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Modeling the Relationship between Trust in Science 
and Reliance on Formal News Sources 

 
William Randall 

University of North Texas 
 

Vikas Sinha 
University of North Texas 

 
Victor Prybutok 

University of North Texas 
 

Communication of scientific information to the population presents challenges because of the need 
to balance being transparent with avoidance of complex scientific terminology. The spread of 
information and news through social media and other informal sources rose during this past decade. 
The focus of this study is to understand what relates to reliance on formal news media. Formal news 
sources influence businesses and people, making them more apt to believe in science as measured by 
their beliefs in scientific concepts such as vaccinations, climate change, and compliance with COVID 
protocols. While this study looked at science because of the timeliness of COVID-related 
communication, this is a major issue related to all areas of business. For example, consider that in the 
case of COVID vaccines which has a consumer product manufactured by pharmaceutical firms, 
manufacture of the physical products related to vaccines, the distribution, and administration of 
vaccines. Trust in the predictive modeling that suggests vaccinations and PPE protocols impact 
pandemic and health concerns help influence actions. This empirical research shows that belief in 
science is correlated with the formality of the news source. Having a population that uses scientific 
information leads to behaviors like controlling the spread of something like COVID which is good 
for business, and not doing so results in the potential to have shutdowns or restrictions that are bad 
for business. If one fully understands the process, the public trusts in scientific modeling information, 
the impacts of non-compliance, and the negative business impact of non-compliance can be 
minimized. 

 
Keywords: COVID; predictive modeling; information source 

http://journals.sfu.ca/abr 
2022, Volume 12, pages 26-36 

http://journals.sfu.ca/abr


27  

Introduction 
There is a conjecture that some individuals do not trust science and are hesitant to take proper 

actions to respond to what the science suggests (e.g., COVID-19 complications if people do not follow 
preventative measures or fail to take the vaccine) (Plohl & Musil, 2020). This proposed study uses 
research concepts and approaches from studies evaluating the trust (or lack of trust) in science and 
examines the correlation of this trust with the source of information. 

The subject of trust in science is a multilayered area to try to investigate. For example, one 
might trust in the science that leaping from a considerable height is incongruent with a long healthy 
life but trusting in climate change predictions to cause lifestyle changes are more complicated. While 
both the effect of gravity and climate science are predictive aspects of science, the former is considered 
obvious, while the latter would depend more on one’s trust in science. 

The rise of rapid information dissemination through social media channels has increased 
awareness amongst people. However, some people also tend to believe the misinformation or 
disinformation that is coming through those informal channels. This source of information – formal 
news versus informal news – influences whose science people believe in (Brewer & Ley, 2013). 

It is accepted that existing literature does not reach a consensus on defining trust (Rousseau 
et al., 1998). There is an agreement in the peer-reviewed literature on the positive impact trust has on 
collaborative behavior (Badcock & Gambetta, 1990), fostering the rapid formation of informal 
working groups, decreasing harmful conflicts, and reducing transaction costs (Meyerson et al., 1996). 
Mayer et al. (1995) described trust as a “willingness to be vulnerable,” as cited in Meyerson et al. (1996, 
p. 152). In contrast, other researchers describe it as a “willingness to rely on” or “confident, positive 
expectations” (Rousseau et al., 1998, p. 394). This study accepts the definition that encapsulates all the 
above information in this definition of trust. “Trust is one party’s willingness to be vulnerable to 
another party based on the belief that the latter party is (a) competent, (b) open, (c) concerned, and 
(d) reliable” (Meyerson et al., 1996, p. 152). 

This empirical research analyzes various beliefs people have that influence decision they make 
in their lives. The beliefs in science items included vaccinations, climate change, nuclear energy safety, 
the safety of genetically modified food, and obesity and health. The correlation of these beliefs to the 
news source was then analyzed. The study justifies that belief in science is strongly correlated with the 
type of source from which a person gets their news. Here, that would be formal news sources. Formal 
news is defined as news gained from mass media such as major Television and print sources and not 
social media, friends, family, scientific experts, or search engines. The remainder of this paper presents 
a review of prior literature, research methodology, analysis of results, conclusion and future research, 
and reference list. 

 

Literature Review 
The science-society relationship has been turbulent over the years (National Academies of 

Sciences, Engineering, and Medicine, 2017). This turbulence is often valid when the scientific findings 
conflict with religious beliefs or long-held views. Added to that are the concerns about manipulating 
information on popular social media channels like Facebook, Twitter, and WhatsApp (Michael & 
Breaux, 2021). Digital technology has fundamentally altered the sources from which people seek their 
news and information. Readily accessible user-generated information content and numerous niche 
news channels now vie with formal news sources. The growing use of unconventional sources of 
information correlates with a propensity to question (Fletcher & Park, 2017) and increases skepticism 
of formal sources (Tsfati & Cappella, 2003) while not questioning informal news sources. Other the 
other hand, trust in information can also be linked to expectations (Vanacker & Belmas, 2009). An 
individual expecting gossip news is likely to trust a source that fulfills that expectation, while still 
believing that a reputed formal news source, like New York Times, better informs the audience. In 



28  

certain geopolitical regions and cultures, information from governments is deemed to carry more trust 
than the news from social media, interpersonal communication, or religious clerics (Melki et al., 2021). 

This changing consumption pattern has similarly resulted in people relying more increasingly 
on informal news sources such as search engines and social media for their information needs (Gil de 
Zuniga et al., 2017). While the use of television appears to be declining, print news is sharply declining, 
and social media as a source of news is on the rise (Nielsen et al., 2016). Often these informal news 
sources are not vetted and can carry deliberate misinformation or disinformation. People consume 
content from numerous sources with unverified credibility and when not contextualized the source’s 
intentions could negatively impact the public's trust in all news sources' accuracy, quality, and 
objectivity (Flanagin & Metzger, 2017). This presents an even more significant challenge to the 
business and scholarly community when trying to communicate accurate scientific information. 
People do not realize that they still need to vet the information they receive no matter the news source 
(Ognyanova, 2019). This unquestioning belief represents a severe dichotomous relationship between 
information gained from formal and not questioning informal news sources. People are drawn to and 
do not question news sources that reinforce their current beliefs, while they avoid and do not trust 
news sources that are incongruent with their current beliefs (Michael & Breaux, 2021). 

The National Academies of Sciences, Engineering, and Medicine in a study (NASEM, 2017) 
on communicating science effectively suggested that people who understand science or have the 
willingness to understand scientific information often seek to understand the relative merits of the 
evidence presented or how well the facts and underlying information support the particular issue being 
described. Believers in the beliefs in science list are willing to have evidence-based scientific findings 
presented to them and, therefore, would be considered to have some degree of trust in science. 

People's views towards scientific information and science are impacted when emerging 
technologies and scientific information threaten deeply held values and challenge beliefs (Blank & 
Shaw, 2015; NASEM, 2017). The following paragraphs will consider a few topics of science-related 
public controversy or contentious societal issues. 

The topic of vaccines has risen to a broader public conversation lately, primarily because of 
the current COVID-19 pandemic. This controversy has created a divide among people based on sub- 
political affiliations, religious beliefs, or just a long-held belief that vaccines lead to adverse events, like 
autism, later in life (van der Linden et al., 2015). People must decide whether to follow medically 
reasonable vaccination recommendations (Downs et al., 2013). Refusal to vaccinate poses a severe 
public health risk. Those with a higher level of trust in scientific evidence are expected to accept the 
idea of the importance of vaccinations. This trust can be the topic of childhood vaccinations (Stratton 
et al., 1994; McKeever et al., 2016) or vaccination deemed necessary while traveling for protection 
against communicable diseases such as Ebola (NASEM, 2017), malaria, and now COVID-19. 

Those who use the informal social channels for science-related information are 
disproportionately people whose views counter the scientific consensus (McKeever et al., 2016). This 
phenomenon certainly appears to be the case in the climate change debate, gaining extensive traction 
in information sources like social media. Social media in the context of this study consists of social 
internet sites that do not include mass media (major Television and print sources), friends and family, 
scientific experts, or search engines. People who believe that climate change is indeed real are likely 
founding their belief on scientific evidence (Dunlap & McCright, 2010; McCright et al., 2016; McCuin 
et al., 2014; Ranney et al., 2012). 

Knowledge is not needed for having an opinion on a subject, which helps to explain why 
knowledge and attitudes do not seem to be linked (NASEM, 2017). In the case of genetically modified 
organisms (GMOs) in food, people think about whether that is a good thing or not, but most people 
do not know about them (McFadden & Lusk, 2016). In such cases, the news source can easily frame 
the topic to influence how individuals think about the issue. Similarly, people are faced with 



29  

complicated and inconsistent claims, for example, about the hazards and benefits of consuming added 
sugar, salt, and fats, while making their food choices to avoid obesity. The primary topic in front of 
them is whether they believe the food industry contributes to obesity and whether obesity is linked to 
health (Bleich et al., 2007; Dunlap & McCright, 2010). 

People believe the risk is more minor or the potential benefits as more significant when the 
information they receive is from an institution they trust (Chryssochoidis et al., 2009). Trust in 
institutions strongly influences support for nuclear power (Besley & McComas, 2015; Visschers & 
Siegrist, 2012; Whitfield et al., 2009). 

In each area discussed above, vaccinations, climate change, the safety of GMOs in food, 
obesity, health, and nuclear energy safety, individuals cling to their common-sense mental models or 
long-held views about how the world works (Vosniadou & Brewer, 1992). Nevertheless, there is also 
evidence that with scientific reasoning and evidence from formal sources, such thinking can be 
influenced and revised (Ranney et al., 2012) toward greater trust in science. News from formal and 
scientific sources can therefore increase awareness of how people perceive and act on a range of 
controversial scientific issues. 

 

Method 
This study employed a deductive quantitative research approach in a survey form(Creswell & 

Creswell, 2018). The data collection was done with an online survey instrument created from the 
scholarly articles described in the literature review, which provide the framework for studying the 
relationship between trust in science and the formal news source as the primary source for scientific 
information. All the statistical analyses presented in this paper are done with IBM SPSS Statistics 
version 27. 
Data Collection 

The online survey instrument was created in Qualtrics (https://www.qualtrics.com/), 
approved by the institutional review board, and given to students at a Midwestern university. This 
quantitative survey used Likert scale responses to measure the strength/intensity of responses for each 
question created by contextualizing the questions related to science-related public controversies and 
complexities of communicating science from NASEM (2017). The survey captured responses to a 
series of questions that would help understand their degree of belief in topics of science-related 
controversies from formal news sources. 
Sampling Frame 

The study used a sample drawn from undergraduate and graduate students at a large 
metropolitan midwestern university in the United States. This student population was chosen as a 
target group owing to their availability throughout the study's length, and a higher chance of 
consenting to participate. Additionally, the chosen university has a mature student body, with many 
students surveyed also working full-time in the industry outside the university. Table 1 shows the 
demographics of the survey respondents. The study obtained 207 valid survey responses. Using 
G*Power t-tests for multiple linear regressions, the a priori required sample size for testing the 
parameters of a two-tailed test with an effect size of 0.07, Alpha 0.05, Power 0.9, and 10 predictors 
required a minimum sample size of 153. The study sample size was 207 which was adequate to support 
the planned analysis. 
Data Analysis 

This research measures the trust in formal news sources as the source for obtaining facts and 
information. Eleven independent variables are employed in the model. These are described in Table 
2. The survey also asked respondents to rate their level of confidence in the news coming from formal 
news sources and scientists. This confidence formed the dependent variable FormalNewsSource in 
the study. The survey was conducted between October 2020 and February 2021. Ordinary least 

http://www.qualtrics.com/)


30  

squares (OLS) regression analysis was conducted to analyze the trust in formal news sources based on 
the respondents' trust in science. The stepwise regression method was used to iteratively construct the 
regression model by adding and removing independent variables in succession and testing for 
statistical significance after each iteration. This method allowed us to retain the most influential and 
statistically significant independent variables in the regression model. 

 
Table 1 – Sample Demographics 

 
Gender N %  Income N %  Age N % 
0 Female 146 70.5%  1 (Less than $31,000) 44 21.3%  1 (18y - 21y) 113 54.6% 

1 Male 59 28.5%  2 ($31,001 to $42,000) 33 15.9%  2 (22y - 25y) 33 15.9% 

2 Other 2 1.0%  3 ($42,001 to $126,000) 97 46.9%  3 (26y - 29y) 22 10.6% 
    4 ($126,001 to 

$188,000) 18 8.7%  4 (30y or 
Over) 39 18.8% 

    5 ($188,001+) 15 7.2%     

 
Education Level N %  College attending N % 
1 First-year undergrad 
student 79 38.2%  1 College of Education 12 5.8% 

2 Sophomore 25 12.1%  2 College of Engineering 16 7.7% 

3 Junior 13 6.3%  3 College of Health and Public Service 92 44.4% 

4 Senior 7 3.4%  4 College of Information 3 1.4% 

5 Graduate Student 83 40.1%  5 College of Liberal Arts and Social Sciences 22 10.6% 
    6 College of Merchandising, Hospitality, and 

Tourism 1 0.5% 

    8 College of Science 22 10.6% 
    9 College of Visual Arts and Design 1 0.5% 
    10 School of Journalism 5 2.4% 
    11 College of Business 33 15.9% 

 

Table 2 – Independent Variables 
 

Independent Variables 

1 ChildhoodVaccine Trust in childhood vaccines 

2 TravelVaccine Trust in vaccines for travel 

3 ClimateChange Trust in climate change predictions 

4 GMOSafety Trust in the safety of genetically modified organisms 

5 NuclearEnergySafety Trust in the safety of nuclear energy 

6 OverweightAndHealth Trust in the link between obesity and health 

8 Gender Gender of the respondent 



31  

9 Age Age of the respondent 

10 Income Income of the respondent 

10 CollegeMajor Major college of the respondent 

11 EducationLevel Graduate or undergraduate level of the respondent 
 

Results 
The survey questionnaire asked the respondents to rank their level of agreement with each 

statement about the selected science-related public controversy or long-held personal belief. The OLS 
regression was conducted in multiple passes to determine the best approach. Each result table 
presented herein identifies the variables, coefficients, t-statistic, p-values, and adjusted R-square for 
each regression run. 

Table 3 presents the results of this regression analysis. The first regression was conducted with 
all independent variables included in the regression model, and the adjusted R-square for this 
regression was 0.332. However, several independent variables, such as ChildhoodVaccine, 
GeneticModified, NuclearEnergy, Gender, Income, College, Age, EduLevel, were statistically 
significant in this regression. Therefore, a stepwise regression was subsequently run to iteratively 
construct the regression model by adding and removing independent variables in succession and 
testing for statistical significance after each iteration. Stepwise regression retains the most influential 
and statistically significant independent variables in the regression model. 

Table 4 presents the results of the stepwise regression analysis. This analysis identified three 
models, each with a different combination of independent variables. Model #3 emerged as the model 
with the highest adjusted R-square value and lowest standard error of the estimate. The adjusted R- 
square for model #3 was 0.335. This R-square is slightly better than the 0.332 adjusted R-square from 
the regression run with all variables included. However, each independent variable selected in model 
#3 – TravelVaccine, OverweightAndHealth, ClimateChange – was statistically significant with p- 
values <0.001. 

 
Table 3 – OLS Regression With All Variables Included 

 
Model Summary    

 
Model 

 
R 

 
R Square Adjusted R 

Square 

 
Std. Error of the Estimate 

   

 .607a 0.368 0.332 0.68906    

a. Predictors: (Constant), EduLevel, ClimateChange, Income, Gender, OverwtHealth, 
NuclearEnergy, College, ChildhoodVaccine, GeneticModified, Age, TravelVaccine 

   

ANOVAa 

Model Sum of 
Squares df Mean Square 

Error F Sig. 

 Regression 53.895 11 4.900 10.319 .000b 

Residual 92.585 195 0.475   

Total 146.481 206    

a. Dependent Variable: FormalNewsSource 



32  

b. Predictors: (Constant), EduLevel, ClimateChange, Income, Gender, OverwtHealth, NuclearEnergy, 
College, ChildhoodVaccine, GeneticModified, Age, TravelVaccine 

Coefficientsa  

 
 

Model 

Unstandardized 
Coefficients 

Standardize 
d 

Coefficients 

 
 

t 

 
 

Sig. 

95.0% Confidence 
Interval for B 

 

 
B Std. 

Error 

 
Beta Lower 

Bound 

 
Upper Bound 

 

 (Constant) 1.061 0.306  3.472 0.001 0.458 1.663  

ChildhoodVaccine 0.000 0.077 0.000 -0.001 1.000 -0.152 0.152  

TravelVaccine 0.212 0.076 0.293 2.768 0.006 0.061 0.362  

ClimateChange 0.156 0.055 0.202 2.822 0.005 0.047 0.265  

GeneticModified 0.039 0.059 0.057 0.666 0.506 -0.077 0.155  

NuclearEnergy -0.007 0.053 -0.010 -0.123 0.902 -0.112 0.099  

OverwtHealth 0.282 0.053 0.332 5.272 0.000 0.177 0.388  

Gender 0.048 0.108 0.027 0.444 0.657 -0.165 0.261  

Income 0.047 0.044 0.062 1.063 0.289 -0.040 0.133  

College -0.020 0.017 -0.077 -1.171 0.243 -0.053 0.014  

Age 0.018 0.072 0.025 0.253 0.801 -0.123 0.159  

EduLevel -0.078 0.049 -0.169 -1.602 0.111 -0.175 0.018  

a. Dependent Variable: FormalNewsSource  
 

Table 4 – Stepwise Regression Summary 
 

Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  

1 .439a 0.193 0.189 0.75938  
2 .552b 0.304 0.298 0.70669  
3 .587c 0.345 0.335 0.68748  

a. Predictors: (Constant), TravelVaccine  
b. Predictors: (Constant), TravelVaccine, OverweightAndHealth  

c. Predictors: (Constant), TravelVaccine, OverweightAndHealth, ClimateChange  
ANOVAa  

Model Sum of 
Squares df Mean 

Square F Sig. 

 
1 

Regression 28.266 1 28.266 49.016 .000b 
Residual 118.215 205 0.577   

Total 146.481 206    

 
2 

Regression 44.600 2 22.300 44.653 .000c 
Residual 101.880 204 0.499   

Total 146.481 206    

 
3 

Regression 50.536 3 16.845 35.642 .000d 
Residual 95.944 203 0.473   

Total 146.481 206    



33  

a. Dependent Variable: FormalNewsSource 
b. Predictors: (Constant), TravelVaccine 
c. Predictors: (Constant), TravelVaccine, OverweightAndHealth 
d. Predictors: (Constant), TravelVaccine, OverweightAndHealth, ClimateChange 

Coefficientsa 
 
 

Model 

Unstandardized 
Coefficients 

Standardized 
Coefficients 

 
 

t 

 
 

Sig. 

95.0% Confidence 
Interval for B 

B Std. 
Error Beta Lower 

Bound 
Upper 
Bound 

1 
(Constant) 2.354 0.189  12.485 0.000 1.982 2.725 
TravelVaccine 0.317 0.045 0.439 7.001 0.000 0.228 0.406 

 
2 

(Constant) 1.245 0.262  4.759 0.000 0.729 1.760 
TravelVaccine 0.298 0.042 0.412 7.039 0.000 0.214 0.381 
OverweightAndHealth 0.285 0.050 0.335 5.719 0.000 0.187 0.383 

 
 

3 

(Constant) 0.988 0.265  3.735 0.000 0.466 1.509 

TravelVaccine 0.217 0.047 0.300 4.602 0.000 0.124 0.309 

OverweightAndHealth 0.258 0.049 0.303 5.259 0.000 0.161 0.355 

ClimateChange 0.181 0.051 0.234 3.544 0.000 0.080 0.281 

a. Dependent Variable: Formal NewsSource 
 

The results shown in Table 4 were sufficient to determine the variables that were most influenced by 
formal news sources. However, the variables that were excluded from model #3 during stepwise 
regression, shown in Table 5, were further examined to evaluate if retaining any of these in the 
regression model would uplift the model fit. It is of note that the variable EduLevel was excluded with 
a p-value rounded to 0.050. Forced retention of this variable into the model did not yield any 
significant improvement in the R-squared value. Therefore, it was decided to stay with the regression 
model as determined by the stepwise method. 

 
Table 5 – Best model (Excluded Variables) 

 
Excluded Variablesa 

 
Variable 

 
Beta In 

 
t 

 
Sig. 

 
Partial Correlation 

 
Collinearity Statistics 

Tolerance 
Gender .055d 0.957 0.340 0.067 0.980 

Income .040d 0.694 0.488 0.049 0.996 

CollegeMajor -.007d -0.118 0.907 -0.008 0.992 

Age -.084d -1.430 0.154 -0.100 0.926 

EducationLevel -.114d -1.970 0.050 -0.137 0.946 

ChildhoodVaccine .003d 0.033 0.973 0.002 0.314 

GMOSafety .064d 0.932 0.352 0.065 0.683 

NuclearEnergySafety .034d 0.537 0.592 0.038 0.812 



34  

a. Dependent Variable: FormalNewsSource 
d. Predictors in the Model: (Constant), TravelVaccine, OverweightAndHealth, ClimateChange 

 

A puzzling factor was why TravelVaccine was included during stepwise regression and 
ChildhoodVaccine was excluded. The authors believe that those who do not believe in vaccines would 
not believe in any vaccine, or conversely, those who believe in the vaccines would unconditionally 
trust them regardless of whether they are trying to protect their children during their younger years or 
their family and themselves while traveling to regions where pre-travel vaccinations are highly 
recommended. To test our hypothesis, a correlation analysis was run comparing the variables 
TravelVaccine and ChildhoodVaccine. The results of the correlation analysis are shown in Table 6. As 
anticipated, a high correlation between these two variables with a Pearson correlation factor of 0.83 
was found. Including both these variables in the model would have presented a problem with 
collinearity, and therefore it now made sense to include the one that resulted in a higher adjusted R- 
square value. 

 
Table 6 – Correlation Results 

 
Correlations 

  ChildhoodVaccine TravelVaccine 
 
 
 

ChildhoodVaccine 

Pearson Correlation 1 .813** 

Sig. (2-tailed)  0.000 

Sum of Squares and Cross-products 260.734 220.126 

Covariance 1.266 1.069 

N 207 207 
 
 
 

TravelVaccine 

Pearson Correlation .813** 1 

Sig. (2-tailed) 0.000  

Sum of Squares and Cross-products 220.126 280.995 

Covariance 1.069 1.364 

N 207 207 

**. Correlation is significant at the 0.01 level (2-tailed). 
 

From these results, one can conclude that people who believe in science, as evidenced by their 
beliefs in topics of science-related controversies, trust formal news sources for obtaining scientific 
facts and information. Among the science-related controversies or long-held beliefs are belief in 
vaccinations (these are people who want to ensure their families and loved ones are safe and well- 
protected while at home or away), belief in scientific predictions (understand the climate change, its 
impact, and the future predictions), recognize the importance of being healthy (understand the 
relationship between obesity and health), and degree of maturity achieved through a level of academic 
education. Listening to or following formal news sources or mainstream news correlates to a belief in 
scientific concepts. Therefore, belief in science is highly correlated to where people get their news. 



35  

Discussion and Future Research 
Communicating scientific impact on business is difficult. This research confirmed that news 

coming from formal sources such as mainstream media and scientists is the most impactful. Among 
the areas of belief that were found to be significantly correlated to getting news from formal sources 
were belief in vaccinations (these are people who want to ensure their families and loved ones are safe 
and well-protected while at home or away), belief in predictive sciences (understand the climate 
change, its impact, and the future predictions), and recognize the importance of being healthy 
(understand the relationship between obesity and health). The areas that did not influence were 
individuals' gender, income, age, college major, education level, and their beliefs in the safety of nuclear 
energy and the safety of genetically modified organisms. 

Mainstream news and formal news sources influence people, making them more likely a 
believer in science as measured by their beliefs in scientific concepts measured and analyzed in this 
study. This information is useful to anyone interested in communicating science effectively. At the 
time of this writing, there is a reluctance on the part of many to take COVID-related actions based on 
the scientific information available. This research furthers the knowledge base in support of this and 
other communicating science issues. 

A similar future study relating to those who primarily consume news from information sources 
such as social media and through friends and family would help put an alternate perspective on trust 
in science. 

 

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	Introduction
	Literature Review
	Method
	Data Collection
	Sampling Frame
	Data Analysis
	Results
	Discussion and Future Research
	References

