The Diversity of Participants in Environmental Citizen Science

Abstract

Reported benefits of environmental citizen science include the collection of large volumes of data, knowledge and skills gained by participants, local action, and policy influence. However, it is unclear how diverse citizen science participants are, raising concerns about representativeness of data and whether individual, societal, and environmental benefits are evenly distributed. We surveyed 8,220 people representing a cross section of the population in Great Britain to ask whether they had participated in environmental citizen science, allowing us to examine who is and who is not participating. Using logistic regression, we examined relationships between demographic variables, and crucially the interactions between these variables, and the likelihood of participation and whether participation was repeated. Men were more likely to participate than women. People identifying as from white ethnic groups were more likely to participate than those identifying as from minority ethnic groups; participation by women from minority ethnic groups was particularly low. Participation by those from white ethnic groups declined with socio-economic status, but this was not the case for those from minority ethnic groups. Participation was highest amongst those in education (studying at school, college, or university) and lowest amongst the unemployed. We recommend citizen science practitioners carefully consider the aims of projects and thus the diversity of participants they wish to attract. We discuss potential mechanisms for widening participation, for example, engaging participants through third parties already embedded in communities and providing a variety of tasks for people with different amounts of time and types of skills to offer. Finally, we encourage practitioners to document and publish participant demographics to monitor diversity in citizen science.

Full text

RESEARCH PAPER
ABSTRACT
Reported benefits of environmental citizen science include the collection of large volumes
of data, knowledge and skills gained by participants, local action, and policy influence.
However, it is unclear how diverse citizen science participants are, raising concerns
about representativeness of data and whether individual, societal, and environmental
benefits are evenly distributed. We surveyed 8,220 people representing a cross section
of the population in Great Britain to ask whether they had participated in environmental
citizen science, allowing us to examine who is and who is not participating. Using logistic
regression, we examined relationships between demographic variables, and crucially the
interactions between these variables, and the likelihood of participation and whether
participation was repeated. Men were more likely to participate than women. People
identifying as from white ethnic groups were more likely to participate than those
identifying as from minority ethnic groups; participation by women from minority ethnic
groups was particularly low. Participation by those from white ethnic groups declined with
socio-economic status, but this was not the case for those from minority ethnic groups.
Participation was highest amongst those in education (studying at school, college,
or university) and lowest amongst the unemployed. We recommend citizen science
practitioners carefully consider the aims of projects and thus the diversity of participants
they wish to attract. We discuss potential mechanisms for widening participation, for
example, engaging participants through third parties already embedded in communities
and providing a variety of tasks for people with different amounts of time and types of
skills to offer. Finally, we encourage practitioners to document and publish participant
demographics to monitor diversity in citizen science.
CORRESPONDING AUTHOR:
Rachel Pateman
Stockholm Environment
Institute, Department of
Environment and Geography,
University of York, UK
rachel.pateman@york.ac.uk
KEYWORDS:
demographics; ethnicity;
socio-economic status; gender;
recruitment; barriers
TO CITE THIS ARTICLE:
Pateman, R, Dyke, A and West,
S. 2021. The Diversity of
Participants in Environmental
Citizen Science. Citizen Science:
Theory and Practice, 6(1): 9,
pp. 1–16. DOI: https://doi.
org/10.5334/cstp.369
RACHEL PATEMAN
ALISON DYKE
SARAH WEST
*Author affiliations can be found in the back matter of this article
The Diversity of Participants
in Environmental Citizen
Science

2Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
INTRODUCTION
Many benefits of citizen science methods have been
discussed, including benefits for scientific research, such
as collecting large amounts of data (Hochachka et al.
2011) and drawing on local knowledge (Lidskog 2008); for
environmental monitoring and decision-making at local
(Ballard, Dixon, and Harris 2017), national (Hayhow et al.
2019) and international (Turbé et al. 2019) scales; and
for participants, including the ability to gain knowledge
(Bonney et al. 2016) and skills (Stedman et al. 2009),
build communities (Fernandez-Gimenez, Ballard, and
Sturtevant 2008) and use data for advocacy (Johnson et
al. 2014). However, there are doubts as to whether citizen
science participants are representative of wider society,
with biases in age, gender, ethnicity, and socio-economic
status all reported (NASEM 2018), and this has important
consequences for many of these reported benefits.
However, our understanding of who is participating in citizen
science is still limited. Previous research focusses largely
on participants in individual projects, is geographically
restricted, and examines demographic characteristics
independently rather than looking at how they interact
with each other to affect participation. We begin here by
discussing how diversity is relevant to many aspects of the
purported benefits of citizen science and how inequalities
might arise through a lack of diversity in participants. Our
focus is on environmental citizen science as one of the
largest and most established fields in citizen science. We
then present results of a large study of a cross-section of the
population in Great Britain that examines who is and who is
not participating in environmental citizen science. Our aim
is to generate a clear understanding of the demographics
of participation to start to address arising inequalities.
BENEFITS AND INEQUALITIES IN CITIZEN
SCIENCE
There are many reported benefits that a citizen science
approach may have for science and decision-making. These
benefits vary depending on the aims of a project but can
include data generation across broad spatial and temporal
scales and/or at fine spatial and temporal resolutions, and
data collection from otherwise inaccessible areas that
enables insights that would not be possible without citizen
engagement (Bonney et al. 2016; Fritz et al. 2019). Citizen
science data provide the evidence base for thousands
of scientific papers (Kullenberg and Kasperowski 2016)
as well as for official environmental reporting (e.g., UK
Biodiversity Indicators used for assessing progress towards
Aichi biodiversity targets; Hayhow et al. 2019) and policy-
making (Turbé et al. 2019). However, if some sectors of
society are not participating in citizen science and there is a
correlation between environmental variables and population
demographics, the data generated may not reveal the true
state of the environment (Purcell, Garibay, and Dickinson
2012). This in turn could have important consequences for
the reliability of scientific conclusions as well as decision-
and policy-making.
Citizen science can also have many benefits for
participants. These include gaining knowledge (Evans et al.
2005; Jordan et al. 2011; Bonney et al. 2016; Phillips et al.
2019) and skills, for example in environmental monitoring
or in the communication of results (Stedman et al. 2009).
These benefits could result in greater employability;
Geoghegan et al. (2016), for example, found that 10%
of survey respondents had participated in citizen science
to help their future career. Citizen science participation
can increase a participant’s sense of place (Evans et al.
2005), reduce stress (Coventry et al. 2019), and lead to the
formation of new relationships and communities (West et
al. 2020) with the potential for social learning, whereby
people learn from each other via observation and imitation
(Becker et al. 2005; Fernandez-Gimenez, Ballard, and
Sturtevant 2008; Dickinson et al. 2012). Those who do not
have the opportunity to participate in citizen science will be
excluded from receiving these myriad benefits.
Finally, the process of bringing scientists and citizens
together is also purported to have numerous benefits
that, again, will be limited by a lack of diversity in
participants. Participants can gain a better understanding
of the scientific process and the relevance of science to
their daily lives, as well as develop critical thinking skills
(Trumbull et al. 2000; Bonney et al. 2016; Merenlender
et al. 2016), which can help in scientifically relevant
decision-making (Dickinson et al. 2012). In addition,
scientists and other citizen science project leaders often
aim to raise awareness of environmental issues, change
participants’ environmental values and perspectives, and
in turn influence behaviour (Fernandez-Gimenez, Ballard,
and Sturtevant 2008; Couvet and Prevot 2015; Bonney et
al. 2016; Ballard et al. 2017). Through increased scientific
literacy, project leaders also aim to generate a greater
acceptance of outcomes of science (Stone 2015; Brouwer
and Hessels 2019). Exclusion of some sectors of society
will limit the reach of these impacts for participants and
scientists. Engagement between citizens and scientists
can also challenge the traditional expert-citizen hierarchy.
Working with citizens can open scientists’ eyes to new
questions and considerations (Burke and Heynen 2014),
potentially creating more relevant and democratic science
(Irwin 1995). Working together can also give scope for
incorporating local, often place-based, knowledge into the
scientific process (Bäckstrand 2003; Lidskog 2008; Cigliano
et al. 2015; Ramirez-Andreotta et al. 2015; Kimura and

3Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
Kinchy 2016), which is important for ensuring science is
relevant to society and can lead to local action (see Lidskog
2008 for some examples). However, only the priorities
of groups who are participating in citizen science will be
represented. Innovation, invention, and creativity are more
likely to be fostered when people of diverse backgrounds
are brought together (Woolley et al. 2010; Dickinson et al.
2012; NASEM 2018) and this opportunity will be lost if there
is a lack of diversity in citizen science participants.
DIVERSITY IN CITIZEN SCIENCE
Given the potential impacts of citizen science and the
injustices that may arise from some sectors of society
not participating, it is vital to explore who is, and who is
not, represented in citizen science so inequalities can
begin to be addressed. Those studies that have examined
participant demographics have shown higher rates of
participation in people who are of middle or older age (Crall
et al. 2013; Wright et al. 2015; Domroese and Johnson
2017; Mac Domhnaill, Lyons, and Nolan 2020), have higher
levels of education (Trumbull et al. 2000; Evans et al. 2005;
NASEM 2018; Mac Domhnaill, Lyons, and Nolan 2020), have
higher household incomes (Overdevest, Orr, and Stepenuck
2004; Mac Domhnaill, Lyons, and Nolan 2020), are in
areas with lower levels of deprivation (Hobbs and White
2012), are employed (Crall et al. 2013; Mac Domhnaill,
Lyons, and Nolan 2020), are in rural areas (Evans et al.
2005; Mac Domhnaill, Lyons, and Nolan 2020), and who
identify as being from white ethnic groups compared
with those identifying as from other ethnic groups (e.g.,
Wright et al. 2015; Merenlender et al. 2016; Domroese
and Johnson, 2017; NASEM 2018). Participation by gender
is less straightforward, with some studies finding higher
participation rates in men (Wright et al. 2015; Ganzevoort
et al. 2017; NASEM 2018), others in women (Crall et al.
2013; Merenlender et al. 2016; Domroese and Johnson
2017), and others finding no difference (Mac Domhnaill,
Lyons, and Nolan 2020), which may be a result of the type
of project or location of participants being examined. In
their meta-analysis, for example, Pandya and colleagues
(NASEM 2018) found that the male bias in participation was
stronger in projects focussed on physical science compared
with biological science, in online projects, and in roles with
increasing levels of competition and responsibility.
AIMS OF THE STUDY
Although this literature provides some insights, a recent
review of citizen science (including non-environmental
citizen science) literature (NASEM 2018) found that only
10% of papers presented any data relating to participant
demographics, and most projects that did present data
(75%) were based wholly or partially in the United States
(US), with nearly a quarter of these relating to online-
only projects. The aim of the study we present here is
to describe the demographics of environmental citizen
science participants in Great Britain. We have focussed
on Great Britain because it has a long history of citizen
science (Pocock et al. 2015) and is a major contributor to
citizen science globally (an estimated 7.5 million volunteer
hours are spent annually on biodiversity monitoring alone
in Great Britain and Northern Ireland; Hayhow et al. 2019)
but has received very little attention when it comes to the
demographics of participants (Hobbs and White 2012 is an
exception to this).
Unlike previous surveys, which have either looked at the
demographics of participants in individual projects (e.g.,
Evans et al. 2005; Domroese and Johnson. 2017) or used
purposive sampling (i.e., targeting known citizen science
participants) to survey the characteristics of participants
in particular fields such as biological recording (Ganzevoort
et al. 2017; Mac Domhnaill, Lyons, and Nolan 2020),
we conducted a national survey of people for whom we
have no prior knowledge of their engagement with citizen
science, thus avoiding the partiality of a self-selecting
sample (Berk 1983). This allowed us to understand diversity
in citizen science participants, i.e., how representative
the pool of citizen science participants is of the wider
population (Brouwer and Hessels 2019). By conducting a
survey through a third party, we also reduced the risk of
social desirability bias (where participants try to give an
answer they think would please the questioner) (Nederhof
1985), compared with a situation in which an interview was
conducted by, for example, a citizen science organisation.
Furthermore, in contrast to previous studies, we also
examine how different participant characteristics interact
to affect participation in order to consider intersectionality.
This is important because social categorisations such as
ethnicity, gender, and age do not operate alone but interact
with each other and can create overlapping systems of
disadvantage (Cho, Crenshaw, and McCall 2013). By giving
an overview of who is and who is not participating in citizen
science, we hope to encourage practitioners to consider
how to address this in their project design, including seeking
understanding of potential barriers to excluded groups and
how these can be overcome.
METHODS
NATIONAL SURVEY VIA A MARKET RESEARCH
COMPANY
We commissioned TNS UK Ltd (www.tnsglobal.com/united-
kingdom), a data provider that is now part of the Kantar
market research group (www.kantar.com), to deploy a
national survey to understand who has participated in

4Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
environmental citizen science in Great Britain. The survey
was undertaken as part of TNS UK’s weekly Omnibus survey
of a stratified sample of UK households to which anyone
could pay for questions to be included (see Supplemental File
1 for full details of how households are selected). Selected
households were visited by interviewers who explained
TNS, the purpose of the interview, and why the household
had been selected for participation. If residents agreed to
participate, interviews were conducted immediately, face-
to-face, using Computer Assisted Personal Interviewing,
whereby the interviewer used an electronic device to record
answers to questions. Our survey ran for two consecutive
weeks in May 2015. Interviews were conducted only with
people 16 and over and no incentives were offered for
taking part.
QUESTIONS ASKED
For our study, interviewees were asked, “Have you ever
taken part in any type of project that involved collecting
any environmental scientific information or data?” For
clarification, the interviewer added, “By this we mean
national projects that help scientists like the RSPB Big
Garden Birdwatch, one of the OPAL Surveys on worms,
climate, tree health, biodiversity, bugs or water, or a local
project.” We chose this wording because all citizen science
projects include an element of data collection (Cooper and
Lewenstein 2016) and we wanted people to think about
both national and local projects, and contributory and co-
created forms of citizen science. However, we may have
missed people who are involved in citizen science projects
in other ways, for example analysing data or disseminating
results. Respondents could answer “Yes, once,” “Yes, more
than once,” “No,” or “Don’t know/can’t remember.” TNS UK
also collected a wide range of demographic information
from all interviewees. Interviewees could refuse to provide
any or all of this information. The variables we used in our
analysis are described in the “Data analysis” section below.
Because we asked people if they had ever taken part in
citizen science, their demographic characteristics at the
time of the survey may not have been the same as those
during the period in which they did the data collection,
which may affect the interpretation of results.
ETHICS
TNS UK abides by the Market Research Society Code of
Conduct (MRS Evidence Matters 2019), which regulates
all market research activity in the UK in compliance with
data protection and human rights legislation. Details of
TNS UK’s quality assurance and ethics protocols are given
in Supplemental File 1. The survey was also approved by
the University of York Department of Environment and
Geography’s Ethics Committee.
DATA ANALYSIS
We used chi-squared tests to test for relationships between
demographic variables and (1) whether people had
participated in citizen science and (2) if they repeated this
participation. First, people who responded “Don’t know/
can’t remember” were removed from the sample. Then, for
each demographic variable in turn, we carried out separate
tests where the inputs were the number of people who
had and the number of people who had not participated
in citizen science in different groups of the variable. We
then used data only from respondents who said they had
participated in citizen science and carried out tests for each
demographic variable where the inputs were the number
of people who had participated once and the number who
had participated more than once in each group of the
variable. Where chi-squared test results were significant,
adjusted residuals were calculated, and groups for which
the value was greater than 2 were considered to be drivers
of the significant result.
The demographic variables we considered were age,
gender, ethnicity, social grade, work status, and area as
these have all been identified previously as factors that
relate to participation (see Introduction). Groupings within
these are shown in
Table 1
. For ethnicity, interviewees could
select from 16 groups, plus an option to decline providing
this information. We initially present participation rates for
all these groups; however, owing to the small numbers of
interviewees for some groups (14 groups had fewer than
10 interviewees who had participated in citizen science),
for our analysis we combined interviewees into the
following broader groupings: white, mixed white and other
ethnic groups, Asian, Black, and other. For the repeated
participation test, because of small numbers in some
groups, we combined the mixed ethnicity, Asian, Black and
other ethnicity groups into a single minority ethnic group.
Although these categories are far from perfect (for example,
this potentially excludes those identifying as from minority
white groups), they are used in similar demographic
studies in the UK (e.g., The Royal Society 2014; DCMS
2018). Social grade is a socio-economic classification used
in the UK based on occupation. Groups are defined by the
Market Research Society (MRS Evidence Matters, undated)
as higher managerial, administrative, and professional
occupations (A); intermediate managerial, administrative,
and professional occupations (B); supervisory, clerical
and junior managerial, administrative, and professional
occupations (C1); skilled manual occupations (C2);
and semi-skilled and unskilled manual occupations,
unemployed, and lowest grade occupations (DE). Area
categorises people as being from rural or urban settings,
with rural being defined as outside of settlements with
more than 10,000 people.

5Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
Considering each variable separately allowed us to
interrogate more groups within each variable before
moving on to include all variables in a single model,
where sample sizes required us to combine some groups.
Including all variables in single models, however, allowed
us to examine the issue of intersectionality by determining
whether different demographic variables had independent
effects on citizen science participation or whether these
variables interacted with each other. We used binary
logistic regression, which requires a binary response
variable and one or more explanatory variables and tests
for a relationship between the explanatory variable(s) and
the probability of a particular outcome of the response
variable. In our first model, each of our interviewees was
VARIABLE GROUP ESTIMATED
% GB 16+
POPULATION1
% (NUMBER)
IN SAMPLE2
% (NUMBER) WHO
PARTICIPATED IN
CITIZEN SCIENCE3
PARTICIPATION
χ2 TEST RESULT4
% (NUMBER)
WITH REPEATED
PARTICIPATION3
REPEATED
PARTICIPATION
χ2 TEST RESULT4
Total 8,220 7.5 (613)
Gender Male 48.5% 47.8% (3,931) 8.3% (323)* χ2 = 6.323
P
= 0.012
55.1% (178) χ2 = 2.059
P = 0.151
Female 51.5% 52.2% (4,289) 6.8% (290)* 49.3% (143)
Age 16–24 14.4% 15.1% (1,238) 7.1% (88) χ2 = 35.175
P
< 0.001
38.6% (34)* χ2 = 25.620
P
< 0.001
25–34 16.1% 17.5% (1,438) 4.1% (58)* 44.8% (26)
35–44 17.6% 14.6% (1,199) 9.2% (109)* 41.3% (45)*
45–54 17.5% 14.8% (1,215) 8.3% (100) 53.0% (53)
55–64 11.9% 12.5% (1,024) 9.3% (94)* 64.9% (61)*
65+ 22.4% 25.6% (2,106) 7.5% (164) 62.2% (102)*
Ethnicity White 85.6% 85.6% (7,057) 8.1% (565)* χ2 = 24.821
P
< 0.001
53.5 (302)* χ2 = 4.104
P
= 0.043
Mixed 1.5% 1.4% (114) 7.0% (8) 37.8 (17)*
Asian 7.6% 7.3% (615) 3.4% (21)*
Black 4.5% 4.5% (367) 3.9% (14)*
Other 0.5% 0.5% (37) 5.6% (2)
Work
status
Full time
employed
51.2% 32.2% (2,650) 8.1% (214) χ2 = 37.682
P
< 0.001
49.5% (106) χ2 = 7.951
P = 0.093
Part time
employed
14.3% (1,172) 7.9% (92) 48.9% (45)
Unemployed 48.8% 17.3% (1,420) 3.9% (55)* 49.1% (27)
Retired 28.4% (2,334) 7.9% (184) 60.9% (112)
In education 7.8% (644) 10.6% (68)* 45.6% (31)
Social
grade
A 3.5% 2.8% (227) 15.9% (36)* χ2 = 27.537
P
< 0.001
66.7% (24) χ2 = 11.458
P
= 0.022
B 18.6% 14.6% (1,202) 16.7% (199)* 57.8% (115)
C1 33.4% 26.4% (2,166) 8.9% (191)* 52.4% (100)
C2 20.3% 20.8% (1,708) 5.4% (91)* 40.7% (37)*
DE 24.2% 35.5% (2,917) 3.3% (96)* 46.9% (45)
Area Urban 80.9% 79.7% (6,767) 7.3% (488) χ2 = 0.020
P = 0.069
50.2% (245)* χ2 = 4.478
P
= 0.034
Rural 19.1% 20.3% (1,453) 8.7% (125) 60.8% (76)*
Table 1 Participation in citizen science by demographic group.
1 Data provided by TNS UK Ltd as part of the survey results; work status data were available only for unemployed and employed).
2 Group numbers for ethnicity do not add up to 8,220 because some interviewees did not provide this information.
3 Groups for which adjusted residuals were greater than two (i.e., those considered to be driving the significant result) are starred.
4 Significant results are in bold.

6Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
a data point, and whether or not they had participated
in citizen science was the binary response variable. In
our second model, each of the interviewees who had
participated in citizen science was a data point, and
whether their participation was one-off or repeated was
the binary response variable. Our explanatory response
variables were the categorical demographic variables
described above, and all of their second-order interactions
(e.g., interactions between age and ethnicity; social grade
and gender, etc.). Work status was not included because
of a lack of power in the model and an overlap between
work status and age. For ethnicity, we used the combined
minority ethnic group described above. We also combined
the oldest two age groups into a single group of over 55,
and social grades A and B into a single AB group.
To identify variables and interactions between variables
that had a significant effect on likelihood of citizen science
participation and likelihood of repeated participation,
backward and forward stepwise regressions were performed
on the initial models. This process retains only variables and
interactions between variables that have a significant effect
on the response variable in a minimal adequate model.
Main effects were included in the final model if they were
present in a retained interaction term. We used the stepAIC
function in the MASS package (Venables and Ripley 2002) in
the R statistical software (R Core Team 2017).
RESULTS
A total of 8,220 people were surveyed.
Table 1
shows
the estimated percentage of the over-16 population of
Great Britain in each of our demographic groups and the
percentage of our sample of 8,220 in these groups, showing
that our sample represented the wider population well. Of
the 8,220 interviewees, 59 people (0.7%) responded “Don’t
know/can’t remember” to our question about citizen
science participation and were excluded from further
analyses; 613 (7.5% of the remaining sample) said that
they had taken part in a project that involved collecting
environmental scientific information or data; and 321
(52.4%) of these had participated more than once.
INDIVIDUAL DEMOGRAPHIC VARIABLES
Results of chi-squared tests can be seen in
Table 1
. There was
a significant relationship between gender and participation
in citizen science, with fewer women participating than
men; but there was no significant difference in single versus
repeated participation between genders. There was also
a significant relationship between age and participation,
driven by low participation in 25- to 34-year-olds and
high participation in 35- to 44- and 55- to 64-year-olds.
Repeated participation rates were significantly lower in
younger than older age groups.
There was a significant relationship between ethnicity
and participation, driven by high participation by people
identifying as belonging to white ethnic groups and low
participation in people belonging to Asian and Black
ethnic groups. Rates of repeated participation were also
significantly higher amongst those identifying as from
white ethnic groups than those identifying as being from
minority ethnic groups. Looking at participation in the
16 ethnic groups separately, however, shows variation
within these combined groups (
Figure 1
). For example,
participation appears higher in those identifying as white
British and white Irish than those identifying as belonging to
other white ethnic groups. Rates of participation amongst
people identifying as from some mixed ethnic groups were
also high, with those identifying as being from mixed white
and Asian groups having the highest rates of participation
of any ethnic group. There was some variation amongst
Asian and Black ethnic groups, with the lowest rates of
participation being in the Pakistani and African ethnic
groups, respectively. It is important to note, however, that
the small sample sizes in some of these groups makes it
difficult to draw any firm conclusions.
There was a significant relationship between work
status and participation, driven by low participation in the
unemployed and high participation in those in education,
but there was no difference in rates of single versus
repeated participation between these groups. There was
also a significant relationship between social grade and
participation, driven by high participation in those from socio-
economic groups A, B, and C1 (non-manual professions),
and low participation in those in socio-economic groups C2
and DE (manual professions and the unemployed). Similarly,
rates of repeated participation were lower amongst C2 and
DE groups, with low participation in the C2 group driving
the significant result. Finally, there was no significant
relationship between whether people were in urban or rural
areas and participation, but rates of repeated participation
were significantly higher in rural than in urban areas.
INTERACTIONS BETWEEN DEMOGRAPHIC
VARIABLES
In the logistic regression model that examined likelihood
of participating in citizen science, the variables retained
in the minimal adequate model were the interactions
between age and ethnicity, social grade and ethnicity,
gender and ethnicity, and age and area (
Table 2
). The
significant interaction between age and ethnicity showed
that participation amongst those identifying as from
minority ethnic groups was highest in 16- to 24-year-olds
(7.7%) and then fell to between 2.2% and 3.4% for all other

7Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
age groups, whereas for respondents identifying as from
white ethnic groups, participation was at 6.9% for 16- to
24-year-olds, dropped to 4.3% for 25- to 34-year-olds, and
then rose to between 8.5% and 10.7% for all remaining
age groups (
Figure 2a
). The significant interaction between
ethnicity and social grade revealed that, amongst people
identifying as from white ethnic groups, there was an
increase in citizen science participation moving from social
grade DE through to AB, but this did not follow amongst
those identifying as from minority ethnic groups, where
participation was highest in those in social grade C1 and
lowest in C2, with AB and DE falling in between (
Figure 2b
).
A significant interaction between ethnicity and gender
revealed that amongst those identifying as from both
white and minority ethnic groups, women were less likely
to participate than men, but this disparity was greater
for those from minority ethnic groups (
Figure 2c
), where
participation amongst women was very low. Finally, the
significant interaction between age and area showed that
amongst 25- to 34- and 45- to 54-year-olds, participation
was higher in rural than in urban areas, whereas in 16- to
24-year-olds, participation was higher in urban than in rural
areas (
Figure 2d
). In the repeated participation model, social
grade, gender, ethnicity, age, and the interaction between
gender and ethnicity were retained in the final model. The
interaction between gender and ethnicity showed that not
only is participation in women from minority ethnic groups
less likely than those from other groups, but amongst those
who had participated, repetition of this participation was
also less common than for other groups (
Figure 2c
).
DISCUSSION
Our study of a large cross section of the population in Great
Britain has revealed that environmental citizen science
Figure 1 Percentage of interviewees identifying as being in each ethnic group who had participated in citizen science. Labels show the
total number of respondents in each ethnic group.

8Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
GROUP1COEFFICIENT ESTIMATE (β)2ODDS RATIO (EXP(β))3STD. ERROR Z VALUE P VALUE
Citizen science participation
Constant −1.202 0.301 0.558 −2.154 0.031
Gender Female 0.450 1.568 0.373 1.205 0.228
Age 25–34 −0.460 0.631 0.593 −0.776 0.438
35–44 1.783 5.946 0.592 3.010 0.003
45–54 0.885 2.422 0.600 1.474 0.140
55+ 2.579 13.183 1.077 2.394 0.017
Social grade C1 −1.561 0.210 0.508 −3.075 0.002
C2 −0.987 0.373 0.753 −1.311 0.190
DE −2.983 0.051 0.557 −5.352 0.000
Ethnicity ME −0.326 0.722 0.483 −0.675 0.500
Area Rural −0.414 0.661 0.387 −1.068 0.286
Age*Ethnicity 25–34*ME −0.275 0.760 0.458 −0.599 0.549
35–44*ME −1.467 0.231 0.505 −2.904 0.004
45–54*ME −0.835 0.434 0.507 −1.646 0.100
55+*ME −2.445 0.087 1.042 −2.346 0.019
Social grade* Ethnicity C1*ME 0.853 2.346 0.470 1.816 0.069
C2*ME −0.201 0.818 0.717 −0.281 0.779
DE*ME 1.182 3.262 0.506 2.336 0.019
Gender*Ethnicity Female*ME −0.585 0.557 0.339 −1.726 0.084
Age*Area 25–34*Rural 1.097 2.996 0.497 2.206 0.027
35–44*Rural 0.146 1.157 0.488 0.299 0.765
45–54*Rural 0.742 2.101 0.462 1.607 0.108
55+*Rural 0.388 1.473 0.419 0.926 0.355
Repeat participation
Constant −0.503 0.605 0.562 −0.896 0.370
Social grade C1 −0.091 0.913 0.207 −0.439 0.661
C2 −0.726 0.484 0.259 −2.803 0.005
DE −0.444 0.641 0.257 −1.730 0.084
Gender Female 2.717 15.128 1.161 2.339 0.019
Ethnicity ME 0.376 1.457 0.409 0.919 0.358
Age 25–34 0.276 1.318 0.357 0.773 0.440
35–44 −0.055 0.946 0.309 −0.179 0.858
45–54 0.520 1.683 0.315 1.652 0.099
55+ 0.942 2.565 0.276 3.413 0.001
Gender*Ethnicity Female*ME −2.821 0.060 1.120 −2.517 0.012
Table 2 Results of stepwise logistic regression models, showing the main effects variables and interactions retained in the minimal
adequate models.
1 ME refers to minority ethnic groups.
2 ‘Constant’ shows the predicted coefficient estimate (β) when all variables are in their baseline groups: male for gender, 16–24 for age, AB
for social grade, white for ethnicity and urban for area.
3 For the participation model, the odds ratio represents the odds of someone participating in citizen science compared with the baseline
group. For gender, for example, the odds of a female participating in citizen science is 0.873 that of a male. For the repeated participation
model, the odds ratio is the odds of someone participating in citizen science multiple times compared with the baseline group.

9Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
participants are not representative of the wider population.
While some of our findings echo those of previous studies in
Europe and North America, our approach of looking across
the population and at interactions between demographic
variables means we are able to offer further insights.
PATTERNS IN PARTICIPATION
Overall, our results present a picture of typically
marginalised groups in society also underrepresented in
citizen science. These results reflect patterns in volunteering
in general, where people with lower social capital (the
ability to obtain benefits by being part of social networks
(Portes 1998)), lower human capital (levels of education
and skills), and lower economic capital are less likely to
volunteer (Rutherford et al 2019; Southby, South, and
Bagnall 2019). Although previous research has not found
consistent patterns in participation by gender, perhaps due
to differences in projects that have been examined (NASEM
2018), here we have shown that across environmental
citizen science in Great Britain as a whole, women are
less likely to participate than men (8.2% of men had
participated compared with 6.8% of women). Also in line
with previous studies (Wright et al. 2015; Merenlender et
al. 2016; Domroese and Johnson 2017; NASEM 2018), we
found higher rates of participation in those identifying as
from white ethnic groups compared with those identifying
as belonging to minority ethnic groups. We have also
shown that participation amongst women from minority
ethnic groups is particularly low (2.6% of respondents
had participated in citizen science compared with 7.4% of
women from white ethnic groups and 5.7% of men from
minority ethnic groups, and less than 1% of this group
had participated more than once). Unemployed people
and those from lower socio-economic groups were also
shown to be underrepresented, with 3.3% of the lowest
socio-economic group compared with 15.9% of the
highest having participated, confirming for Great Britain
patterns observed in other countries (Trumbull et al. 2000;
Overdevest, Orr, and Stepenuck 2004; Evans et al. 2005;
Crall et al. 2013; Mac Domhnaill, Lyons, and Nolan 2020).
These patterns reflect barriers to participation in
citizen science identified previously, which are likely to
have a particular impact on marginalised groups. Lack
of time, for example, has been repeatedly identified as a
barrier (Everett and Geoghegan 2016; Merenlender et al.
2016; Domroese and Johnson 2017), which may explain
underrepresentation of groups likely to have more caring
responsibilities (i.e., women, particularly those from
Figure 2 Interactions between participant characteristics on likelihood of citizen science participation. In each case, figures show the
percentage of interviewees in each group who had participated in citizen science. Interactions are between (a) ethnicity and age, (b)
ethnicity and social grade, (c) ethnicity and gender, and (d) area and age. In (c), the hatched areas show the percentage of the group that
had participated in citizen science more than once.

10Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
minority ethnic groups [Clark and Drinkwater 2007]),
and those from lower socio-economic groups who may
have multiple jobs and poorer transport options (Evans
et al. 2005; Pandya 2012). This latter group may also
be exluded by prohibitive participation costs, including
those for transport and for equipment (Merenlender et
al. 2016). Furthermore, a lack of previous experience of
scientific methods has been identified as a barrier for those
with lower levels of formal education (Evans et al. 2005;
Merenlender et al. 2016). Although not all environmental
citizen science projects take place in the countryside, where
they do, a barrier for people from minority ethnic groups
could be a lack of sense of belonging in these landscapes
(Ward-Thompson et al. 2003) as they are seen to be places
inhabited by white people (Agyeman and Spooner 1997).
Feeling uncomfortable in (Levine, González, and Martínez-
Sussmann 2009) and difficulties in accessing (Evans 2005)
natural environments may also be a barrier for people
from urban settings, which may explain our finding of
lower rates of repeated participation in people from urban
environments. Underrepresentation of certain groups may
also result from projects not aligning with the motivations,
interests, or needs of these groups (Pandya 2012). Poor
alignment may be a result of lack of diversity in the science
profession, which displays similar patterns of participation
to those identified here (The Royal Society 2014). A lack
of diversity amongst those setting research agendas and
designing citizen science projects may mean they do not
appeal to the priorities of marginalised communities.
Our study also offers new insights about participation
amongst different age groups. We found 25- to 34-year-
olds were less likely to have participated in citizen science
than people in other age groups, in line with previous
studies that have shown higher participation in middle-
aged and older people (Crall et al. 2013; Wright et al. 2015;
Domroese and Johnson 2017; Mac Domhnaill, Lyons, and
Nolan 2020). Again, time constraints may explain the lower
rates of participation we observed in this group as they are
more likely to have young families and be investing time
in building their careers (Merenlender et al. 2016). This
may also explain the particularly low rates of participation
in people from urban areas in this age group, for whom
travel time may be more of a barrier than for people in
rural areas. Unlike previous studies, however, we found
higher rates of participation in 16- to 24- than in 25- to
34-year-olds. Although this may be driven in part by young
people (particularly those under 18) being excluded from
previous studies (NASEM 2018), it may also be a result of
an increase in citizen science being used as an educational
tool in schools and universities in the UK in recent years
(e.g., through the OPen Air Laboratories [OPAL] [Davies
et al. 2016] and Polli:Nation [polli-nation.co.uk] projects).
For people identifying as from minority ethnic groups,
the highest rates of participation were amongst 16- to
24-year-olds, and the upturn in participation in over-35-
year-olds that is seen in those identifying as being from
white ethnic groups is not present. In other activities, such
as music, childhood experiences can facilitate a return
to participation after a break in early adulthood (Lamont
2011). It may be, therefore, that recent inclusion of citizen
science participation in formal education will eventually
lead to an upturn in participation in older people from
minority ethnic groups.
IMPLICATIONS
These findings have implications for the purported scientific
and societal benefits of citizen science. The exclusion of
marginalised groups is important because those who could
have the most to gain from volunteering are the least likely
to participate (Southby, South, and Bagnall 2019). For
example, there are known links between deprivation and
environmental quality, often with the most deprived areas
also having the poorest environmental quality (Fairburn,
Butler, and Smith 2009). Our finding, therefore, that those
from the lowest socio-economic groups are less likely
to participate in citizen science means that, in particular
for those projects where participants are encouraged to
collect data from their local areas, environmental quality
could be overestimated. People living in areas of poor
environmental quality and vulnerable to environmental
injustice are those most in need of information about their
local environment (Purcell, Garibay, and Dickinson 2012).
Their lack of participation in citizen science may mean
their local areas are invisible in environmental datasets
and thus not considered in prioritisation for action or
funding. Furthermore, these groups will not gain through
participation the tools, skills, and support needed to
campaign on issues relevant to them and to make sure
their voices are heard in decision-making (Purcell, Garibay,
and Dickinson 2012).
Clearly, as citizen science does not appear to be reaching
diverse participants, other potential outcomes of citizen
science, such as wellbeing benefits and the opportunity
to be part of a community, will not be spread equitably
in society. Some of the most frequently cited benefits of
citizen science are that participants will gain knowledge,
skills, and scientific literacy (Bonney et al. 2016), which in
turn could help their career development. Our results have
shown that the unemployed, who may benefit most from
this if they are seeking work, are underrepresented in citizen
science. In addition, women and people from socially
disadvantaged groups are underrepresented in science
careers in general (CaSE 2014), and people identifying as
from minority ethnic groups are severely underrepresented

11Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
in environmental science careers (The Royal Society 2014).
Underrepresentation of these groups in citizen science is a
missed opportunity to provide a path into scientific careers.
The experiences and perspectives of these groups will not be
included in the setting of research agendas, and the benefits
for creativity and innovation that come from bringing
together people with diverse backgrounds will be missed.
RECOMMENDATIONS
When designing projects, citizen science practitioners
should carefully consider both the scientific and societal
aims of a project, and the implications for these aims if
participants are not diverse. This will help to define target
participant groups, which can then be used to carefully
inform various aspects of project design. Approaches that
are successful for currently underrepresented groups will
differ from those that work for typical participants. As
Pandya explains,
“there is no research to suggest that some groups
of people are inherently less able to participate in
citizen science projects because of some perceived
deficit—cultural, social, educational, linguistic, or
otherwise. Rather … all participants need some
encouragement or scaffolding to participate in
citizen science regardless of demography or prior
experience.” (NASEM 2018, p. 45)
Examples and experiences exist that practitioners can
draw on. For example, which recruitment strategies are
used affect who hears about and is recruited to projects
(Brouwer and Hessels 2019). People from groups not
widely represented in the current pool of volunteers
may feel excluded, unwelcome, and like they don’t fit in
(Merenlender et al. 2016), or they may simply be unaware
of opportunities. Traditional approaches to recruitment
such as by word of mouth or through media channels are
likely to recruit people similar to those already engaged.
By contrast, targeted invitations to participate (Brouwer
and Hessels 2019) and engagement through third-party
organizations or through key individuals already embedded
in and trusted by communities have been shown to be
effective ways to reach underrepresented groups (Sorensen
et al. 2019).
Known barriers to participation can also be tackled in
project design. For example, to overcome time barriers,
projects can be designed to be modular so participants
can do what is possible for them alongside existing
commitments (Purcell, Garibay, and Dickinson 2012).
One-off activities can facilitate the inclusion of the time
poor (Everett and Geoghegan 2016), and activities can be
targeted at families or incorporated into community events
focusing on other topics of interest to local communities,
such as gardening or the arts, to widen participation
(Purcell, Garibay, and Dickinson 2012). Projects should be
open to people with a breadth of previous experiences. For
example, project designers should consider whether they
are excluding people without previous scientific experience,
and if particular skills are required, how these can be gained
within a project (e.g., Purcell, Garibay, and Dickinson 2012).
Project designers should also seek to recognise and to be
inclusive of the different skills and types of knowledge that
people with different backgrounds and experiences can
bring (Hermoso et al. 2021). Compensation for participants
should also be considered, especially when working in
resource-poor settings and where participation may take
time away from paid work (e.g., see West et al. 2020).
Co-design at every stage has been demonstrated to be
effective in designing projects that appeal to the needs and
motivations of previously underrepresented groups (NASEM
2018). There are a growing number of co-designed projects
to be learned from. For example, Pandya (2012) outlines a
five-step framework for co-designing projects, which was
successfully implemented in the Baltimore Mosquito Study
(Sorensen et al. 2019). The Celebrate Urban Birds project
(Purcell, Garibay, and Dickinson 2012) and Project Harvest
(Davis, Ramirez-Andreotta, and Buxner 2020) have also
demonstrated the successes that can come from taking
the time and effort to understand the needs, daily lives, and
potential barriers to participation of a target community,
and working with this community to co-design a project
with mutual goals and appropriate methods. However,
it should be noted that such approaches are resource
intensive and so the strategies selected will need to be
balanced with the other aims of a particular project.
It is also important to note that citizen science does not sit
in isolation in tackling these issues. A wealth of lessons can
be learned from experiences in related fields, including but
not limited to environmental volunteering (as summarised
by West and Pateman 2016), environmental justice (see
Sorensen et al. 2019), and science communication (e.g.,
Humm, Schrögel and Leßmöllmann 2020).
As well as the practical steps that project designers can
take to increase diversity of citizen science participants,
further research is also required. This includes better
documentation of the demographics of citizen science
participants by practitioners (Theobald et al. 2015; Burgess
et al. 2017), and reporting this in the literature (NASEM
2018). Our study is limited to environmental citizen science
in Great Britain, and work is needed to understand patterns
in other fields of citizen science and in other contexts. We
are also unable to examine in our study if these patterns
of participation hold for different project types (e.g.,
contributory, collaborative, and co-created) and different

12Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
tasks within these projects (e.g., research design, data
collection, and data analysis). Better documentation
of participants will also allow examination of if and how
patterns are changing over time (for example, if we
begin to see older people from minority ethnic groups
participating in citizen science because of exposure at a
young age). Further work is also required to understand the
relationship between ethnicity and participation. Although
we have demonstrated a broad pattern of participants
being dominated by those identifying as being from white
ethnic groups, our data suggest variation within broad
ethnic groups that warrants further attention. For example,
participation amongst those identifying as from “other”
white ethnic groups appears to be lower than those from
white British and white Irish groups, which could imply
underrepresentation of those from minority white ethnic
groups. Our data also suggest variation within Asian, Black,
and mixed groups, but our sample was not large enough to
test for these. Different participant characteristics should
also be included in further research. For example, disability is
known to affect particpation in environmental volunteering
(Ockenden 2008) and so could be an important factor in
citizen science participation that needs to be understood.
Our work has also considered only the diversity of
participants. Our questions did not reveal the quality of
participants’ experiences and so we cannot comment on
how effective or meaningful participants’ experiences of
citizen science were, and as a result, our ability to address
inclusivity or equity in citizen science is limited (NASEM
2018). There is some evidence to suggest that people
identifying as disabled or from minority ethnic communities
have a less positive experience of volunteering than other
volunteers (NCVO 2019). Quality of experience affects the
likelihood of repeated participation (NCVO 2019), so our
finding that marginalised groups are also less likely to
participate multiple times suggests they may also have
less rewarding experiences. Qualitative studies will be
critical to go beyond understanding who is participating
to understanding how the quality of participation differs
between groups.
Finally, identifying the underlying causes of
underrepresentation is crucial for understanding how
these can be overcome (Pandya 2012). Some of the
potential barriers to participation are outlined above, but
the relative importance of these different factors is unclear,
and there may be other barriers that have not yet been
identified. Further work is required to understand barriers
for particular groups in more detail (for example, women
from minority ethnic groups), and practitioners should seek
to gain this understanding from potential participants and
document the success and failure of different strategies
used to overcome these barriers.
CONCLUSIONS
We have offered some new insights into citizen science
participants; in particular, we have explored how different
participant characteristics interact to affect likelihood of
participation, and in line with previous studies, found that
those already marginalised in society are the least likely to
participate. There are scientific and societal implications
of a lack of diversity in citizen science participants, which
practitioners should consider, and we have offered some
guidance on how projects can be designed to widen
participation. Better documentation of who is participating
in projects, further research, and a sharing of best practices
around how to overcome barriers to participation are all
required to tackle the issue of underrepresentation in
citizen science.
DATA ACCESSIBILITY STATEMENTS
Data will be deposited with the UK Data Service.
SUPPLEMENTARY FILE
The Supplementary File for this article can be found as
follows:
• Supplemental File 1. Survey methodology. DOI: https://
doi.org/10.5334/cstp.369.s1
ETHICS AND CONSENT
This study gained approval by the University of York
Department of Environment and Geography’s Ethics
Committee.
ACKNOWLEDGEMENTS
Thanks go to Steve Cinderby and John Forrester for useful
discussions in the development of the paper and to two
anonymous reviewers of a previous version of the paper
who provided valuable feedback.
FUNDING INFORMATION
The national survey was commissioned by the UK’s
Department for Environment, Food and Rural affairs, project
number PH0475 “Data Submission in Citizen Science Projects.”

13Pateman et al. Citizen Science: Theory and Practice DOI: 10.5334/cstp.369
COMPETING INTERESTS
The authors have no competing interests to declare.
AUTHOR CONTRIBUTIONS
AD and SW designed the survey methodologies, drafted
sections of the manuscript, and critically revised other
sections; RP analysed the data and led the writing of
the manuscript. All authors gave final approval of the
submitted version and agree to be accountable for aspects
of the work they conducted.
AUTHOR AFFILIATIONS
Rachel Pateman orcid.org/0000-0002-2260-170X
Stockholm Environment Institute, Department of Environment
and Geography, University of York, UK
Alison Dyke orcid.org/0000-0003-2639-1620
Stockholm Environment Institute, Department of Environment
and Geography, University of York, UK
Sarah West orcid.org/0000-0002-2484-8124
Stockholm Environment Institute, Department of Environment
and Geography, University of York, UK
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