making conclusions about the presented results of this study. Future

methodological research could focus on creating techniques that could use the full range of

variance of variables, to create more precise measurements and results.

When looking at the different classes that were created, it becomes clear that all four

classes can basically be lined up from high to low on all scales. Indeed, the alarmed activists

score high on concern, as well as on influence and efficacy. The concerned group scores slightly

lower on all three scales, while the Indecisive score even lower on the scales. Finally, the

sceptics scores the lowest on all three scales. This would indicate that between the three scales

some form of correlation should exist. Future research should point out if a latent class analysis

would be necessary. When one creates similar scales based on a factor analysis and a reliability

analysis, it would be interesting to check the coherence between all scales. If the coherence

would be high, it would not be necessary to perform a latent class analysis, but groups could be

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made simply based on the newly created scales. Interestingly enough, the sceptic group scored

lower on influence than on efficacy, creating the idea that mankind has limited influence on

climate change, but still some action needs to be taken. Further research should try to explain

this.

Even though most hypotheses are refuted, reflexive modernisation theory could prove

useful in explaining the differences in climate change perception. Still, differences between the

levels of education proved to be explainable. However, further research that focusses on the

usefulness of the theory for explaining climate change opinions is needed. Other mediating

variables that could explain the relation between climate change opinions and reflexive

modernity should be taken into account. Political partisanship could be a mediating variable

here as a higher level of reflexive modernity could push people towards certain political parties

that hold certain opinions on climate change. In turn, this can form the opinion of people on

climate change. The influence of media coverage should not be forgotten in this aspect either,

as the media forms an important source of information for people on a variety of aspects,

including climate change. Here, the boomerang effect could be tested again. Furthermore, as

the conceptual model is complex, it could be useful for replication studies to perform a path

analysis model. This could help gaining further insight into the relations between the different

(mediating) variables.

The scale for reflexive modernity is perhaps problematic. As it tried to resemble the

scale created by Achterberg et al. (2015) as closely as possible, it is difficult to do so when

using secondary data. Future researchers who have the possibility to collect data could probably

replicate the second part of this study while creating a better scale for reflexive modernity.

Further, CCPI is included as a measurement for the level of input a country’s government puts

into tackling climate change. However, this does not immediately mean that it noticeably affects

citizens directly. Communication from the government about a certain topic could be low, while

they put high levels of effort into tackling the problem. Therefore, perhaps a better way of

establishing the effect of a backlash on the country’s policy on tackling climate change could

be by incorporating different variables from the public opinion. As stated before political

partisanship or voting behaviour are interesting variables that could be incorporated here.

The multilevel multinomial logistic regression models show that with every new model,

the BIC values and -2 log pseudo likelihood increase. This indicates that the less complex

models are favoured. However, as the intention of this study was to explain the different

Tom Welman u294874

52

opinions about climate change based on the theory of reflexive modernisation, not much value

is attached to this pseudo measurement of model fit.

As the dependent variable in the second analysis are the four classes, an analysis could

be performed on the posterior probability scores of each individual respondent. Instead of a

multinomial logistic regression, another technique could be used where respondents are not

labelled to either one group but are given scores on their likelihood of being part of a group.

Tom Welman u294874

53

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