Uppsala University Department of Statistics Bachelor Thesis, Spring 2017 Supervisor: Mattias Nordin

Mass media’s influence on attitudes

towards the EU Do people with different levels of news consumption differ in their

attitude towards the EU?

Madeleine Larsson

Abstract

The news media is an important institution for all democracies. It helps the citizens to

keep informed and be able to take part of the public debate, but in recent years the

gap between the active and the inactive news consumer has increased. Does it

make any difference? In order to contribute to the field, this research paper is to

make a quantitative analysis to look at whether people with a high consumption of

news from the Swedish mass media differ in their attitude towards the EU.

As an ordered logistic regression was not applicable when analyzing the categorical

dependent variable, that are measuring attitudes towards the EU, three binary

logistic regressions was instead used. The results show that individuals with a high

consumption of news from the Swedish mass media have higher odds of having an

opinion of a positive attitude toward the EU. The data used are however self

provided and voluntary survey­data, which contain various biases. The fact that it is

only observed ­ and not experimental data ­ makes it impossible to estimate a causal

effect, which instead is up to future research.

Keywords:

Attitudes, EU, news consumption, logistic regression, Swedish mass media

1

Table of contents

Introduction 3

1. Theory 4 1.1 Background 4

2. Data 6 2.1 Swedish mass media 8 2.2 Descriptive statistics of Attitudes towards EU 9

Table 1 ­ Generally speaking ­ what is your attitude towards the EU? 10

3. Methodology 10 3.1 The model 11

3.1.1 Probabilities 11 3.1.2 Odds 12

3.2 Independent variables 13 Table 2 ­ The Dependent Variable 15 Table 3 ­ Personal Attributes 15 Table 4 ­ Professional life 15 Table 5 ­ European background 16 Table 6 ­ Political belief 16 Table 7 ­ News Consumption 17

3.3 Levels of media consumption 17 3.4 The “no opinion” category 18

4. Results 19 4.1 “No opinion” vs. “opinion” 19

Table 8 ­ “No opinion” vs. “Opinion” 20 4.2 Analysis of attitudes towards the EU 21

4.2.1 Positive Attitude vs. Not Positive Attitude 22 Table 9 ­ Positive attitude vs. Not Positive Attitude 22

4.2.2 Not Negative vs. Negative Attitude 22 Table 10 ­ Not Negative attitude vs. Negative Attitude 23

4.2.3 Neutral attitude vs. Not Neutral Attitude (Positive + Negative) 23 Table 11 ­ Not Neutral attitude vs. Neutral Attitude 25

4.3 Overall analysis 25 Table 12 ­ Overall Analysis 26

5. Discussion and Conclusion 27

References 28

Appendix 30

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Introduction

News media is a cornerstone in any society, no matter if it gets used to oppress the

citizens (in authoritarian regimes), boost a community's citizens during wartime or

simply inform about the news. What was previously perceived as a citizen's duty to

keep informed and take part of the public debate, is now rather a question of

preference, as the gap between news readers and never­readers increase (Aalberg,

2013). In a new era where extreme parties are becoming normalized around the

world, and some western politicians rather see media as a threat that has to be

controlled (e.g. Trump), than to be protected, these are all worrying signs. But does it

make a difference? Do people in the 21st century that consume a lot of news

perceive the world differently than those who do not? And, if they do, in what way?

This research paper will not answer all these questions, but by analysing data from

the SOM­institute’s 2015 survey, wish to do an opening research to see if it is

something there to build from. As 2015 was a year when a lot of light was shed on

the turbulence within the EU, e.g. terror (in Paris) and discussions of “Grexit” and

“Brexit”, the attitude towards EU is a good focus of research (Berg, et al, 2016). By

limiting the scope, this essay seeks to perform a deeper analysis regarding attitudes

which can help future and wider research of the influence by mass media in the 21st

century. By examining attitudes towards EU in relation to news consumption by the

Swedish population the essay seek to answer the question “Do people with different

levels of news consumption differ in their attitude towards the EU?” and in a wider

sense, is it possible that the mass media has influenced this attitude.

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1. Theory

1.1 Background

It is easier and faster than ever before in history to access news. Technology has

made it possible to access the latest updates from around the world within seconds,

wherever you are. The news can be obtained from several different sources ­ from

traditional papers to radio, tv or via internet (on a computer or smartphone). You no

longer need an expensive subscription to keep informed. News agencies is a

cornerstone of any democracy and have a great responsibility within any society.

They not only form the platform for the public debate but also serve as an informer of

politics and society to the public. Despite the importance and openness of the mass

media in today’s Sweden, research show that the gap between news readers and

never­readers has broadened (Lindell, 2016). By avoiding reading the news, one

miss a big part of the public debate but also lose information (and in the long term

understanding) of processes within a society. If you do not have personal experience

with it in other ways, a lot of functions and institutions therefore may appear to be

more distant and out of your / the people’s control than for people taking part in the

consumption of news.

EU is one such example of an institution which may seem distant if you do not

consume news frequently. EU­upplysningen (2016) showed that around 30% of all

new laws and regulations passed in Sweden refer directly to EU’s directions and a

total of 60% of all queries brought up in communal council is affected by EU. This

clearly show that Sweden’s membership in the EU affect our lives every day, and as

of 2017­ has done so for the past 22 years . Despite this, only 51 % of the Swedish 1

eligible population cast their vote in the 2014 EU­election. Although this is a record

number of voters in a EU election in Sweden (lowest numbers were recorded in 2004

with merely around 38 %) it is around a 35 percentage points difference in

1 Sweden became a member of the EU, Jan 1995

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participation compared to the 2014 election of the national parliament (which had a

85,8 % participation rate) (Statistiska Centralbyrån, 2014).

Cohen (1963) stated that “ while the media can not tell the public what to think, they

can have a great impact on what the public thinks about ” (Prat & Strömberg, 2013,

p.3), this is a well­known phenomenon, called priming , that often is discussed in

studies of how the media influence the public. Whether this mean that media can

shape the public debate of how people think about certain issues or not is however

still an ongoing debate. Some literature, such as Mutz and Soss (1997) suggest that

this is not the case. Through an experiment designed study they tried to move

community opinion regarding low­income housing. The study measured the effects

after a year of purposefully carrying out a (positive) news agenda (for the low­income

housing) in a local paper. The result of the experiment showed that individual’s

awareness of the issue increased (priming effect), and the perceived view of other

people's opinion corresponded well with the tone of the articles (a phenomenon

called third­person perception). The reader’s own view on the subject on the other

hand had not changed as an effect of the experiment, but remained the same as in

the control group.

Other literature, for example Maier and Rittberger (2008), mean that agenda

setting by media may not work for local levels of politics, as in Mutz and Soss

experiment where citizens have first­hand experience and knowledge of the topic

themselves. For new issues on the other hand, that are perceived as far­away or

complicated for the everyday person the press has a strong effect on public attitudes.

This due to the news media often being the first and only institution to provide

information or attention to an issue. By then setting a first tone, (e.g. by describing

the glass as half full or half empty) the reporter can ‘frame’ a reader's future thoughts

on that issue. In the same manner mass media can, by choosing what not to publish,

frame a whole subject.

What most experts do agree on however is that knowledge, negative or

positive, boosts a feeling of importance and inclusiveness ­ without knowledge you

do not know that an issue exist, and therefore cannot have an opinion about it. From

this the hypothesis of this research paper has been drawn ­ that people consuming a

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greater amount of news feel more included in the EU and can therefore understand

the importance and impact of the institution on our everyday life. These people,

because of this, tend to have a more positive attitude.

2. Data

This essay seek to examine the relationship between the level of consumption of

news media on a weekly basis and its effect upon the attitude towards EU within

Sweden. The data used in the analysis originates from the SOM (Society, Opinion

and Media) institute, at the Gothenburg University, and their 2015 national survey.

The SOM institute’s 2015 national survey consist of five parallel questionnaires

(however, all of which containing the questions of focus for this analysis). The five

questionnaires are each randomly assigned to 3 400 individuals (thus 17 000

surveys are sent out in total) living in Sweden in the ages of 16 to 85. The 2015

survey received a response rate of 51,3 % (Vernersdotter, F. 2015).

To start the analysis, I would like to point out that the data used originates from a

voluntary survey, and that the 51,3% of people choosing to participate differ from the

people choosing not to do so (even if a participation rate of 51,3% is fairly good for

survey data). Both groups are however a part of the population, meaning that the

analysis always will have some selection bias. Further is the data collected from a

survey, which, compared to an experimental research (where the randomly chosen

subjects are randomly assigned to do/not do something ­ e.g. to read or not to read

the news), will always be affected by self­selection. This because survey­data are

merely observed (and not randomly assigned) and self­reported. This means that the

respondents themselves have made all the active choices they are reporting in the

survey ­ such as how often, what and where to read the news ­ and these decisions

are all made according to their current situations and believes. In turn this means

that even though the respondents are randomly chosen for the survey, their recorded

actions are not (as it would in an experimental research). A large part of the

randomness is therefore missing and when ignored can cause problems of

endogeneity (Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. 2014).

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The two biggest threats of endogeneity in regression analysis, especially from

survey­data, is in omitted variable bias (OVB) and simultaneous equation bias

(SEB). OVB is present when crucial variables are left out in a model. This causes

the output, both dependent variable and other independent variables that are

correlated with the omitted variable, to give faulty results. As we do not know when

we have omitted a variable, and its effects on the output, one cannot know when it

occurs, how big it is, or in what direction the output is faulty (too positive, too

negative or a non­significant problem). It is therefore important to understand the

topic of analysis well, and benefit from previous studies and experts in the field, in

order to minimize the risk of OVB. Simultaneity is when an independent variable is

jointly determined by the dependent variable, which causes a bias (SEB). This mean

that the explanatory variable, included to explain y in the simultaneous time is also

explained by y (Antonakis, 2010).

Another problem with survey data are that, as said before, it is self­reported.

Respondents can therefore misinterpret questions, have a flaw in their memory,

embellish the truth or simply just lie ­ without anyone knowing. The SOM­survey is

rather long (around 13 pages with questions, 20 pages in total) with topics requiring

the respondent to really think and reflect throughout the survey. This will likely

increase the bias in the end of the questionnaire as people get bored or “primed”

(influenced) by the topics brought up earlier. By using all five variations of the

questionnaires the problem of priming is expected to level out for this analysis, as

the topics appear in different orders for them all. The main explanatory variables

(consumption of news) is in the beginning of all the surveys, so the problem of not

being concentrated is expected to not be as big as if they would appear in the end.

However, as there is a big amount of questions regarding what kind of news the

respondents take part of, and as it is seen as “correct behavior” by the society to

take part of at least some news, one can imagine that people have embellished the

truth regarding the amount of news they consume to come off as “better people”. It

therefore seems likely that the data has some Social desirability bias.

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2.1 Swedish mass media

Swedish ‘mass media’ is an undefined and therefore a very wide definition, that and

can vary in meaning. The encyclopedia ‘National encyklopedin’ define mass media

as “media and media­organizations that provide information or entertainment to a

large audience” . As this essay focuses on news, or information, only the news 2

media will be included in the research. As the data in this study originates from the

2015 national SOM­survey ­ an institution with years of knowledge and experience

from the field of Swedish media, the inclusion of agencies comes from their survey.

To get a more precise result, some of the media included in the questionnaire, that

do provide news, was however excluded as the sources (and therefore the reliability

of the news) could not be ensured. The excluded news sources from the

questionnaire in the analysis are:

­ “Social Media”

­ “Foreign News Agencies”

­ “Other News Agencies”

The Swedish mass media is compared to many other democracies, such as the US,

relatively politically neutral in the reporting of news (Johansson, 2011). Nevertheless,

the news sources reporting is still organizations trying to sell their articles. This is

done by adapting and framing the news to fit the Swedish political climate, but also

as Aalberg et. al. (2012) also points out ­ doing this in a simplified manner where

there often is one winner and one loser. This can create skepticism towards

politicians and institutions, which in turn might mean a more negative attitude

towards EU.

A new political debate has also arisen in the media since the last election and

the growth of the extreme party Sverigedemokraterna (SD). Many news agencies do

not want to support/promote the ideology, and thus take a stand against it by

publishing debate articles against the party and its politicians, or by simply ignoring

2See: http://www.ne.se/uppslagsverk/encyklopedi/lång/massmedier

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Sverigedemokraterna (Johansson, 2011). By doing this the Swedish mass media

might have lost credibility for the consumers sympathizing with SD, and whom are

strongly against the EU. Instead, this audience might have turned to more extreme

papers that do hold political views (and are not included in the Swedish mass

media). Examples of such papers are “avpixlat” and “friatider”, which are not

included in the survey.

2.2 Descriptive statistics of Attitudes towards EU To get an overview of the distribution of attitudes towards EU, we will first examine

the answers received from the question making the dependent variable. The result

show that a majority of people within the survey is neutral or rather positive (around

60%). Only around 4 % did not have an opinion, which indicates that by the

individuals choosing to participate in the survey thought it was an important question

to have an opinion about.

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Table 1 ­ Generally speaking ­ what is your attitude towards the EU?

Attitude Coding Total Percentage Frequency

Percentage frequency ­ 3 groups

Very Positive 1 6,32 % 41,75%

Rather positive 2 32,83%

Neither Positive or Negative

3 27,72 % 28,16%

Rather Negative 4 19,76% 30,09%

Very Negative 5 9,34%

No opinion 6 4,03% not included

144 observations missing (deleted)

3. Methodology

The response variable (attitudes toward EU) in this thesis is in an ordered scale,

namely a five level Likert scale, ranging from Very Positive to Very Nega tive. A sixth

option of “no opinion” was also available as a response and will be discussed further

below. Attitudes does not have a natural or simple way of measurement as they

measure a subjective opinion of the respondent, with only a limited number of

answers to choose from. Because of this, linear regression will not be a good choice

of model, as the outcome of attitudes isn’t quantitative (and the predicted values, at a

maximum, cannot be beyond 5). A logistic regression, on the other hand, takes the

fact that the dependent variable is not continuous into account. By Maximum

Likelihood the discrete explanatory variable, which is restricted to a short scale, can

be properly estimated by yielding an output of conditional probabilities for different

odds ratios of the explanatory variables. There are different types of logistic

regressions depending on how many categories of the dependent variables there

are, both types are to be explained below. The statistical software SAS was used

throughout the research, but as the output from a logistic regression is rather hard to

interpret and to get a better understanding of how and why the output look as it does,

the logistic regression is explained below.

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The significance level in this thesis will be 0.05 throughout the analysis.

3.1 The model

3.1.1 Probabilities The logistic regression, as mentioned earlier, calculates the unknown probability ( p)

of an event or feeling happening. Depending on the dependent variable’s number of

categories and weather they are ordinal (ordered categories) or not, different

versions of the logistic regression can be used. For data in this analysis the

probability correspond to an individual having a specific attitude towards the EU. As

the data to be analyzed is ordinal, the logical choice of model would be to use an

ordered logistic regression, which can be used when comparing more than two

(ordered) categories of the dependent variable between each other. However, as the

proportional odds assumption (explained below) was violated when the test was

performed, a binary logistic regression instead had to be used.

To perform a (binary) logistic regression the five categories had to be reduced into

two levels instead. The two groups represent “success”, or for example “a positive

attitude” (given value 1), compared to “failure”, (= 0) or for our analysis “not a positive

attitude”. To estimate the parameters in the logistic model two approaches can beβ

applied ­ least squares estimation or maximum likelihood estimation (MLE). SAS

uses MLE, however, as this is a numerically tedious process that require

sophisticated software (for example SAS), this essay will not go into more details of

this estimation process (Mendenhall et. al., 2014).

The antilog of the logit function, which serves as a link function for the Bernoulli

distribution and the independent variables, allows SAS to solve for . So as the p︿ β

parameters have been estimated (with MLE) the following process gives the

probability of success (Foltz, 2015).

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n(odds) logit(p) β x l = = 0 + β1 1 ntilog A ⇒ Odds ) e( = p(1 p)− = β +β x0 1 1

p e (1 ) ⇒ = β +β x0 1 1

− p ⇒ (e ) ep + β +β x0 1 1 × p = β +β x0 1 1

⇒ (1 ) ep + eβ +β x0 1 1 = β +β x0 1 1

⇒ p︿ = eβ +β x0 1 1

(1 + e )β +β x0 1 1 (1)

To simplify and adapt to this research, SAS estimated the logistic regression as

follow:

(y)E = exp(β +β x +β x +...+β x )0 1 1 2 2 k k1 + exp(β +β x +β x +...+β x )0 1 1 2 2 k k

or y = 1 if positive attitude (success) 0 if negative attitude (failure)

(y) P E = of success( )

(2), ...x independent variables x1 x2 k =

However, as this analysis specifically focuses on the relationship between news

consumption and attitudes towards EU, and not the individual prediction of different

observations the focus will rather lie on the odds ratios for the variables of interest,

rather than the probability of the model as a whole.

3.1.2 Odds

Odds then can be modelled:

(3)dds O = p(1 p)− = probability of event occurring

probability of event not occurring

The odds ratio, which is provided for all individual variables, and what is of our

interest when examining the results for news consumption (and whether it has an

impact on attitudes towards EU), is then simply the ratio of two odds. The odds ratio

can be modelled:

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(4)dds ratio o = oddsevent 1odds event 2

Say the result of the odds ratio became equal to 2. The odds ratio can then be read

as: the odds of success from event 1 are 2 times greater than success in event two.

If the odds ratio is less than one, the odds of success in event one is lower than in

event two. When the odds ratio is not significantly different from 1, there is no

difference in the odds between event one and two. Where the “events “or variable is

numerical, such as age, the odds ratio instead gives a increase/decrease in odds as

the variable increase by one unit. Odds and probabilities are therefore very different.

The odds of a variable can be very high even if the overall probabilities are low. For

example: the odds ratio for having a negative attitude towards EU is the same for

someone turning 79, as for someone turning 22, even though the overall probability

for the person turning 79 might be higher.

3.2 Independent variables

Berg and Bové wrote for SIEPS (Svenska institutet för Europeiska Studier) in 2016

that the typical person with a positive attitude towards EU is a young, higher

educated woman with a high income. Preferably born or with parents born in another

European country (Berg & Bové, 2016, pg. 7). In order to capture the pure effect of

News consumption on the attitude towards EU, these variables, among others, were

therefore chosen as the control variables (see tables 3 ­ 6). By dividing the control

variables into different groups and inserted them into the model group wise, the

reaction of the variable of interest can be better analyzed.

In a first basic model, only the variable of interest will be included. The

second, individual level (group: personal attributes, table 3), sex and age is

controlled for. SIEPS described the typical person with a positive attitude towards EU

as a younger woman (Berg, 2016, pg.7). This seem logical, not only as the younger

generation has more international connections today due to internet and a better

level of English, but also as the younger people have lived a bigger proportion of

their life in the EU than outside of EU. These two variables are therefore very

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important to check for. At the third level (table 4) variables connected with work and

career are gathered. It includes education and household income. The income for

the whole household was chosen over individual income as this gives a better overall

perspective of an individual's economy and life. As SIEPS explained, well­educated

and high income earners tend to be more positive towards EU. One reason of this

can be that people in higher positions are more likely to work internationally and

therefore has a first hand experience of the benefits of the European Union. Well

educated people has further often read more and by that too understand the EU

better. When having a high household income, even if the person themselves do not

earn it, they are living with someone who do, and thus has a greater chance of being

influenced of their view.

The fourth group (table 5) includes variables checking for a connection from

another European country, which of course makes people feel closer to the rest of

Europe which also tend to make them more positive towards the EU. The last group

(table 6) includes prior (political) beliefs by including perforation for a Swedish party.

SIEP did not mention anything about this in their description, however, as discussed

earlier, experts and previous studies of media's influence on the public often claim

that people chose news that fit their (already existing) conception of the world. Party

affiliation was therefore included as a control variable. The group also includes a

variable measuring whether a person enjoys politics. This variable is needed as

political people better know their stand in a question, and are harder to convince

otherwise. However, the variable might be in danger to the possibility of having an

inverse relationship with the consumption of news ­ a SEB. Because, if a person

enjoy politics s/he would be more probable to consume news (since s/he enjoys it),

especially news concerning the EU. The relationship can on the other hand go the

other way around as well. A person that read a lot of news can get interested in

politics by the news. A person very uninterested in politics is however less likely to

even start consuming political news, and therefore do not even get the chance of

becoming interested. Further, it is not the news reporting, as such, people with

political interest are interested in, but the underlying, third variable ­ politics.

Therefore, it seems logical that the variable will not cause problems. Further would

an even greater risk of an OVB be present if to not include this explanatory variable.

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Table 2 ­ The Dependent Variable

Variable SAS code

Type

Attitude towards EU*

f72 Ordinal 1 = very positive

2= rather positive

3 = either positive or negative

4= rather negative

5 = very negative

(6 = no opinion**)

1 = Negative

2 = Neutral

3 = Positive

*“Generally speaking ­ what is your attitude towards the EU?” ** Later removed

Explanatory (independent) variables:

Control variables:

Table 3 ­ Personal Attributes

Variable SAS code Type

Age Alder Numerical The survey includes people 16 ­ 85 years of age

Sex Sex Binary 1 = Woman, 2 = Man

Table 4 ­ Professional life

Variable SAS code Type

Education Utb Binary dummy 0 = Not graduated with higher education

1 = Graduated with higher education*

income for

household

Hushink categorical 1 = < 300 000 kr / yearly 2 = 301 000 – 700 000 kr / yearly 3 = > 700 000 kr / yearly

* Higher Education = University or similar (e.g. högskola)

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Table 5 ­ European background

Variable SAS code Type

Childhood

country (you)

utlandsfodd Binary dummy

0 = You have not grown up in another European

country, nor your parents

1 = You have grown up in another European country,

but not your parents

Childhood

country (parents)

utlandsfoddf Binary dummy

0 = No of your parents have grown up in another

European country, nor you

1 = At least one of your parents have grown up in other

European country, but not you

Childhood

country (both)

eufodd Binary dummy

0 = Neither you nor your parents have grown up in

another European country

1 = Both you and your parents has grown up in another

European country

Table 6 ­ Political belief

Variable SAS code Type

Prior beliefs V, S, MP, KD,

M, F, C, SD,

ANNAT

Binary dummies

0 = Not the political party of choice

1 = Political party of choice (only one party can be

chosen)

Interest in

politics

interestinp Binary dummies

0 = interest in politics 1 = no interest in politics

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Variable of interest

Table 7 ­ News Consumption

Variable SAS code Type

Quantity &

frequency

News

consumption

oftamanga Binary dummy 1 = If an individual consumes news from at least 3

sources at least 5 days/week (each)

0 = If a person does not

All original questions used as variables are presented in full in the Appendix.

The logistic regression do not have any strict assumptions. Multicollinearity and

outliers were however checked for. Since most variables used are either dummies or

categorical there was not any problem of outliers. The variable “Sex” did however

have a third option for people not identifying themselves as a man or a woman. 16

people, or 0.2% of the sample, choose this option. Because of the small frequency

these observations were deleted.

There was no problem of multicollinearity.

3.3 Levels of media consumption

As the definition of mass media has already been defined, the different levels of

consumptions will now be discussed in more detail. To read “a lot” of news can have

different meanings. This essay divide the definition into two levels ­ a vertical and a

horizontal. The vertical level refers to that an individual read news often. In the

survey the frequency is divided into intervals of two days, starting from the option of

reading the news “daily”,” 5­6 days/week”, ”3­4 days/week” until the option of “more

seldom”. This research define the options of “daily” and “5­6 days/week” as someone

who reads news “often”. The opposite level is seen as “not often”. The variable for

this is referred to as “frequent”. The horizontal level of news consumption refers to

that an individual read the news from several sources. The limit in this analysis for

“several” is at least 3 different sources (each at least 3­4 days a week). The variable

17

for this is referred to as “quantity”. In order to capture both these levels, and get a

better representation of the people both consuming news often and from several

sources one combined variable was created. It measures if an individual consumes

news from at least three different sources, each at least 5 days/week. This variable

alone is a stronger indicator of a big news consumption for a single observation and

will therefore be the primary variable of interest in the model to answer the research

question.

3.4 The “no opinion” category There is an ongoing debate within the field of survey­data and statistics, regarding

what to do with the responses of “no opinion” (and/or “I do not know”) option within

closed answer questions (Converse, 1977; Krosnick et. al, 2002). As the category

stands outside any given ordered scale, it is clear that it cannot simply be included

as is in the analysis. Some then argues that the category in some cases in fact can

be included in the “neutral” opinion, which in our data are the option of “neither

negative nor positive” attitude towards EU. The problem is however that since the

“neutral” option already exist, that is neither negative nor positive, and the

respondent still have chosen not to use that answer, it becomes unclear how to

interpret “no opinion”. By speculation, it can mean that the respondent doesn’t know

enough about the EU to be confident enough to answer, or that they use this option

instead of the “neutral” answer in the ordered scale. A third reason could also be that

the respondent in fact do have an opinion but chooses not to declare this to SOM by

some unknown reason. Because of the difficulty to interpret the meaning of “no

opinion” as we still do not know the attitude towards EU of the people choosing this

answer, it would create a bias if to combine it with the “neutral option”. In turn, just

deleting the observations would create a sampling error. Therefore, an analysis (on

its own) between “no opinion” and a merge of the entire ordered scale, (called

opinion ­ as we know these respondent’s opinions) will be conducted. I will also take

a closer look at the group of people in the “no opinion” group, to try to get a better

understanding of the reasons behind their answer.

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4. Results

4.1 “No opinion” vs. “opinion” A first test was performed to investigate whether consuming a lot of news simply

gives you an opinion ­ no matter good, bad or something in­between. A binary logit

between “no opinion” (= 0) and (all other) “opinions” (=1) was thus performed. The

background variables for party affiliation was however not included in this first model

because of sensitivity of number of variables with small group sizes when performing

logistic regression. The rule of thumb states that at least 10, preferably 20,

observations per variable and group, less than that can overfit the model ( Peduzzi et

al., 1996) . As the group of people without an opinion of the EU is relatively small

(merely 199 observations) to include the 9 binary variables would go above the

maximum limit.

Tests of the whole models against the constant only model all proved statistically

significant, as can be seen in table 8. All models further had a significant variable of

interest ­ “big consumption of news”, with high odds ratios.

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Table 8 ­ “ No opinion ” vs. “Opinion”

Model Tests

Basic* + Personal

attributes**

+Professional life***

+European background**

**

+ interest in politics*****

Variable statistics

Odds ratio

2.669

2.669

2.405

2.427

1.851

P­value <.0001 <.0001 <.0001 <.0001 <.0001

95% CI 2.124 ­ 3.354 2.068 ­ 3.444 1.796 ­ 3.157 1.810­3.253 1.375 ­ 2.430

Overall model statistics

Testing global null

: Beta=0H 0

(rejection of)H 0

(rejection of

)H 0

(rejection of)H 0

(rejection of)H 0

(rejection of)H 0

df 1 3 6 9 10

Nagelkerke R2 0.0317 0.0463 0.0989 0.100 0.1819

No. of obs (opinion / no op.)

7776 / 325 7775 / 325 7187 / 255 7187 / 255 7187 / 255

* basic model = Big Frequency & Quantity of news consumption ** Personal attributes = sex and age *** Professional life =education and income of the household **** European background = Childhood country (within EU) for you, your parents or both*****variable interestinp.

The dependent variables has reference group 0 in the model (people with no

opinion) meaning that the odds of having an opinion increases as an individual

consume news both more frequently and/or from more sources. This is an interesting

finding, which can make the next analysis questionable as it can be the case that

consuming a lot of news not gives you a particular attitude but rather an attitude (at

all).

From the analysis, it looks as if people not consuming a lot of news has a higher

tendency of answering “no opinion”. How many and what makes someone choose

this option instead of the neutral category, is however still unclear. In the overall

analysis (with all five surveys) the “no opinion” category is rather small (only around

4%, see table 1) which lead to the conclusion that the selection bias created by

removing these observations probably will be smaller than combining it with the

neutral category for the next analysis. To establish that people who consume a lot of

news has a bigger tendency to have an opinion at all, is however insightful, and a

20

step closer to answer the question of how (if at all) the Media influence attitudes

towards EU.

4.2 Analysis of attitudes towards the EU As the response data is ordinal an ordered logit regression would be the most

intuitive model to use as it takes the differences and similarities between all five

categories into consideration. A binary logit model only distinguishes between two

groups. The ordered logit model was therefore first attempted, but as the

proportional odds assumption failed for the model, the inference could not be

statistically assured.

The proportional odds assumption states that the odds ratios should be the same

between all the dependent variable’s ordered categories. With other words, the slope

estimate between each pair of outcomes across two response levels should be the

same, no matter which pair studied. The assumption of proportional odds is very

strong for the ordered logit model, which also has given the method its second name

of ‘proportional odds model’ (Williams, 2016). On the other hand, by combining

observations into fewer categories less information can be extracted, as the

categories cover a bigger range of observations which therefore become less

specific. As more information can be extracted from the proportional odds model,

another attempt was tried but with fewer levels ­ positive, neutral and negative. Also

this test failed because of the same reason as before.

Three separate binary logistic models were therefore created so that each pair of

outcomes Positive vs. Not positive (a group of negative and neutral), Negative vs.

Not Negative, Neutral vs. Not Neutral instead could be compared individually. By

doing this the odds can vary between the groups, and the proportional odds

assumption is no longer required.

21

4.2.1 Positive Attitude vs. Not Positive Attitude

To evaluate the correlation of consuming a lot of news and having a positive attitude

towards EU, a binary logit model between the positive observations and a combined

group of negative and “neither positive or negative” observations was performed. All

models proved significant, likewise did the variable of interest perform a good overall

results.. The “ Not positive attitude” is the reference group of the analysis, meaning

that the odds of having a positive attitude increase when consuming more news.

Table 9 ­ Positive attitude vs. Not Positive Attitude

Model Tests

Basic* + Personal

attributes**

+Professional life***

+European background****

+ Prior beleifs*****

Variable Statistics

Odds Ratio

1.114 1.304 1.264 1.280 1.194

P­value 0.0371 <.0001 <.0001 <.0001 0.0052

95 % CI 1.006 ­ 1.234

1.163 ­ 1.462

1.125 ­ 1.420 1.139 ­ 1.439 1.054 ­ 1.351

Overall model statistics

Testing global null

:H 0 Beta=0

(rejection of)H 0

(rejection of)H 0

(rejection of )H 0

(rejection of )H 0

(rejection of)H 0

df 1 3 6 9 18

Nagelkerke R2

0.0009 0.0094 0.0596 0.0633 0.1717

No. of obs (pos. / not pos.)

3170 / 4606

3170 / 4605 2945 / 4242 2945 / 4242 2945 / 4242

* Basic model = Frequency & Quantity of news consumption, ** Personal attributes = sex and age *** Professional life =education and income of the household **** European background = Childhood country (within EU) for you, your parents or both. *****Prior beliefs = Political party affiliation & interest in politics.

4.2.2 Not Negative vs. Negative Attitude

The second test performed was between the observations with a negative attitude

towards EU versus “not a negative attitude” (= positive and neutral attitude). The

negative group is the reference group of the model. The basic* model that only

22

include consumption of news, did not prove significant but notably improved as more

background variables was added.

The overall result confirms the previous model and show that consumption of news

has a positive correlation with the attitude towards EU. With other words ­ people

consuming more news have higher odds of not having a negative attitude. When

including all control variables, the 95 % confidence interval of the odds ratio is

between 1.02 to 1.328, meaning that people consuming a lot of news have 1.02 to

1.328 times higher odds of not to have a negative attitude.

Table 10 ­ Not Negative attitude vs. Negative Attitude

Model Tests

Basic* + Personal

attributes**

+Professional life***

+European background****

+ Prior beleifs*****

Variable Statistics

Odds Ratio

1.035 1.190 1.161 1.176 1.164

P­value 0.5396 0.0051 0.0179 0.0104 0.0241

95 % CI 0.928 ­ 1.154

1.054 ­ 1.344 1.026 ­ 1.313 1.039 ­ 1.334 1.020 ­ 1.328

Overall model statistics

Testing global null

:H 0 Beta=0

(rejection of )H 0

(rejection of)H 0

(rejection of )H 0

(rejection of )H 0

(rejection of)H 0

df 1 3 6 9 18

Nagelkerke R2

0.0001 0.0074 0.0271 0.0298 0.1511

No. of obs (Not neg. / neg.)

5418 / 2358

5418 / 2357 5002 / 2185 5002 / 2185 5002 / 2185

* Basic model = Big Frequency & Quantity of news consumption ** Personal attributes = sex and age *** Professional life =education and income of the household **** European background = Childhood country (within EU) for you, your parents or both. *****Prior beliefs = Political party affiliation & interest in politics.

4.2.3 Neutral attitude vs. Not Neutral Attitude (Positive + Negative)

In the last model the odds of consuming more news while having a positive or

negative attitude is compared to the odds of consuming a lot of news while having a

23

category 3 opinion (in the scale of 5, meaning that the individual is neither positive or

negative). The test was carried out to see if people consuming more news get a

broader perspective and therefore tend to be in the middle of the scale. This can

seem similar to the first analysis between “opinion” and “no opinion”. However, the

difference being that the underlying reasons of the category 3 answers are within the

ordered scale, and is therefore an attitude, which “no opinion” cannot be proven to

be. An interpretation and comparison with only the other two levels are therefore now

possible. The results show that the basic* model (only including the media

consumption variable) is not statistically significant. As background variables are

added the overall model improves and becomes more powerful, even if there are

almost no advancement in the explanation rate. The single variable of media

consumption is only significant in the second model. This show that the model, and

more importantly ­ news consumption is not a good variable to distinguish between

these groups. There is thus no difference in news consumption between someone

that neither are pro or against EU, and the two groups with more defined positions.

24

Table 11 ­ Not Neutral attitude vs. Neutral Attitude

Model Tests

Basic* + Personal

attributes**

+Professional life***

+European background****

+ Prior beleifs*****

Variable Statistics

Odds Ratio 1.098 1.146 1.125 1.128 1.048

P­value 0.0967 0.0312 0.1205 0.0606 0.4751

95 % CI 0.983 ­ 1.227

1.012 ­ 1.297 0.992 ­ 1.274 0.995 ­ 1.278 0.992 ­ 1.191

Overall model statistics

Testing global null

:H 0 Beta=0

(rejection of )H 0

(rejection of)H 0

(rejection of )H 0

(rejection of )H 0

(rejection of)H 0

df 1 3 6 9 18

Nagelkerke R2

0.0006 0.0066 0.0198 0.0207 0.0519

No. of obs (Not neu. / neu.)

5528 / 2248

5527 / 2248 5130 / 2057 5130 / 2057 5130 / 2057

* basic model = Frequency & Quantity of news consumption ** Personal attributes = sex and age *** Professional life =education and income of the household **** European background = Childhood country (within EU) for you, your parents or both. *****Prior beliefs = Political party affiliation & interest in politics.

4.3 Overall analysis

Comparing all the results side by side the test of “opinion vs. no opinion”, a little

unexpectedly, yields the highest numbers. Consumption of news has the highest

odds ratio between these two groups and the overall model has the highest

explanation rate. This, even as fewer variables are included than in the rest of the

models. Both the test of “positive vs. not positive attitude” and “negative vs. not

negative attitude” clearly points in the same direction, and show that people

consuming more news have higher odds of having a positive attitude towards the

EU. It is however a slight difference in the odds ratios (1.19 and 1.16), where the

odds ratio is lower when positive and neutral attitude is compared to the negative.

This indicates that the neutral group have decreased the odds when added to the

positive. The logistic regression between “neutral attitude and not neutral attitude”

both provide the lowest explanation rate but more importantly has insignificant odds

25

ratios for consumption of news. The level of news consumption is therefore not

significantly different between these two constructed groups. The overall analysis

that can be drawn when comparing these tests are therefore that people consuming

more news have higher odds of simply having an opinion/attitude in the question of

EU. Why people don’t have an opinion is on the other hand unclear. Within the

attitude scale which the odds are in favor for when consuming a lot of news, the

odds are in a second stage also higher for a positive attitude (when consuming a lot

of news).

Table 12 ­ Overall Analysis

Analysis Variable Statistics Overall model ­ Nagelkerke R2

Opinion vs no opinion

Odds ratio

P­value

1.851

<.0001

0.1819

Positive vs. Not Positive

Odds ratio 1.194 0.1717

P­value 0.0052

Negative vs. Not Negative

Odds ratio 1.164 0.1511

P­value 0.0241

Neutral vs. Not Neutral

Odds ratio 1.048 0.0519

P­value 0.4751

* Results are from full models ** underlined group = reference group

To summarize ­ The odds are 1.85 times higher of having an opinion when

consuming a lot of news. If an individual does have an opinion and consume a lot of

news, the odds are 1.19 times higher of that opinion to be positive towards EU (than

a neutral or negative attitude). When combining the neutral attitude observations with

the group of positive attitudes the odds ratio of news consumption decreases by

0.03, indicating that there are lower odds of being neutral than positive when the

news consumption is high.

26

5. Discussion and Conclusion

The fact that there is a differences between individual’s attitude towards EU when

consuming a lot of news can thus be verified by the logistic regression. The odds are

higher of having an opinion when news consumption is high. When having an

opinion, it is higher odds of the attitude to be positive. This confirms the hypothesis

that the paper stated in the beginning of this paper, however, correlation does not

mean causation. Do individuals develop the positive attitude because they consume

a lot of news, or do they consume a lot of news because they have an interest and

by that have an attitude/opinion?

To capture the true causation, or as for this study, the influence of mass media on the

opinion of the EU, an experimental study would be necessary. By randomizing the

consumption of news between individuals, and comparing the attitudes of EU with a

control group after the experimental period is over, the data would be of better quality

and with less bias. Both OVB, SEB, Social desirability bias and selection bias would

be controlled for. The only variable differentiating between the groups would further

be the news consumption, and therefore the only variable possible to influence the

attitude. This experiment would in practice, be extremely difficult to realize. Firstly,

the experiment would have to last for a rather long period of time to capture the

effects, and it would therefore be hard to find (randomly chosen) participants. To

avoid the news is in addition rather hard in today’s society, where it can be accessed

from various sources, which always are surrounding us, whether we like it or not.

However, the intention of the essay was never to provide all answers. Rather to

conduct a first analysis of the influence of the Swedish mass media by consumption

of news in the 21st century, for future research to build on. So to answer the

research question of “Does people with a high consumption of news differ in their

attitude towards the EU?” The thesis concludes that there is a correlation between a

positive attitude and a big consumption of news, which confirms the hypothesis in

the beginning of the paper. The causation of why the people reading more news

have a positive attitude is however still unanswered.

27

References

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NON­ATTITUDE REDUCTION OR AN INVITATION TO SATISFICE?. Public Opinion Quarterly . 66 (Issue 3), pp. 371 ­ 403. Mendenhall, W. & Sincich, T.. (2014). . In: A second Course in statistics Regression Ananlysis . Essex, England: Pearson New International Edition. pg. 455. Maier, J. & Rittberger, B.. (2008). Shifting Europe’s Boundaries: Mass Media, Public Opinion and the Enlargement of the EU. European Union Politics . 9 (issue 2), 243–267, DOI: 10.1177/1465116508089087 Mutz, D., & Soss, J.. (1997). Reading Public Opinion: The Influence of News Coverage on Perceptions of PublicSentiment. The Public Opinion Quarterly. 61 (No. 3), pp. 431­451. Nationalencyklopedin, massmedier . http://www.ne.se/uppslagsverk/encyklopedi/lång/massmedier (Last accessed: 2017­05­09) Nico Drok & Liesbeth Hermans (2016) Is there a future for slow journalism?, Journalism Practice, 10:4, 539­554, DOI: 10.1080/17512786.2015.1102604 Peduzzi, P., Concato, J., Kemper, E., Holford, T.R. & Feinstein, A.R.. (1996). A Simulation Study of the Number of Events per Variable in Logistic Regression Analysis. J Clin Epidemiol . 49 (no. 12) , pp. 1373–1379 Prat, A. & Strömberg, D.. (2011). The Political Economy of Mass Media. CEPR Discussion Paper . No. DP8246, Available at SSRN: https://ssrn.com/abstract=1763655. Statistiska Centralbyrån. (2014). VANLIGARE ATT RÖSTA I RIKSDAGSVALET PÅ SENARE ÅR. Available: http://www.scb.se/hitta­statistik/sverige­i­siffror/val­och­partier/valdeltagande/. Last accessed 2017­04­27. Vernersdotter, F.. (2015). KODBOK ­ Den nationella SOM­undersökningen. SOM­Institutet 2015 . Göteborgs Universitet, pp. 1. Williams, R.. (2016). Understanding and interpreting generalized ordered logit models. The Journal of Mathematical Sociology, . 40 (1), pg. 7 ­ 20. DOI: 10.1080/0022250X.2015.1112384

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Appendix Part 1 ­ The Questions Vernersdotter, F.. (2015). KODBOK ­ Den nationella SOM­undersökningen. SOM­Institutet 2015 . Göteborgs Universitet, pp. 13 ­ 58.

Media Consumption

30

Personal Attributes

Professional life

31

European Background

Prior beleifs

32

Part 2 ­ SAS Statistical Output 2.1 Age variable

33

2.2 Spearman correlation Coefficients

ofta sex manga hushink oftamanga

alder utb utlandsfodd utlandsfoddf

ofta 1.00000 0.03313 0.54349 0.04284 0.49627 0.33492 0.01715 ­0.07132 ­0.06895

sex 0.03313 1.00000 0.03388 0.03746 0.03365 0.02682 ­0.11080 ­0.01480 ­0.02101

manga 0.54349 0.03388 1.00000 0.04312 0.68158 0.34930 0.01665 ­0.05735 ­0.06357

hushink 0.04284 0.03746 0.04312 1.00000 ­0.02770 ­0.25382 0.27213 ­0.05478 ­0.03058

oftamanga 0.49627 0.03365 0.68158 ­0.02770 1.00000 0.44193 ­0.02034 ­0.05176 ­0.05459

alder 0.33492 0.02682 0.34930 ­0.25382 0.44193 1.00000 ­0.10625 ­0.00228 ­0.04080

utb 0.01715 ­0.11080 0.01665 0.27213 ­0.02034 ­0.10625 1.00000 0.03232 0.04901

utlandsfodd ­0.07132 ­0.01480 ­0.05735 ­0.05478 ­0.05176 ­0.00228 0.03232 1.00000 0.74132

utlandsfoddf ­0.06895 ­0.02101 ­0.06357 ­0.03058 ­0.05459 ­0.04080 0.04901 0.74132 1.00000

34