INTRODUCTION Since the Industrial Revolution

Since the Industrial Revolution, human activity has been the most significant

factor influencing the environment (Vitousek, Mooney, Lubchenco, & Melillo, 1997).

Human influence on the Earth system is so significant that scientists are calling this

geological age the Anthropocene, or the Age of Humans (Crutzen, 2006; Lewis & Maslin,

2015; Steffen, Grinevald, Crutzen, & McNeill, 2011). Global climate change,

deforestation, pollution, and threats to biodiversity are just a few major environmental

problems that have been driven primarily by human activity (Walther et al., 2002).

Knowledge of how humans influence the environment has given rise to an environmental

revolution pushing environmentally-friendly alternatives to previous ways of life.

Additionally, the public is generally aware of the issues facing the Earth today

(O’Connor, Bord, & Fisher, 1998).

However, there is plenty of progress that must still occur in order to adequately

slow the pace of environmental degradation. Some of that progress will be in the form of

scientific research monitoring the progression of environmental problems, some will be in

further technological advances, but most of that progress must come from a greater

understanding of human psychology and inspiring behavior change (Leviston, Leitch,

Greenhill, Leonard, & Walker, 2011; St. John, Edwards-Jones, & Jones, 2011; Stern,

1992; Swim, Stern, Doherty, Clayton, Reser, et al., 2011). Despite an awareness of the

issues facing the earth today, some people debate the extent to which those issues are

human-caused (Arbuckle, Morton, & Hobbs, 2015; Weber & Stern, 2011), and others

exhibit relatively low levels of pro-environmental behavior, despite their noted interest

and concern for the environment (Kollmus & Agyeman, 2002). Environmental efforts

will only be successful if a larger number of people support it personally, socially, and

politically. Thus, understanding why people do, or do not, engage in all types of

proenvironmental behavior is one of the greatest challenges of our time.

Researchers have been interested in understanding the root of pro-environmental

behavior for a long time, but there is no single factor or technique that increases all types

of pro-environmental behavior in all types of people. The assumption that people simply

must be educated about environmental problems in order to act on that knowledge has

been refuted and even when one cares deeply about environmental problems, they still

face barriers to action (Kollmuss & Ayeman, 2002). Decades of research from

environmental and behavioral psychology into this awareness-action gap shows that the

factors that lead to pro-environmental behavior are complex, multifaceted, and

inconsistent between individuals (Blake, 1999; Lane & Potter, 2007; Moser & Dilling,

2011; O’Brien, 2013; Taylor, 1989). Pro-environmental behavior can depend upon

environmental knowledge, values, attitudes, perceived locus of control, personal and

social norms, extrinsic motivation, age, sex, race, socioeconomic status, religion, and

geographic region (Clark, Kotchen, & Moore, 2003; Finger, 1994; Karp, 1996; Scannell

& Gifford, 2010; Steg & Vlek, 2009). Even when all the factors come together so that

one forms an intention to engage in pro-environmental actions, barriers such as access,

past behavior and habits, and social pressures can prevent pro-environmental behavior

(Gifford, 2011; Hargreaves, 2011; Kollmus & Agyeman, 2002). We know that behavior is

more likely to change when there is an emotional tie to the issue (Sia, Hungerford, &

Tomera, 1986), yet people are less likely to change their behavior if they are emotionally

overwhelmed by alarmist claims or feel that claims are being exaggerated (Whitemarsh,

2011). In an age where the public has access to an enormous amount of accurate

information at their fingertips, they are also faced with an onslaught of misleading claims

and politicized information in the media (Askanius & Uldam, 2011; Ladle, Jepson &

Whittaker, 2005). Through the internet, people have access to like-minded individuals

who can help them feel part of a pro-environmental culture (Brulle, 2014). But

simultaneously, those who oppose environmental policy or reject environmental science

can find others to support and reinforce their views, more than ever before (McCright &

Dunlap, 2011).

Despite these challenges, research has elucidated some factors that are

antecedents of pro-environmental behavior and potential areas of successful intervention.

Most recent meta-analyses and theoretical models include some measure of

environmental values or worldview as a significant determinant of pro-environmental

behavior (Xiao, Dunlap, & Hong, 2019). This has been measured using survey tools such

as the new environmental paradigm (Dunlap, Van Liere, Mertig, & Jones, 2000) and the

connectedness to nature scale (Mayer and Frantz, 2004). These scales measure the extent

to which one feels a part of nature, as opposed to an anthropocentric worldview in which

one feels that man dominates over nature. Not surprisingly, people who have an

environmental worldview are more likely to engage in pro-environmental behavior than

their anthropocentric counterparts (Dunlap et al., 2000). Similarly, studies show that

having frequent and meaningful outdoor experiences in early life can lead to more

positive attitudes and values toward the environment as an adult (Chawla, 1998; Ewert,

Place, & Sibthorp, 2005; Palmer, Suggate, Robottom, & Hart, 1999; Stevenson et al.,

2014). It is also clear that nature experiences are more impactful when supported by or

shared with family members, friends, and role models (Chawla, 1998). However,

instilling children with an environmental worldview and a feeling of connectedness to

nature is challenging during an age when people are more and more disconnected from

nature and live increasingly indoor lives (McCurdy, Winterbottom, Mehta, & Roberts,

2010). At the same time, access to natural places and nature-loving role models are not

universal across all groups of people (Strife & Downey, 2009).

Gaps in the Literature

The problem of increasing pro-environmental behavior requires complex

solutions that are tailored to the culture of specific groups of people. Yet much of the

previous research has overlooked or under-emphasized the impact of social and cultural

factors that can shape which antecedents of pro-environmental behavior and barriers to

pro-environmental behavior are most important for a particular group. For example, the

significant factors determining the pro-environmental behavior of an African American

female from an urban region and high socioeconomic status are probably not the same as

those of a white male from a rural region with low socioeconomic status.

Previous research has not taken the approach of framing the culture and

demographics of the population as central to the research, aiming for depth rather than

breadth. Additionally, much of the research on pro-environmental behavior has focused

more on the private-sphere and direct consumptive behavior of an individual versus

political and collective behavior that can greatly influence society and the culture of

environmentalism and stewardship (Hargreaves, 2011; Steg & Vlek, 2009; Stern, 2000).

The focus on these behaviors must shift if the movement is to succeed (Jugert,

Greenaway, Barth, Buchner, & Eisentraut, 2016; Lee, Kim, Kim, & Choi, 2014). The

field needs more research that studies the pro-environmental behavior of different groups

of people to learn what pro-environmental behavior looks like for them. This would allow

researchers to learn how to adapt efforts and interventions to overcome a group’s specific

barriers, especially those that can lead to collective action and cultural shifts.

One particular group of people that has been under-studied in the literature are

those from rural geographic areas (Larson, Stedman, Cooper, & Decker, 2015; Takahashi

& Selfa, 2015). Early studies and environmental education efforts focused on urban youth

due to the assumption that their physical disconnection from nature would make them

less likely to exhibit pro-environmental behavior (Tidball & Krasny, 2010). However, this

does not seem to be true; rural youth often exhibit similarly low pro-environmental

behavior, despite their physical proximity to natural spaces (Larson, et al., 2015). Some

studies show that rural students exhibit less direct pro-environmental behavior, have

lower environmental knowledge, and lower environmental literacy compared to students

from more urban schools (Chen et al., 2011; Williams, 2017). These findings could be

due to a difference in the availability of resources to behave pro-environmentally (Chen et

al., 2011), or a difference in values and worldviews (Huddart-Kennedy, Beckley,

Mcfarlane, & Nadeau 2009; Rauwald & Moore, 2002). In contrast, other studies have

found greater pro-environmental behavior and environmental sensitivity in rural students

due to greater time spent outdoors in nature (Gallay, Marckini-Polk, Schroeder, &

Flanagan 2016; Meyer, 2015), and a greater moral obligation to behave

proenvironmentally (Berenguer, Corraliza, & Martin, 2005). Numerous other studies see

no correlation between urban versus rural residence and pro-environmental behavior

(Arcury and Christianson, 1995; Halder et al., 2012; Lutz, Simpson-Housley & deMan,

1999). Clearly this is an area of the pro-environmental behavior research that requires

further exploration.

Another weakness in both research and practice is the lack of synergy between

environmental efforts and the recent emphasis on improving science, technology,

engineering, and math (STEM) literacy. Major STEM initiatives have successfully

garnered widespread support for educational programs aimed at improving the STEM

literacy of American teachers and students (DeJarnette, 2012; Jolly, 2009). It might seem

that improvements in science literacy would lead to improvements in environmental

literacy, but this would require a concerted effort to weave environmental issues into

STEM programming. Additionally, STEM initiatives tend to be valued based on their

contribution to industry and the economy, which is not always congruent with the goals

of the environmental movement (Atkinson & Mayo, 2010; Kennedy & Odell, 2014).

Despite this strange dichotomy between the ways we perceive “science” versus

“environment”, public views of environmental science are intrinsically tied to those of

science as a whole. As such, environmental science is likely plagued by many issues

facing the sciences in general (Chang, Eagan, Lin, & Hurtado, 2011; Hazari, Sadler, &

Sonnert, 2013). Research in the STEM realm has shown that science literacy and science

identity are generally low, especially in females, people of color, and those with low

socioeconomic status (Lee & Luykx, 2007; Miyake et al., 2010). At the same time, public

distrust of science is high (Gauchat, 2012). If science is not broadly accepted, accessible,

and relatable, people are not likely to trust or value what scientists have to say. This has

serious implications for environmental efforts, which require the public to know about

environmental issues and trust in scientists’ findings related to those issues, before they

can overcome the other barriers preventing pro-environmental behavior.

One promising area of research that comes from the STEM field is the concept of

science capital (Archer, Dawson, DeWitt, Seakins, & Wong, 2015), which was developed

in order to help researchers assess the cultural factors that influence the science

aspirations of young people. Derived from the concept of social capital, science capital is

the sum of the science-related experiences that one builds up over a lifetime that

influence certain social groups to participate in science, while others remain

underrepresented. Science capital includes what you know about science, who you know

that influences your views on science, your values and attitudes toward science, and your

engagement with science in daily life (enterprisingscience.com). This concept has not

been used to assess engagement in environmental science specifically, although its

emphasis on culture could help to explain the tremendous variation in pro-environmental

behavior research. The concept of environmental science capital is introduced in this

study to help explain why some individuals or groups behave pro-environmentally, while

others do not.

Significance of the Study

The concept of environmental science capital will help researchers to study

proenvironmental behavior in a more holistic way that considers the individual’s

sociocultural background and life experiences. In practice, formal and informal educators

will benefit from a deeper understanding of how to foster pro-environmental behavior in

rural youth, because they can present lessons or programs that are more effective with

that specific population. Therefore the findings of this study will benefit society by

building more pro-environmental citizens, especially among rural American populations.

This could result in greater social and political support for the pro-environmental

movement, benefitting the Earth and all its inhabitants.

Theory of Change

This study will use the Kollmus and Agyeman model of pro-environmental

behavior as the theoretical framework. Through the exploration of numerous theoretical

frameworks developed to explain the gap between the possession of environmental

knowledge and awareness and displaying pro-environmental behavior, Kollmus and

Agyeman (2002) analyzed the factors found to have some influence, positive or negative,

on pro-environmental behavior. These factors include: demographic factors, external

factors (institutional, economic, social and cultural) and internal factors (motivation,

proenvironmental knowledge, awareness, values, attitudes, emotions, locus of control,

responsibilities and priorities). Environmental knowledge, values, and attitudes together

with emotional involvement make up a complex Kollmus and Agyeman call

“proenvironmental consciousness”. This complex is embedded in the broader personal

values and is shaped by personality traits as well as other internal or external factors.

From this analysis, Kollmus and Agyeman developed a model (Figure 1.1) that

incorporates all of these factors to illuminate the complexity of what shapes pro-

environmental behavior (Kollmus & Agyeman, 2002).

The model indicates how the different factors influence each other and how they

ultimately influence pro-environmental behavior. Figure 1.1 illustrates that both internal

and external factors can directly lead to pro-environmental behavior (shown by two

narrow arrows), however, when both factors act synergistically (shown by one wider

arrow) there is a greater positive influence. The model also depicts several possible

barriers Kollmus and Agyeman found within their analysis as being the most important.

The possible barrier of old behavior patterns is illustrated graphically with the largest

barrier box to draw attention to old habits as a very strong barrier often overlooked in the

pro-environmental behavior literature (Kollmus & Agyeman, 2002).

The Kollmus and Agyeman model of pro-environmental behavior is the

framework for this research because it includes both the internal and external factors that

we believe must be considered when studying the pro-environmental behavior of a

specific population.

Figure 1.1. Model of Pro-Environmental Behavior (adapted from Kollmus and Agyeman,

2002).

Other recent theoretical frameworks (Bamberg & Moser, 2006; Klockner, 2015)

place less emphasis on the external factors such as political ideology, social norms,

economic situation, and culture. A framework that emphasizes culture is an ideal basis for

the present research. Although it does not encompass all the factors that could impact pro-

environmental behavior, the Kollmus and Agyeman model is more useful for conducting

research that is relevant to a specific demographic population, so that actionable

recommendations can be derived from the research to increase the proenvironmental

behavior of the specific population being studied. The model also references indirect

environmental actions and barriers to pro-environmental behavior, both of which are

addressed in the present research.

This study’s proposed theory of change attempts to build upon the model of

proenvironmental behavior by theorizing that environmental science capital is the

“missing middle” needed to overcome or address the barriers to pro-environmental

behavior. Environmental science capital is added to our version of the model, along with

two other variables that are not emphasized in science capital is added to the model

because the present research on science capital demonstrates the ability of science capital

to help underprivileged youth overcome barriers to aspirations and engagement in science

(Archer, DeWitt, Dillon, Willis, & Wong, 2012; Archer et al., 2015). The present study

will examine whether environmental science capital has a similar relationship with

proenvironmental behavior. The variables of meaningful nature experiences and role

models are added to the model because we hypothesize that these variables are potential

drivers of pro-environmental behaviors in rural youth (Chawla & Cushing, 2007;

Hungerford &

Volk, 1990).

This proposed theory of change also divides pro-environmental behavior into

individual actions that benefit the environment and collective actions that build

environmental culture, politics, or workforce. Kollmus and Agyeman’s model is the only

known prominent theoretical model of pro-environmental behavior that represents both

individual and collective forms of behavior and separates them on the model, although

they call them “indirect environmental actions” and present them as a side effect more

than a major outcome (Figure 1.1). Other frameworks either do not emphasize collective

pro-environmental behavior at all, or combine all forms of pro-environmental behavior

together (Bamberg & Moser, 2007; Hines, Hungerford, & Tomera, 1987; Klockner, 2015;

Stern, Dietz, Abel, Guagnano, & Kalof, 1999). Since we see individual and collective

pro-environmental behavior as equally important outcomes, and expect that they are

influenced by different factors, Kollmus and Agyeman’s model is applicable to

conducting research that values both forms.

Our theory of change (Figure 1.2) posits that internal and external factors build an

individual’s environmental science capital, giving them the tools to overcome barriers

preventing pro-environmental behavior. This study will examine environmental attitudes

using the connectedness to nature scale (CNS), a widely-used tool for measuring one’s

feeling of connectedness to nature (Mayer & Frantz, 2004). Environmental concern,

along with external factors such as race, ethnicity, gender, geographic region,

socioeconomic status, and political affiliation build the environmental science capital of

rural youth. Individuals with high environmental science capital have the means to

exhibit pro-environmental behavior, but may not necessarily do so because of many

barriers that exist in the gap between concern and action (Kollmus & Agyeman, 2002).

We hypothesize that meaningful nature experiences and the positive influence of family,

friends, and mentors will help rural youth with high environmental science capital to

“bridge the gap” and overcome barriers to pro-environmental behavior (Figure 1.2).

Purpose Statement

This study contributes to the pro-environmental behavior literature by introducing

the concept of environmental science capital and using it to explore the factors

influencing pro-environmental behavior in rural American youth. Using an explanatory

sequential mixed methods design, data were obtained from 252 surveys of youth aged 13

– 22 from Franklin County, Missouri, Berkley County, West Virginia, and the state of

Kansas. After the surveys, 35 youth were selected to participate in follow-up focus group

interviews to further explain survey results.

S Capital

STEM I

R M

E Behavior

Figure 1.2. Theory of Change, Proposed Pro-Environmental Behavior Model

incorporating Science Capital based on the Model of Pro-Environmental Behavior

(adapted from Kollmus and Agyeman, 2002).

In the first quantitative phase of the study, surveys assessed how environmental

science capital, interest in STEM and agriculture fields, meaningful nature experiences,

role models, connectedness to nature, and environmental identity influence

proenvironmental behavior. The results of those quantitative analyses informed the

structure of focus groups, which further explored the factors contributing to pro-

environmental behavior in participants.

Research Questions

The quantitative phase of this study used surveys to address the following questions:

Research Question 1. How does the concept of environmental science capital

(ESC) help to explain pro-environmental behavior (PEB) of rural youth in this

study?

Sub-question 1a: What is the relationship between environmental science

capital and pro-environmental behavior?

Sub-question 1b. Which aspects of environmental science capital best

predict pro-environmental behavior?

Research Question 2. How do the following factors influence PEB in our sample

populations?

• Meaningful Nature Experiences

• The Influence of Role Models

• Connectedness to Nature (CNS)

• Environmental Identity

• STEM Interest

Qualitative focus groups were conducted following analysis of the quantitative

survey results. Research questions for focus groups are as follows:

Research Question 3. How do described experiences of freshman and sophomore

college students enrolled in science courses help to explain patterns observed in

quantitative surveys?

Sub-question 3a: What personal success stories emerge from descriptions

of rural college students who have overcome barriers to

environmental science capital and pro-environmental behavior?

Sub-question 3b: What life experiences do students perceive as most

important in shaping their interests and environmental actions?

What is the importance of role models?

Sub-question 3c: What themes emerge in the lived experiences of students

with different courses, academic majors, or career plans? Research Question 4.

How do described experiences of high school STEM and environmental club

participants help to explain patterns observed in quantitative surveys?

Sub-question 4a: How do their lived experiences and descriptions explain

or contradict the findings from the quantitative study?

Accountability Statement

As with any successful group effort, the course of this research and writing has

been shared by all. How one untangles all the contributions made by the three-member

cohort is difficult to say the least. Each researcher helped develop the survey instrument

and was responsible for administering the survey to their distinct populations. Each

researcher uploaded their collected data into a shared spreadsheet and we were all

together when we ran most of the statistical tests. Qualitative data were collected by each

researcher from their respective focus groups. Coding took place as a group effort once

comments were uploaded into a shared Google Drive spreadsheet and we discussed codes

at our weekly meetings until we reached a consensus. Different chapters within the

document were spearheaded by different cohort members but the final product was a

combined effort from everyone.

Beyond the duties shared by all members, each researcher contributed in distinct

and unique ways. Michelle Donlan was able to carry out two focus groups to help offset

the smaller number of surveys she was able to obtain. Michelle has also undergone the

greatest review of the literature, developed the literature map, and is skilled at setting up

the starting phases of various chapters due to her vast knowledge on the subject matter,

especially in the realm of science capital. She was also instrumental in the development

of the focus group protocol. Writing for this document has been an ongoing effort but the

bulk of the formatting, layout, and editing for a single cohesive voice has been

undertaken by Elizabeth Flotte. Elizabeth also took the lead in the production of the

survey instrument and formatted, created spreadsheets, and generally took care of the

documents needed for this cohort to be successful. Elizabeth contributed the most content

to the pro-environmental behavior literature, especially as it relates to political views. Pat

Silovsky has undertaken extra quantitative and qualitative data analysis duties beyond

those conducted as a group. Pat has also contributed the most content to the literature

review regarding outdoor recreation and rural experiences. She kept the group on track,

particularly in the early stages, as she guided the development of this project by

introducing the group to some seminal research which laid the foundations for our

variables of interest.

CHAPTER 2: LITERATURE REVIEW

This study addresses the lack of pro-environmental behavior and unequal

distribution of environmental science capital. To explore the factors that contribute to

these phenomena in rural youth, a literature review was conducted. Search words

included: pro-environmental behavior, environmental sensitivity, responsible

environmental behavior, environmental identity, science identity, political identity, role

models, meaningful nature experiences, outdoor experiences, environmental literacy, and

science capital. ERIC, EBSCO, ProQuest, and Google Scholar databases were used.

Pro-Environmental Behavior

Pro-environmental behavior includes the actions that generate positive

environmental impacts, promote environmental quality, and result in sustainable use of

natural resources (Stern, 2000). This has been the ultimate goal of the environmental

education field since its inception. Early goals of environmental education were

developed according to recommendations from a meeting organized by the United

Nations Education, Scientific, and Cultural Organization (UNESCO) to discuss the role

of environmental education in addressing global environmental issues (UNESCO, 1977).

In their final report, UNESCO provided a list of recommendations, goals, objectives, and

guiding principles for environmental education (Tilbury, 1995; UNESCO, 1977).

The goals outlined in this report were:

to foster clear awareness of, and concern about, economic, social, political

and ecological interdependence in urban and rural areas; to provide every

person with opportunities to acquire the knowledge, values, attitudes,

commitment and skills needed to protect and improve the environment; to

create new patterns of behavior of individuals, groups and society as a

whole towards the environment (p. 26).

Prior to the UNESCO meeting, William Stapp (1969) stated that “Environmental

education is aimed at producing a citizenry that is knowledgeable concerning the

biophysical environment and its associated problems, aware of how to solve these

problems, and motivated to work toward their solution” (Stapp et al., 1969, p. 24).

Although some changes have been made over time, such as the shift toward a focus on

sustainability (Tilbury, 1995) and sustainable development (Hopwood, Mellor & O'Brien,

2005), the goals of environmental education are largely the same as they were in the

1960’s and 1970’s.

Throughout the history of environmental education research, pro-environmental

behavior has been a major theme, although it has had many names. This concept has been

named pro-environmental behavior (Bamberg and Moser, 2007; Klockner, 2015; Stern et

al., 1999), responsible environmental behavior (Hines et al., 1987),

environmentallyresponsible behavior (Kaplan, 2000), environmentally-friendly behavior

(Dolnicar &

Grun, 2009; Tindall, Davies, & Mauboules 2003), environmental sensitivity (Chawla,

1998), environmentally significant behavior (Stern, 2000), etc. It is also often studied as a

subcategory of the broader goal of environmental literacy or ecoliteracy (McBride,

Brewer, Berkowitz, & Borrie, 2013). Some studies consider only individual

proenvironmental behavior such as recycling, purchasing eco-friendly products, trying to

conserve water, and using public transportation, while others also address collective

proenvironmental behavior such as activism, engagement, and career aspirations that

contribute to the culture of environmental stewardship. It is important to study both types

of behavior because while individual action immediately benefits the environment,

collective actions, such as voting for a particular environmental policy, can affect change

on a very large scale – even at the level of industry or government. Since industry and

government account for two-thirds of the United States’ total energy consumption, the

most effective actions are collective, such as when people organize to pressure industry

and the government to act for the common good (Gardner & Stern, 2002).

Stern (2000) provides evidence that individual and collective pro-environmental

behavior are separate phenomena with their own sets of predictors. In one study, factor

analysis indicated that individual behavior, collective environmental citizenship behavior,

and environmental policy support were statistically distinct and were correlated with

different personal, social, and cultural attributes (Dietz, Stern, & Guagnano, 1998) and

other studies have agreed that all types of pro-environmental behavior cannot be reliably

combined into one concept (Stern, 2000). Yet these variables are not always separated in

the literature, and often collective behavior are not measured at all. Thus, our review of

the pro-environmental behavior literature provided below will include studies that

distinguish between types of pro-environmental behavior, studies that combine them

together, and studies that focus solely on individual behavior.

The determinants of pro-environmental behavior are so complex and multifaceted

that statistical modeling and meta-analyses are useful to obtain a complete understanding

of how one’s life experiences, personality, social factors, and demographics may

influence pro-environmental behavior. Hines, Hungerford, and Tomera (1987) conducted

one of the first major meta-analyses of pro-environmental behavior by reviewing the

existing research and determining not only what variables were predictors of

proenvironmental behavior, but also the strength of those relationships. They found four

major determinants of pro-environmental behavior – attitudes toward the environment,

locus of control, personal norms, and intention to act pro-environmentally. Recent

reviews of the literature indicate that age, sex, race, geographic location, socioeconomic

status, knowledge, cultural norms, and extrinsic motivation can also be important factors

(Gifford & Nilsson, 2014; Stern, 2000). In much of the recent research on

proenvironmental behavior, the factors that influence an individual’s propensity to engage

in individual and/or collective pro-environmental behavior are separated into internal and

external categories (Gifford & Nilsson, 2014; Kollmus & Agyeman, 2002). The internal

and external factors that are most consistently correlated with pro-environmental

behavior are reviewed first. Examples of research specific to rural youth will be provided

when available, since this is the demographic focus of the present study.

Within the category of internal factors, knowledge of environmental problems and

solutions is considered a prerequisite for pro-environmental behavior (Frick, Kaiser, &

Wilson, 2004). This is concerning because research of the past several decades has

indicated that students, specifically, and Americans, in general, lack knowledge regarding

environmental issues (Blum, 1987; Bodzin et. al, 2014; Gambro & Switzky, 1999). In a

study of twelfth grade students, for example, "although environmental knowledge

increased fairly steadily with parental levels of education, the level of knowledge, even at

the highest level of education, remained disappointingly low" and although taking more

science classes generally resulted in higher environmental knowledge over time, students

were only taking an average of two science classes in high school (Gambro & Switzky,

1999). As a result of these deficits, it is clear that environmental knowledge, also referred

to as environmental literacy and/or ecological literacy, is low and should be improved.

However, the assumption that knowledge leads to values and then to pro-environmental

behavior has been very clearly disproven (Stern, 2000), so knowledge is included in

proenvironmental behavior research but is rarely the focus of recent studies.

Pro-environmental values and beliefs are also considered a prerequisite for

proenvironmental behavior, and this relationship is stronger than that of knowledge alone.

Values, beliefs, and attitudes have been studied in the form of environmental concern,

environmental sensitivity, environmental worldview, post-materialistic values, nature

affinity, etc., and have been shown to significantly correlate with pro-environmental

behavior (Dietz et al., 1998; Dunlap et al., 1992; Schultz, 2001). Contemporary studies of

pro-environmental behavior include some sort of measurement of environmental values,

worldview, concern, etc. The new environmental/ecological paradigm (NEP) is the most

widely-used scale for measuring environmental values. This scale measures the extent to

which one holds a pro-environmental worldview, feels that humans are deeply connected

to the natural environment, and believes human activities can have dire environmental

consequences (Dunlap & Van Liere, 1978; Dunlap et al., 1992). Even holding an

environmental worldview does not necessarily result in pro-environmental behavior, so

pro-environmental behavior research began to incorporate psychological behavior theory

to help understand the gap between caring about the environment and engaging in

proenvironmental behavior. This literature shows that although the NEP does not always

directly predict pro-environmental behavior (Scott & Willits, 1994) it is a reliable

measure of environmental worldview, a key variable in theoretical models that predict

pro-environmental behavior (Klockner, 2015; Stern et al., 1999). Mayer and Frantz

(2004) believe the NEP is not an adequate measure of one’s affective, experiential

relationship to the natural world, for two reasons. First, it measures cognitive beliefs

rather than affective experience. And secondly, it measures beliefs about humans in the

aggregate, not the individual’s personal relationship to nature (Mayer & Frantz, 2004). A

commonly used alternative is the connectedness to nature scale, which will be discussed

more below.

One example of the application of behavioral psychology to pro-environmental

behavior research is the value-belief-norm (VBN) theory of environmentalism (Stern et

al., 1999). The VBN was developed based on a review of studies explaining how

environmental values lead to behavior. This theory states that individuals who have high

biospheric and altruistic values and low egoistic values are more likely to have an

environmental worldview, which leads to the development of pro-environmental personal

norms. According to this theory, values and beliefs do not translate into proenvironmental

behavior until personal norms are established. Even if one is aware of the problems

facing the environment, holds environmental worldviews, and knows how to help protect

the environment, they may not display pro-environmental behavior if they do not feel

obligated to do so due to personal norms, habits or routines.

In addition to knowledge, values, and norms, locus of control is a significant

factor influencing pro-environmental behavior (Hines et al., 1987; Stern et al., 1999).

This is similar to the concept of self-efficacy, the belief that one has the ability to help the

environment if they try, and thus have some control over what happens to the environment.

Individuals who lack these traits are more likely to feel overwhelmed or helpless in the face

of environmental challenges and are thus less likely to exhibit proenvironmental behavior

(Stern et al., 1999). Bamberg and Moser (2007) refer to a similar concept of perceived

behavioral control in their meta-analysis of pro-environmental behavior research that builds

upon the work of Hines, Hungerford, and Tomera by incorporating psychological action

theory. Their work takes a more holistic look at proenvironmental behavior and applies

what psychologists know about behavior in general. The concept of perceived behavioral

control comes from psychological action theories such as the theory of planned behavior

(Azjen, 1991) and the norm-activation theory

(Schwartz & Howard, 1981). It describes one’s belief in their ability to perform the

behavior in question and also considers the difficulty or inconvenience of the behavior.

Bamberg and Moser found that pro-environmental attitudes, personal norms, and

perceived behavioral control were relatively equal predictors of intention to act

proenvironmentally (Bamberg & Moser, 2007). Thus, individuals consider the difficulty

of the behavior just as much as they consider their attitudes toward the behavior and their

personal moral obligation to perform the behavior. Perceived behavioral control has

continued to be an important variable in pro-environmental behavior research that is

conducted from an environmental psychology lens (Klockner, 2015; de Leeuw, Valois,

Ajzen, & Schmidt, 2015).

Klockner (2015) combined all of the relevant environmental psychology

proenvironmental behavior theories into one model through a meta-analysis of 56 data

sets. This model is called the comprehensive action determination model (CADM), as it is

meant to encompass all previous theories so that more robust conclusions can be drawn

and the model can be used in a variety of situations. Based on his analysis, Klockner

concluded that habits should be part of the model due to their direct influence on

environmental behavior. The intention to act pro-environmentally is formed by attitudes,

personal norms, social norms, and perceived behavioral control (Klockner, 2015). Thus,

the most powerful interventions to increase pro-environmental behavior would likely

focus on breaking old habits, improving social support for the behavior so that it becomes

a social and personal norm, and increasing perceived behavioral control by educating

people on how to act pro-environmentally and reducing the barriers to those actions.

Interventions that build pro-environmental values influence pro-environmental behavior

indirectly, through their effect on personal norms.

The meta-analyses described above are useful when measuring pro-environmental

behavior because they identify potential predictors of and barriers to pro-environmental

behavior that can be tested broadly in any population. This helps to clarify which

variables are generally most important. However, demographics and external factors such

as cultural, social, and economic situations are not represented in these models, unless

indirectly through their relationship with norms. Yet these are crucial factors to consider

when making suggestions for particular interventions or action strategies. If the goal of a

study is to determine how to address the problem of low pro-environmental behavior, the

demographics and external factors of the study population must guide the research.

Gifford and Nilsson (2014) reviewed the demographic and external factors influencing

pro-environmental behavior and found that age, gender, religion, political views, urban

versus rural residence, proximity to environmental problem sites, social class, culture and

ethnicity build the personal and social norms upon which pro-environmental behavior are

built (Gifford & Nilsson, 2014).

Age comparisons show that older people have more knowledge and ability to

exhibit pro-environmental behavior (Gifford, 1982), while younger people are more

concerned about environmental problems (Klineberg, McKeever, & Rothenbach, 1998).

Gender comparisons show that females have more positive environmental attitudes and

values (Meyer, 2015; Uitto & Saloranta, 2010; Zelezny, Chua, & Aldrich, 2000), despite

the fact that males often have greater environmental knowledge. There is conflicting

evidence as to whether these attitude and knowledge differences result in actual

behavioral differences (Gifford & Nilsson, 2014). The research on the effect of religion is

highly varied and seems to imply that religion influences how environmental concern is

manifested in pro-environmental individuals, but does not predict the presence or absence

of pro-environmental behavior (Gifford & Nilsson, 2014). Higher social class is generally

correlated with greater pro-environmental behavior, at least in developed countries

(Balderjahn, 1988; Laidley, 2011, Inglehart, 1997), except in the case of poorer citizens

who are particularly concerned about local environmental problems that directly affect

their communities (Brechin, 1999).

Pro-environmental Behavior and Rural Populations

The present study is particularly interested in the external factor of rural

residence. There are conflicting results regarding whether rural residence influences

proenvironmental behavior, despite the obvious difference in how rural and urban people

experience the natural world. Some studies indicate that people living in larger cities are

more likely to engage in pro-environmental behavior (Chen et al., 2011) due to the fact

that many types of pro-environmental behavior, such as recycling and using public

transportation, are more available in urban settings. Williams (2017) found that rural

students exhibit less pro-environmental behavior, lower knowledge, and overall lower

environmental literacy scores than students from urban and suburban schools (Williams,

2017). In contrast, Hinds and Sparks (2008) and Meyer (2015) report greater

proenvironmental attitude and behavior in rural students, compared to urban students.

This is supported by a study comparing rural and urban students in Michigan which

confirmed that rural students have higher baseline environmental sensitivity due to

greater time spent outdoors engaged in “rural” activities such as hunting, fishing, and

camping (Gallay et al., 2016). Berenguer, Corraliza, and Martín (2005) found moral

obligation and the level of pro-environmental behavior to be higher among rural than

urban residents, but the opposite was found for environmental concern. Not only do rural

and urban residents exhibit different levels of environmental concern and behavior, but

the type of concern also differs. Rural residents tend to be more anthropocentric and wish

to protect the environment so that it can better fulfill human needs, while urban residents

are more likely to display ecocentric values (Bjerke & Kaltenborn, 1999; Huddart-

Kennedy et al., 2009; Rauwald & Moore, 2002). Still, numerous studies have observed no

difference between the pro-environmental behavior of rural and urban residents (Arcury

Christianson, 1990; Halder et al., 2012; Lutz, Simpson-Housley & deMan, 1999).

Rural residence is closely tied to political values and other cultural factors that can

influence pro-environmental behavior. Studies have shown that political

conservativeness results in less environmental concern (Dunlap, Xiao, & McCright,

2001; MCright & Dunlap, 2011), although the degree to which this difference exists

depends on the framing of pro-environmental statements (Feinberg & Willer, 2013).

Other cultural factors such as race, ethnic group, and immigrant status yielded highly

variable and conflicting results (Gifford & Nilsson, 2014). One meta-analysis that places

particular emphasis on demographic, external, and social factors is that of Kollmus and

Agyeman (2002). In their review of pro-environmental behavior literature, they

developed a model of pro-environmental behavior that displays internal and external

factors as separate categories that can influence each other and have a synergistic effect

on pro-environmental behavior when both are strong (Figure 1.1). Of all the reviewed

studies and meta-analyses described here, the study Kollmus and Agyeman may be the

most relevant for research that aims to result in actionable strategies that will work for a

specific population, due to the emphasis on external factors and demographics.

Gifford and Nilsson (2014) concluded that:

A person with a particular personal and social profile will be more likely to

be concerned about the environment and to act on its behalf… such

persons are likely to have spent time in nature as a child, to have accurate

knowledge of the environment, its problems and potential solutions, to

have an open, agreeable, and conscientious personality, to consider the

future consequences of their actions, to feel in control of their behaviors,

to harbor biospheric, post-material, liberal values and responsibility for

environmental problems, to be among the upper half of the economic

classes, to hold personal and descriptive norms about pro-environmental

action, to adhere to a religion that teaches a stewardship orientation to the

earth, and to spend time in non-consumptive nature activities (p. 151).

The above quote highlights some experiences and interventions that can alter

one’s path toward pro-environmental behavior. Specifically, Gifford and Nilsson mention

spending time in nature as a child and participating in non-consumptive nature activities

throughout life. The present study will refer to these as meaningful nature experiences.

Further, studies show that meaningful nature experiences and the influence of role

models are the most self-reported factors in pro-environmental research. Given their

importance, this research will focus on these two influential factors.

Connectedness to Nature Scale

Currently there are at least nine published assessment tools that measure

connectedness to nature. Tam (2013) did empirical research to compare seven of these

scales. His results showed "strong convergent validity and little incremental validity" so

he concluded these scales can be considered "markers of the same underlying construct."

Therefore, however connectedness to nature is measured, the research is showing that a

reliable relationship exists between connectedness to nature and self-reported

proenvironmental behavior. (Brugger et al., 2011; Clayton, 2003; Davis, Le, & Coy, 2011;

Dutcher, Finley, Luloff, & Johnson, 2007; Nisbet, Zelenski, & Murphy, 2009).

One of the most widely used measures is Mayer and Frantz's (2004)

connectedness to nature scale (Brugger et al., 2011; Cervinka et al 2012; Corralize &

Bethelmy 2011; Olivos & Aragones 2011; Zhang et al., 2014). This scale has been used

around the world and translated into several languages (Navarro, Olivos & Fleury-Bahi

2017; Pasca, Aragones & Coello, 2017). According to Mayer and Frantz (2004), the CNS

was developed based on the views of Aldo Leopold, particularly his “contention that

people need to feel they are a part of the broader natural world if they are to effectively

address environmental issues”. The CNS-R (Frantz, Mayer, & Sallee, 2013) is a shorter

version suitable for use with children and low-income adult samples. The CNS-R has also

been shown to predict self-reported pro-environmental behavior across multiple

populations, including college students, children, and a general adult population (Frantz,

Mayer, Gordon, & Handley, 2010; Frantz, Mayer, & Sallee, 2013; Gordon, Frantz, &

Mayer, 2012).

Pasca, Aragones and Coello (2017) did an analysis of the CNS using item

response theory and found seven items presented appropriate indices of discrimination

and difficulty, in addition to a good fit. The remaining items of the scale were redundant

or didn't discriminate well between individuals with different levels of connectedness. By

reducing the scale to seven items, they demonstrated a slightly higher reliability than

Mayer and Frantz (2004) obtained in their original 14-item scale. The new 7-item scale is

more “reliable, easier to administer, and correctly measures connectedness insofar as the

scores obtained actually discriminate between individuals who are connected and those

who are not” (Mayer & Franz, 2004). These researchers even went so far as to say that

item 11 ("Like a tree can be part of a forest, I feel embedded within the broader natural

world") would be the best option in the event that it is necessary to measure

connectedness with a single item.

Perrin and Benassi (2009) argue that the CNS scale measures cognitive beliefs

and not emotional connections. Their confirmatory factor analysis showed that

participants responded similarly to the items with the word feel and the items that used

cognition words. Based upon content analysis of scale items that include no emotional

component (e.g., I think., I consider., I imagine.) and their suggestion of a cognitive

interpretation of the word feel, Perrin and Benassi concluded that the one-factor CNS taps

into is a cognitive connection to nature, not an emotional connection to nature.

Meaningful Nature Experiences

In their review of prior research on pro-environmental behavior, Chawla and

Cushing (2007) found that half to more than 80% of the respondents identify childhood

experiences of nature as a significant experience. Childhood experiences includes a

variety of activities such as free play, hiking, camping, fishing and berry picking.

Respondents also mentioned influential family members or other role models equally

often or second in importance which is in agreement with other research and will be

discussed more below. Other common answers are experiences in organizations like the

scouts or environmental groups, witnessing the destruction or pollution of a valued place,

and reading books about nature and the environment (Chawla and Cushing, 2007).

Although this research has been criticized because it looks backwards to distant

childhood experiences rather than focusing on contemporary conditions for young people

(Scott, 1999) no conflicting evidence has been presented. Chawla and Cushing (2007)

further argue that the fact that similar formative experiences are identified by descriptive

qualitative studies and large correlational surveys, in a variety of cultures, by secondary

school students as well as older populations, gives these findings weight. (Chawla &

Cushing, 2007)

Similarly, a study with Wisconsin High School students found that the outdoors

and environmental influences were major influences on respondents’ level of

environmental sensitivity, an important precursor to both environmental literacy and

environmentally responsible behavior (Sivek, 2002). During the study’s focus group, the

most frequently mentioned subcategories to emerge under environmental influences were

accessibility to or frequency of visits to outdoor areas and opportunities for in-depth

learning and/or involvement. Negative experiences (such as the Exxon Valdez oil spill

and loss of a cherished natural area) were also mentioned as important influences,

whereas, media, in general, appeared to have relatively low influence. Results from the

study’s paper and pencil survey found several strong influences including having ready

access to the outdoors (96%), spending time outdoors (95%), seeing bad things happen to

the environment (78%) and spending time outdoors alone or with a few friends (67%).

Moderate influences included work or volunteer experiences with animals, having wild

animals as pets and books and other print media (Sivek, 2002).

Outdoor recreation, an example of a meaningful nature experience, is often cited

as the most influential activity that contributes to an individual's environmental activism

with hunting and fishing being mentioned by more than a third of the respondents. Tanner

(1980) investigated the backgrounds of members of environmental organizations to try

and find antecedents to environmental activism. When he found recurring accounts of

"childhood hours spent outdoors" he formed the hypothesis that these may be critical

experiences for environmental activism and sent open-ended surveys to staff of several

prominent environmental organizations. When people explained the sources of their

environmental career choice, activism, or environmental concern or interest, similar

answers recurred: positive experiences in natural areas, adult role models, environmental

organizations, education, negative experiences of environmental degradation, books and

other media, and on-the-job experience. Peterson (1982) surveyed environmental

educators and found similar results. 77% of Peterson's samples were males and 45% of

these mentioned hunting and fishing as their outdoor activity.

Current research on wildlife recreation and pro-environmental behavior within

rural New York residents found that wildlife recreationists (hunters and birdwatchers)

were four to five times more likely than non-recreationists to engage in conservation

behaviors (Cooper, Larson, Dayer, Stedman, & Decker, 2015). These behaviors include

donating to support local conservation efforts, enhancing wildlife habitat on public lands,

advocating for wildlife recreation and participating in local environmental groups. They

also found that there was an additive effect with hunter-birdwatchers as having the

greatest likelihood of engaging in all types of conservation behavior. Although wildlife

recreationists were more likely to engage in conservation behavior, the study also found

that engagement in environmental lifestyle behavior (recycling, energy conservation and

green purchasing) were roughly comparable among all types of wildlife recreationists and

non-recreationists (Cooper et al., 2015).

Influence of Role Models

Research findings on meaningful nature experiences and role models suggest that

both childhood experiences in nature and the examples of parents, teachers and other role

models are key "entry-level variables" for responsible environmental behavior. (Chawla

& Cushing, 2007). An example of an entry-level variable could be membership in an

environmental club or organization. Being involved in these clubs or organizations allows

youth to gain increased knowledge about environmental issues and learn environmental

action skills – the skills referred to as “ownership” and “empowerment” variables

(Chawla & Cushing, 2007). Chawla and Cushing highlight several characteristics of

effective programs: an extended duration of time, opportunities to learn and practice

action skills, and success in achieving some valued goal where their efforts are taken

seriously by others (Chawla & Cushing, 2007).

Sivek (2002) assessed the influences on environmental sensitivity in Wisconsin

high school students and found that while time spent outdoors was the most frequently

mentioned influence on environmental sensitivity, role models was the second (Sivek,

2002). The qualitative phase of this study found that students’ responses about role

models fell into five subcategories: teacher or their environmental club advisor, parents,

relatives, friends and others (such as actors or politicians). The greatest number of

students reported teacher or their environmental club advisor. When asked about their

role model’s traits, student responses fell into four subcategories: knowledge,

openminded, action/involved in environmental matters and friendly/personable. The most

frequently mentioned trait was friendly/personable (Sivek, 2002). The quantitative phase

of this study found that male teachers accounted for 44% of role models while parents

and other relatives accounted for 42% of role models ranked as most important. Only

13.7% ranked unrelated people other than male teachers as most important role model

influence (Sivek, 2002). The present study uses Sivek’s survey instrument of role models

and role model traits.

In terms of influence of family and friends, recent research indicates that positive

parental attitudes and support contribute to the concept of science capital. Therefore,

science-related experiences and activities contribute to this concept of science capital,

especially when they are experienced with significant family members. Further, the

results from ASPIRES, a prominent longitudinal study that explored the development of

children’s science attitudes and aspirations, found that parental attitudes to science play

an important role in shaping children’s science aspirations. In fact, Archer and her

colleagues found that parental attitudes to science, experiences of school science, and

student self-concept in science were the variables that had the strongest relationship with

students’ aspirations (Archer et al., 2012). Students with “high science capital” tend to do

science-related activities in their spare time and have family/friends (particularly parents)

who work in science-related jobs (Archer et al., 2015). Thus, the influence of family,

friends, mentors and roles models can help build self-efficacy, create positive shared

outdoor experiences and assist with capital perhaps including environmental science

capital. It is not known whether similar shared experiences in nature contribute to higher

environmental science capital specifically. However, it is known that shared experiences

in nature with family, friends, and mentors have a positive influence on

proenvironmentalism (Chawla & Cushing, 2007).

Meaningful Nature Experiences and Role Models in Rural Youth

For rural students, one positive shared outdoor experience often viewed as the

archetypical rural activity is hunting. Hunting game for recreation is an image often

associated with visions of the family farm and the stereotypical rural way of life. Rural

upbringings can foster an increase in hunting, especially for males (Stedman & Heberlein,

2001). In one study, wildlife recreationists – both hunters and birdwatchers – were four to

five times more likely than non-recreationists to engage in conservation efforts and those

that participated in both had the greatest likelihood of engaging in all types of

conservation behaviors (Cooper et al., 2015). Thus, hunting may be a meaningful nature

experience that is particularly important in forming the pro-environmental behaviors of

rural youth, especially when it is shared with friends, family, or mentors.

Johnson, Bowker, and Cordell (2001) explored outdoor recreation constraints of

race, gender and rural dwelling. Some potential factors that may lead to constraints for

rural populations include lower tax revenues and incomes and restricted access to hunting

and fishing areas. A series of logistic regressions from a national recreation survey was

used to model the probability that individuals perceive certain constraints to participating

in outdoor recreation activities. Results from the survey found that rural residence does

not appear to be an important factor among participants and non-participants in outdoor

recreation constraints. The only constraint shown significance in rural populations is “not

enough time” equation (Johnson et al., 2001).

Science Capital

A recent conceptual tool for understanding the production of classed patterns in

the formation and production of children’s science aspirations is science capital (Archer

et al., 2015). Science capital is the sum of all the science-related knowledge, attitudes,

experiences and resources that an individual builds up through their life. This includes

what science they know about, what they think about science, the people they know who

have an understanding of science, and the day to day engagement with science (House of

Commons Science and Technology Committee, n.d.). Archer and her colleagues advocate

the extension of the Bourdieusian notion of capital beyond the arts by including science

capital. This is not without criticism. While Jensen and colleagues praise the work being

done to address social inequality in science education, they argue that adding “science

capital” to Bourdieu’s existing range of concepts is unnecessary. They go on by saying

that there is just as good an argument for “sports capital,” “numeracy capital, “and many

other domain-specific “capitals” as for “science capital.” Their main concern is by

introducing “science capital”, it may undermine a focus on the ways in which inequalities

and injustice in science education are coterminous with other forms of systemic

inequality (Jensen & Wright, 2015). Despite this criticism, we use science capital in this

present study as a framework in looking at the uneven distribution of science aspirations.

Archer and her colleagues coined the term science capital during Kings College’s

ASPIRES project, a five year longitudinal study (conducted between 2009 – 2013)

exploring science aspirations and engagement among 10 to 14 year olds – a critical age

period for forming science aspirations. The ASPIRES project was funded by the UK’s

Economic and Social Research Council (ESRC) as part of its Targeted Initiative on

Science and Mathematics Education. For this study, Archer and her colleagues used the

Bourdieusian conceptual framework to study how the interplay of family habitus and

capital can make science more “thinkable” for some children (white, middle class) than

others. They then use family habitus rather than family identity or family context to

“better encompass a broad spectrum of family resources, practices, values, cultural

discourses, and “identifications” of “who we are” (Archer et al., 2012). Archer further

explains that it provides a lens for attempting to situate and contextualize individual child

and parent identities (and orientations to science) within the family environment – for

examining the extent to which the everyday family landscape shapes, constrains, or

facilitates aspirations and engagement in science through the combination of attitudes,

values, practices, and ways of being that they engage in (Archer et al., 2012).

The results of this study examining how “thinkable/natural” or

“unthinkable/unusual” science aspirations and engagement within students aged 10 -14

were:

• Analyses highlighted the importance of social class in facilitating or

constraining children’s potential science aspirations and identifications,

even though the overwhelming majority of children in the sample

reported liking science.

• Middle-class family habitus, capital, and a child’s identification with

science were in alignment in favor of science. The result was particularly

powerful, with families able to foster and capitalize on their child’s

interest, enabling them to occupy a strong and privileged position from

which to potentially pursue these aspirations further.

• Within most working class families, science was less “familiar”, being

more “peripheral” to parents’ and children’s everyday lives.

• Despite these clearly classed patterns, our analyses also highlighted the

nondeterministic nature of habitus, with examples of children “going

against the grain” and of home expectations. This agency worked both

ways, with some children resisting a strong science “steer” from home

and others proactively choosing science despite little awareness or

science resources at home.

Ultimately, they found that most young people, from primary through secondary,

find school science interesting. However, interest in science does not translate into post16

participation and careers – with only 15% of 10-14 year olds interested in becoming a

scientist (King’s College London, n.d.).

As part of this larger ASPIRES project, Archer pulled survey and longitudinal

interview data from Black African/Caribbean students and their parents to examine why

science careers are less thinkable for Black students. Additionally, they presented a case

study of two young Black women who bucked the trend and aspired to science careers.

Results from this study suggests although the “being/doing” (liking science, but seeing

science careers as not for me) is common across all students, it is particularly problematic

and exacerbated in the case of Black students (Archer, Dawson, et al., 2015). Archer and

her colleagues suggest three implications for science education based from their analysis:

1. There is an urgent need to find ways to break the pervasive science =

scientist link.

2. Challenges need to be made to the popular association between science and

“braininess.”

3. There is a need for a better and fairer (re)distribution of all forms of

capital, including science capital, across society.

Archer and her colleagues further went on to conceptualize science capital and

explained how they translated this into a survey tool with the “wish to help science

educators and delivery organizations to be able to delineate what they are seeking to

change through their practice and why and to assess to what extent they have been

successful, or not, in these efforts” (Archer et al., 2015). Using logistic regression, 14

questions (12 individual items plus two larger questions) were identified as the strongest

predictors of whether a student would fall into the high or low group on the outcome

variable of future science affinity plus recognition. The twelve individual items are:

• A science qualification can help you get many different jobs?

• When you are NOT in school, how often do you talk about science with other

people?

• One or both of my parents think science is very interesting.

• One or both of my parents have explained to me that science is useful

for my future.

• I know how to use scientific evidence to make an argument.

• When not in school, how often do you read books or magazines about

science?

• When not in school, how often do you go to a science center, science museum

or planetarium?

• When not in school, how often do you visit a zoo or aquarium?

• How often do you go to after school science club?

• My teachers have specifically encouraged me to continue with science

after GCSEs.

• My teachers have explained to me science is useful for my future.

• It is useful to know about science in my daily life.

The two larger questions concerned who students speak with about science and

who they know who has a job using science (Archer et al., 2015).

This research study uses this recent concept of science capital because it explores

social inequities in the distribution of capital and how it impacts engagement and

aspirations within science. It particularly looks at those from more socially disadvantaged

backgrounds and how it can increase their access to science related knowledge, resources,

and social capital (Archer et al., 2012).

Environmental Science Capital of Rural Youth

Within this study, the focus is on rural populations and an expanded definition of

science capital to include environmental science capital – meaning sciences within the

environment such as biology, ecology, agriculture, animal science, environmental science

and natural resource management. By expanding science capital to include the

environmental sciences, hopefully this study will characterize and define environmental

science capital of rural youth.

CHAPTER 3: METHODOLOGY

This study used a mixed methods design that incorporated both quantitative and

qualitative approaches. Quantitative approaches examine relationships among variables

whereas qualitative approaches explore meaning and understanding individuals or groups

ascribe. The rationale for using a mixed methods design is that this form of inquiry

provides a more complete understanding of a research problem than either approach alone

(Creswell, 2014). Thus, this technique allows researchers the potential of answering both

“how” and “how much” questions (Brinkmann & Kvale, 2015). This study used an

explanatory sequential mixed methods design. This design involves a two-phase project

in which the researchers collect quantitative data in the first phase, analyze the results,

and then use the results to plan (or build onto) the second, qualitative phase (Creswell,

2014). The quantitative phase is the emphasis of this approach, with the qualitative phase

providing a supporting role. The qualitative methods seek to add depth and meaning to

the quantitative results. Figure 3.1 is a visual model of the explanatory sequential mixed

method design used in this study.

The challenges of using mixed methods designs in general are the need for

extensive data collection, the time-intensive nature of analyzing both quantitative and

qualitative data and the requirement for the researcher(s) to be familiar with both forms of

research (Creswell, 2014).

Figure 3.1. The Visual Model of the Explanatory Sequential Mixed Method Design

(Creswell, 2014).

The challenge of using an explanatory sequential mixed methods design is to plan

adequately what quantitative results to follow up on and what participants to gather

qualitative data from in the second phase (Creswell, 2014). To address this challenge,

Creswell suggests looking at extreme or outlier cases, significant predictors, and

significant results relating variables, insignificant results, or even demographics in the

quantitative results to build the second qualitative phase. For example, we found identity

to have a significant relationship with pro-environmental behavior, therefore, we focused

on identity during our qualitative focus groups. Another challenge of the explanatory

mixed methods approach is not considering and weighing all the options for following up

on the quantitative results such as focusing on personal demographics and overlooking

important explanations (Creswell, 2014). Creswell suggests drawing on the same sample

for both phases of the study in order to prevent minimizing the importance of one phase

building on the other (Creswell, 2014). In this study, we used a subset of the same sample

for the qualitative phase of the study as was used in the quantitative phase.

As with any study design, validity and reliability must be addressed. Since this is

a mixed methods design, both quantitative and qualitative data must be checked for

validity and reliability. There are additional concerns with the explanatory mixed methods

D Collection

(

QUANT

)

D Co

(

)

design such as the researcher(s) not following up with all of the potential quantitative

findings (Creswell, 2014). This will be noted in the limitations section. Strategies for

addressing validity are triangulating different data sources and using member checking

(Creswell, 2014). Triangulation occurred when looking at the findings from both the

quantitative survey and qualitative data. Member checking occurred when asking

participants if researchers captured their responses and thoughts during the focus group

discussions. Strategies for addressing reliability include checking transcripts and cross-

checking codes (Creswell, 2014). Throughout the qualitative data analysis all participant

responses were compiled in a shared document. The researchers crosschecked codes by

coordinating and communicating code definitions to achieve intercoder agreement. More

details about the validity and reliability of the quantitative data are addressed later along

with the survey description.

Sampling Procedure

For the quantitative and qualitative phases of the study, the researchers sought out

rural youth with access to various types of environmental experiences. Participants were

selected from high schools, colleges, or educational clubs in Franklin County, Missouri,

Berkeley County, West Virginia, and throughout the state of Kansas. These groups were

chosen for study based on the likelihood that respondents would be mostly rural and

within the age range of 13 – 22 years old. Within those areas, researchers selected

individuals who were part of an academic program, club, or college course with a strong

environmental science focus, when possible. Surveys questioned the respondents

regarding their age and rural residence in order to confirm the assumption that they are

rural youth. Students were allowed to self-report whether they consider their hometown

to be rural, suburban, or urban. Zip codes were also collected to check the students’

perception of rural versus the US Census Bureau method of defining rural residence.

Self-reported rural residence was compared to the Census Bureau definition of

“rural” as not existing in an urbanized area or an urban cluster. Urbanized areas include

regions with 50,000 or more people, while urban clusters contain between 2,500 and

50,000 people (www.census.gov). Delineations of area boundaries are defined by census

blocks that are dependent on population density. Thus, when considering two towns with

the same population, one that is not in close proximity to any large cities may have

“rural” status while a town with the same population that is nearer to a large city could be

defined as an “urban cluster”.

For the quantitative phase of the study, researchers administered at least 100

surveys to each of the three different study populations (described below), up to a

maximum of 350 total participants. The sampling procedure is quasi-experimental

because the researchers sampled all willing individuals that met our criteria from

previously formed groups such as a classroom or participants of a particular program

(Creswell, 2014). The sampling unit was 52 from the West Virginia population, 100 from

the Missouri population, and 100 from the Kansas population, for a combined total of 252

participants. This sample size is sufficient because it ensured that enough of the

participants fit the age and rural residence parameters so that meaningful inferences can

be made from the data. It is assumed that this is enough surveys to account for

demographic variations and provide a complete picture of the factors influencing

proenvironmental behavior in the populations studied. Surveys addressed the dependent

variables of pro-environmental behaviors (PEB) to determine how it is influenced by the

independent variables of age (A), gender (G), race (R), geographic region (GR), Hispanic

ethnicity (H), socioeconomic situation (SE), political identity of self and family (PA),

connectedness to nature scale (CNS), environmental identity (IDE), meaningful nature

experiences (MNE), influence of role models (RM), STEM interest (INT) and

environmental science capital (ESC). These variables will be addressed using survey

questions. See Appendix A for survey questions.

For the qualitative phase of the study, researchers conducted focus groups. Along

with focus group data, qualitative data were also collected by asking three open-ended

questions on the quantitative survey. Similar to the quantitative sampling, the sampling

procedure for the qualitative was also quasi-experimental meaning researchers sampled

all willing individuals that met the criteria from previously formed groups (classroom and

program). Participants were selected not because of convenience and availability, but

because they are considered “information-rich” individuals (Creswell, 2014).

Information-rich is defined by containing, providing, or possessing a great deal of

information and having easy access to information - especially information considered

important for full participation in society or politics (Oxford definition). Participants were

deemed information-rich based on their direct experiences either being a student in an

environmental class and/or participating in a club or environmental competition. For

example, for the Kansas portion of the study, participants were considered

informationrich based on the selection process employed by their ECO-Meet coach to

win a spot on a coveted ECO-Meet team. Team members for the Colgan/St. Mary's High

School ECOMeet team must rank in the top 12 to qualify for an ECO-Meet team after

competing with others in a "mini-ECO-Meet" devised by the coach. In essence, these

students must really want to compete in this program and they must agree to study and

practice for the ECOMeet competition.

For focus group interviews, the sampling unit was eight from Spring Mills High

School Science Club (West Virginia), seven from the 4H Animal Science Club (West

Virginia), nine from Colgan St Mary’s High School ECO-Meet team (Kansas), and 11

from the East Central College Introduction to Animal Science class (Missouri), for a total

of 35. This sample size was sufficient because with 35 information-rich individuals,

saturation was achieved and no new insights or themes were revealed. Themes of interest

from the literature review included motivation, environmental identity, meaningful nature

experiences, role models, environmental science capital and pro-environmental

behaviors. Focus group questions were determined based on the themes that arose after

analysis of the quantitative data such as the relationship between identity and

proenvironmental behavior.

Population 1: West Virginia – Berkeley County High School Students & 4H

STEM Clubs

Spring Mills High School is the fourth high school in the Berkeley County West

Virginia school system. It is a relatively new school which opened in the fall of 2013 to

address overcrowding. The student body was formed from about one-half of the student

body of each of Martinsburg High School and Hedgesville High School. Their vision

statement is to “utilize technology and data to facilitate a collaborative and engaging

learning environment. Our students will become lifelong learners with the critical

thinking skills necessary to enter the global 21st century workplace.” Students surveyed

include two 9th grade Earth and Space Science classes, one 9th grade Environmental class

and the Spring Mills High School Science Club.

Along with the Spring Mills Science Club, two other STEM clubs were surveyed,

the Berkeley County 4H STEM Club and the Berkeley County 4H Soaring Eagles –

which is an Animal Science Club. The Berkeley County 4H STEM club fosters and

educates youth members interested in the areas of science, technology, engineering, and

math. 4‑H is delivered by a community of more than one hundred public universities

across the nation that provides experiences where young people learn by doing. 4H is in

every county and parish in the country—through in-school and after-school programs,

school and community clubs and 4H camps (4H website, n.d.). The Berkeley County 4H

is delivered by the West Virginia University Extension. Participants complete hands-on

projects in areas like health, science, agriculture and citizenship, in a positive

environment where they receive guidance from adult mentors and are encouraged to take

on proactive leadership roles.

Berkeley County is in the Eastern Panhandle region of West Virginia (Figure 3.2).

As of the Census of 2010, the racial makeup of the county was 87.8% white, 7.1% black

or African American, 0.8% Asian, 0.3% American Indian, 1.2% from other races, and

2.6% from two or more races. Those of Hispanic or Latino origin made up 3.8% of the

population. The median income for a household in the county was $52,857 and the

median income for a family was $64,001. Males had a median income of $45,654 versus

$34,239 for females. The per capita income for the county was $25,460. About 7.0% of

families and 10.1% of the population were below the poverty line, including 13.2% of

those under age 18 and 6.5% of those age 65 or over.

Figure 3.2. West Virginia map highlighting Berkeley County

(http://www.nationalatlas.gov/).

Population 2: Kansas – ECO-Meets, Agriculture Classes, and Environmental

Clubs

Kansas ECO-Meets have a mission statement of: To challenge and inspire an

interest, appreciation and understanding of the natural sciences and the state of Kansas

environment through interscholastic competition. ECO-Meets have been in existence

since 1991 and are a unique environmental competition in Kansas testing students from

grades eight to twelve on their knowledge of Kansas’ plants and animals. Much of the

competition takes place outdoors so experiences in nature are inherent in the event and

make for ideal survey respondents. Sixty-six survey respondents were ECO-Meet

participants from the following six regional ECO-Meets depicted in Figure 3.3- Girard,

Hays, Milford, Olathe, Salina, and Wichita.

Figure 3.3. Kansas Eco-Meets by regions (www.kansasecomeet.org)

Overall, schools from eleven counties were included in the survey response.

Those counties were Allen, Cloud, Crawford, Geary, Johnson, Mankato, Republic, Riley,

Saline, and Sedgwick counties.

Since each ECO-Meet covers multiple counties in Kansas, the size of schools

participating in an ECO-Meet ranges from 1A to 6A (KSHSAA classifications, 2018).

ECO-Meet respondents were distributed between five 6A (largest) schools, three 5A

schools, zero 4A schools, one 3A school, one 2A school, and five 1A schools (smallest).

According to the 2010 Census, the racial makeup of the Kansas population in general is

83.8% of the population is white, 5.9% is African American, 1.0% American Indian or

Alaska Native, 2.4% Asian American, 0.1% Native Hawaiian and other Pacific Islander,

3% from two or more races and 3% other. Ethnically 10.5% of the total population is of

Hispanic or Latino origin.

Along with ECO-Meet respondents, students from the Introduction to Agriculture

class and the Animal Science class at Maize High School in Maize, Kansas were

surveyed. Maize High School is a fully accredited public high school located in Maize,

Kansas, serving students in grades 9-12. It is a 5A school located in Sedgwick County in

south central Kansas with a student population of approximately 1,500. The racial

makeup of Sedgwick County is 68.1% white, 9.3% Black, 1.3% Native American or

Alaskan Native, 4.6% Asian, 14.6% Hispanic, and 3.7% two or more races. The median

household income is $52,841, the per capita income is $27,583 and 14.2% of persons are

living in poverty.

Eco Club, the environmental club at the Topeka Zoo, was the last youth group

surveyed in Kansas. Eco Club provides an opportunity for children and teens interested in

the environment to meet and share ideas and interests on how to positively impact the

planet. Though the Topeka Zoo is only a medium-sized zoo, it houses over 250 animals in

a number of exhibits, including one of the first indoor tropical rain forests in the

United States. Topeka is the capital city of Kansas and situated in northeast Kansas in

Shawnee County. The racial make-up of Shawnee county is 66.4% white, 11.0% Black,

16.1% Hispanic, 1.5% Asian. 0.8% Native American, and 3.5% two or more races. The

estimated median household income was $45,054 and the estimated per capita income

was $25,602.

Population 3: Missouri – East Central College Students

East Central College (ECC) is a public open access institution in Union, Missouri

providing associate degrees and technical certificates to its service region since 1968.

According to the college website (eastcentral.edu) ECC is supported by the college

district, which includes most of Franklin County and portions of Crawford, Gasconade,

St. Charles, Warren, and Washington counties. This includes ten public school districts:

Crawford County R-1, New Haven, St. Clair R-13, Sullivan C-2, Union R-11,

Washington, Franklin County R-2, Lonedell R-14, Spring Bluff R-15 and Strain-Japan

R-16. ECC’s service region extends beyond its taxing district to the entire east-central

region of Missouri, which includes the aforementioned counties as well as Osage,

Maries, Phelps, and Dent counties (Figure 3.4).

Figure 3.4. Map of East Central College district and service region.

Red indicates the home county of East Central College (Franklin). Orange indicates counties that

are at least partially in the East Central College District (From top right, going counter-clockwise:

St. Charles, Warren, Gasconade, Crawford, Washington). Yellow counties that are not in the East

Central College district, but are part of the service region (From indicates top to bottom: Osage,

Maries, Phelps, Dent). Red and orange counties are also part of the service region (adapted from

Wikimedia commons image, Retrieved from: https://commons.wikimedia.org/wiki/File:

Map_of_Missouri_highlighting_Franklin_County.svg).

In 2017, ECC was comprised of 2,897 students, 45% full-time and 55% part-time,

39.6% male and 60.4% female. Its students are 0.5% American Indian/ Alaskan Native,

0.9% Asian, 1% Black or African American, 2% Hispanic, 0.1% Native Hawaiian/Pacific

Islander, 0.9% Unknown, 1.4% Two or more races, and 93.2% white. Table 3.1 shows the

demographics of the counties served by the college. 93% of first-time, full-time students

receive financial aid, 53% receive Pell Grant aid, and 13% receive other types of Grant

aid. The 2017-2018 tuition was $2,592, $3480, and $4896 for in-district students, out-of-

district students, and out-of-state students, respectively (eastcentral.edu, n.d.). The tuition

of the college is meant to remain relatively low compared to the state of Missouri so that

it is considered affordable for students in the rural communities surrounding the college.

As shown in Table 3.1, Franklin County is the home of East Central College, and

is the most populated county in the service region, except for St. Charles County, which

has been omitted due to the fact that ECC only serves a very small portion of that county

and the rest is served by St. Charles’ own community college district. Of the remaining

counties, the population averages 31,587 people, 45.58 people per square mile, median

household income of $43,986, 95.73% white, with 83.68% of 25+ year olds holding a

high school degree and 16.49% holding a Bachelor’s degree or higher (US Census

Bureau, n.d.). All peripheral counties considered in Table 1 meet the qualifications to be

considered “rural” or “urban cluster” according to the US Census Bureau, so it is

assumed that the sample will contain many individuals with a rural background.

Table 3.1

Demographics of Counties in the East Central College Service Region

Franklin 103,330 96.6

In district, in service region

0.5

1.4

1.7

87.2

19.7

$50,895

110

Warren 34,373 94.5

2.3

0.5

2.1

3.3

85.8

18.2

$51,509

75.9

Gasconade 14,726 97.3

0.5

1.3

1.4

16.4

$45,505

29.4

Crawford 24,102 96.9

0.5

0.3

1.5

78.3

12.5

$36,983

33.3

Washington 25,002 95.4

Out of district, in service region

2.4

0.3

1.4

$36,701

33.2

Osage 13,662 98.3

0.4

0.2

0.8

0.9

90.3

19.1%

$54,119

23.0

Maries 8,867 96.3

0.7

0.6

1.6

1.3

82.6

$40,542

17.4

Phelps* 44,744 90.6

2.4

3.7

2.4

2.5

27.9%

$41,603

67.2

Dent 15,480 95.7

0.6

0.8

1.8

78.9

12.6

$38,020

20.8

Note: Table includes counties served exclusively by East Central College. St. Charles County is

excluded because only a small portion of that county is within the ECC service region. The

remainder of the county has its own community college district.

Only the top 5 race/ethnicity classifications are shown. Data are from the United States Census

(Retrieved from www.census.gov).

* indicates Phelps county as a potential outlier due to the presence of a public 4-year University

in that county.

Variables

For the quantitative portion of the study, the survey questions were based on the

variables below (Table 3.2). The variables of interest were determined based on the

literature reviewed. Survey questions were obtained from previous research when

County

Home County

possible. When new questions were developed, they were closely based on other

questions found in the literature. Since this is an explanatory sequential mixed methods

design, the findings from the first quantitative phase informed the measures for the

follow-up qualitative phase.

Methods of Data Collection

The quantitative phase of the study focused on factors that influence

proenvironmental behavior within rural populations. A survey was used for the collection

of quantitative data. The advantage of a survey is that it can generalize from a sample, is

cost effective and produces a generally quick turnaround. The survey was cross-sectional

– taking a “snapshot” at one point in time (Fink, 2017). For the qualitative phase of the

study, topics explored were environmental identity, meaningful nature experiences, role

models, and pro-environmental behavior. The qualitative phase focused on these topics

because they have shown significance in previous research or in the quantitative analysis.

Open-ended questions on the quantitative survey and focus group interviews were used

for collecting the qualitative data. All data were added to a shared spreadsheets and

analyzed by each member of the group. Statistical analyses were performed using SAS

Studio.

Table 3.2

Variables of Interest in the Present Study

Individual

ProEnvironmental

Behavior (PEB_I)

Actions that directly improve the environment, such as

recycling, purchasing eco-friendly products, choosing

public transportation, etc.

Dependent

Variable a

nd Code

Definition

Type

Collective

ProEnvironmental

Behavior (PEB_C)

Actions that promote environmental culture, workforce,

or politics, such as voting proenvironmentally,

participate in environmental careers, publically

supporting the environment, etc.

Dependent

Connectedness To

Nature Scale (CNS)

The connectedness to nature scale measures to

what degree people feel part of nature.

Independent

Environmental Identity

(IDE)

A sense of identity that transcends the individual

and encompasses one’s position as part of a living

ecosystem. Includes identities related to science,

the outdoors, nature, and environmentalism.

Independent

Meaningful Nature

Experiences (MNE)

An experience with nature that one interprets to

have a serious, important, or useful quality.

Independent

Role Models (RM)

A person looked to by others as an example to be

imitated.

Independent

STEM Interest (INT)

Interest in the fields of Science, Technology,

Engineering, and Mathematics. Science includes

both physical and life sciences, such as

environmental and agricultural sciences.

Independent

Environmental

Science Capital

(ESC)

Sum of the environmental science-related

experiences that one builds up over a lifetime.

Environmental science includes agriculture,

animal care, fisheries and wildlife, ecology,

botany, limnology, and other sciences dealing with

the environment.

Independent

Socioeconomic

Situation (SE)

A combination of social and economic factors.

Independent

Political Identity (PA)

The membership in, participation in, or support of, a

particular political party, group, or candidate.

Independent

Gender (G)

Identification as male/masculine, female/feminine or

something else, and association with a (social) role or set

of behavioral and cultural traits, clothing, etc; a category

to which a person belongs on this basis.

Independent

Race

(R)

A grouping of humans based on shared physical or social

qualities into categories generally viewed as distinct by

society.

Independent

Geographical Region

(GR)

Self-described or defined using Census Bureau definition

- rural refers to all population,

housing, and territory not included within an

urban area. Two urban areas are recognized:

Urbanized Areas (UAs) of 50,000 or more people;

and Urban Clusters (UCs) of at least 2,500 and

less than 50,000 people.

Independent

Hispanic (H)

Relating to Spain or to Spanish-speaking countries,

especially those of Latin America.

Independent

Age (A)

A period of a human life measured by years from birth.

Independent

The qualitative phase of the study involved focus groups conducted with a subset

of the participants. A total of 35 individuals participated in focus groups. Two focus group

were conducted with participants from Berkeley County, West Virginia. The first

consisted of eight participants from the Spring Mills High School Science Club at their

High School in Berkeley County, West Virginia on Monday, February 25, 2019. The

second consisted of seven participants from the 4-H Animal Science Club at their meeting

place Shepherd Whey Farm on Friday, March 15. Participants for the Kansas portion of

the study were from Colgan St. Mary's High School in Pittsburg, Kansas. Nine

individuals took part in a one hour focus group on Wednesday, March 20, 2019 in the

conference room at their high school. For college students, focus groups were conducted

with 11 students from an Introduction to Animal Science course on East Central College

campus on Monday, April 8th, 2019 in their normal classroom.

Description of Survey Instrument

A paper and pencil survey was developed through adoption and modification of

questions previously used in the literature and tested for reliability and validity. The first

introduction section consists of several questions on student information such as age,

race, gender identity, how they would describe the place they live (urban vs. rural). The

next section is the 7-item connectedness to nature scale (Mayer & Frantz, 2004). The next

section consists of 18 Likert-scale questions about meaningful experiences. These

questions were developed from various sources (Nature of Americans Report, Sivek

2002, plus some original questions for modern rural youth). The next two sections deal

with Role Models and is from Sivek’s (2002) study and includes 12 questions about role

models, followed up with seven questions about traits of role models. The next section is

nine original questions about environmental identity. Then there are twelve STEM

interest questions derived mostly from Wallace (2018). Pro-environmental behavior

questions were obtained from Fah and Sirisena (2014). Three open-ended questions were

added to provide qualitative data. One question focuses on meaning nature experiences:

“Which of your experiences has been most meaningful? What about it was so

meaningful?” The other two focuses on role models: “If you stated that an unrelated adult

who you know personally or a public figure who you do not know personally was an

important influence on your connectedness to nature, who were you referring to?” And

“Of all the role models who have influenced your connectedness to nature, which role

models would you consider to be the most important?” See Appendix A for a list of all

survey questions and their sources and Appendix B for a copy of the survey instrument.

Several steps were taken to protect the validity and reliability of the survey

instrument. Survey questions obtained from previous research were only used if they

were shown to have validity in previous studies. However, a threat to the internal validity

is the combined instruments and modification of survey items. This may impact the

validity and reliability of the original instruments. To minimize this impact, when

questions had to be modified, they were written as similarly as possible to questions from

previous research that showed validity. These questions, in addition to the few new

questions that were developed, were tested for validity and reliability after survey

analysis using the Cronbach alpha tests. Due to the characteristics of individuals, the

uniqueness of the setting, and the timing of the research, researchers cannot generalize

beyond the study population. Additional experiments will need to be conducted for

groups with different characteristics, new settings or future settings (Creswell, 2014).

Description of Focus Group Protocol

A focus group protocol was developed to gain further insights into patterns

observed in survey data. (Appendix C). This protocol was developed using Krueger’s

“Designing and Conducting Focus Group Interviews (2002). Questions were few in

number and open-ended to elicit the views and opinions of the participants. The types of

questions included: introductory, follow-up, probing, and closing. Notes were also taken

during the focus group to record observed behaviors. During the focus group, participants

began by writing their answers down before sharing to encourage the participants to

selfreflect, to share individual perspectives, and feel comfortable giving in-depth

responses. Participants were then guided through a pre-selected set of questions and the

facilitator captured notes on major themes that arose. At the end of the focus group, the

facilitator shared the themes that arose allowing the participants to determine whether

they agreed with the facilitator’s interpretation of themes for member checking. Written

responses were collected and added as documentation for triangulation.

Data Analysis Procedures

For the quantitative portion of the study, descriptive and inferential statistics were

calculated. Descriptive statistics are used to describe the basic features of the data in a

study. They provide simple summaries about the sample and the measures and are a

logical first step in data analysis. Inferential statistics make inferences about populations

using data drawn from the population. Inferential statistical tests used in this study

include univariate linear regression, multivariate linear regression, one-way Analysis of

Variance (ANOVA), and two-sample t-test.

A linear regression is an appropriate analysis when the goal of research is to

assess the extent of a relationship between a dichotomous or interval/ratio predictor

variable on an interval/ratio criterion variable. This technique was used for the category

means of connectedness to nature, STEM interest, environmental identity, meaningful

nature experiences, role models, and environmental science capital. One-way ANOVA is

an appropriate statistical analysis when the purpose of research is to assess if mean

differences exist on one continuous dependent variable by an independent variable with

two or more discrete groups. This technique was used for all questions with categorical

data, and for each individual question in the broader independent variable categories. The

only exception would be for questions with only two answer choices, for which the

twosample t-test was used. The assumption of normality and homogeneity of variance

will be assessed for ANOVA and regression test results when significance is indicated.

For all of the tests described here, the alpha value was 0.05, so tests returning a p<0.05

were deemed statistically significant.

For certain tests, correlation coefficients were also calculated. A multiple

regression analysis provides the relative prediction of one variable among many in terms

of the outcome (Creswell, 2014). Correlation quantifies the degree to which two variables

are related. By computing a correlation coefficient (r) that tells you how much one

variable tends to change when the other one does. When r is 0.0, there is no relationship.

When r is positive, there is a trend that one variable goes up as the other one goes up.

When r is negative, there is a trend that one variable goes up as the other one goes down.

For the qualitative part of the study, both qualitative data from the open-ended questions

on the survey and focus groups were analyzed. The analysis process was adapted from

Creswell (2014). First, all participant responses (open-ended questions and focus group

discussions) were compiled in a shared document. After all responses were compiled,

researchers read over all the responses to get a sense of the “big picture” and reflected on

its overall meaning. Researchers then identified themes by starting with the broad

concepts of meaningful nature experiences, role models, connectedness to nature, STEM

interest, environmental identity, environmental science capital and proenvironmental

behavior. Researchers then started to identify patterns within these concepts. Researchers

also noted the frequency of themes that were mentioned by participants and recorded

quotes to illustrate those major themes and patterns. These themes and patterns further

informed the interpretation of quantitative data. Results were captured in tables

(Appendix D).

Limitations of Study

The data collected in the quantitative portion of the study were self-reported and

thus could be subject to socially desirable answering behavior. To reduce the effects of

social desirability, the questionnaires ensured full confidentiality and the respondents

were asked to state their own opinions and to answer all questions honestly. There was no

contact between the researchers and the participants that could lead to any type of

emotional response caused by sympathy or antipathy to the participants' answers. For the

survey instrument, several instruments were combined (connectedness to nature scale and

environmental science capital) and some survey items were modified. This may impact

the validity and reliability of the original instrument.

For the qualitative portion of the study, the intent was to describe the particular

themes of rural secondary school and college students within Berkeley County, West

Virginia, Franklin County, Missouri, and Crawford County, Kansas, which presents

limitations to generalizability outside of this study. Additional cases of participants with

the same characteristics of similar programs could be studied for potential

generalizability. However, to repeat a case study’s findings in a new case study setting

requires good documentation of qualitative procedures, such a protocol for documenting

the problem in detail and the development of a thorough case study database (Creswell,

2014).

For the explanatory mixed methods study design, there are challenges in terms of

validity and reliability that must be addressed. One such challenge is not following up on

all of the quantitative results needed in order to explain findings. Creswell (2014) warns

that if this occurs then important explanations may be overlooked compromising overall

findings. Although researchers were careful to follow up on results deemed significant,

more quantitative findings could be addressed in future studies.

Other limitations to the study include discrepancy between age groups of

populations in science capital research and the adequacy of the connectedness to nature

scale (CNS) in measuring one’s affective, experiential relationship to the natural world.

Previous research on science capital, such as the ASPIRES longitudinal study, focused on

youth ages 10 – 14, while this present study focuses on youth ages 13– 22. At present no

studies on science capital focusing on rural populations have been found.

Ethics and Human Relations

Because this research involves collecting data from young people, care must be

taken to protect research participants and personal disclosure, trust must be developed

with research participants, the integrity of research must be promoted, and misconduct

must be guarded and new problems that emerge must be coped with (Creswell, 2014).

Prior to the study, the researchers applied for approval from the Institutional Review

Board (IRB) at the University of Missouri – Saint Louis and obtained the necessary

permissions to gain access to the sites and to study participants (Appendix E).

Researchers conveyed the purpose of the research to participants and obtained the

necessary informed consent from participants. Additional parental consent was obtained

from participants below the age of 18. At the start of surveys or focus groups, the

researchers expressed to participants that they may decline to participate or cease

participation at any time with no consequences. They were also assured that their privacy

and confidentiality would be maintained by properly storing information and sharing data

per the requirements of the institutional IRB. During data collection and analysis,

researchers avoided the exploitation of the participants and collection of any potentially

harmful information.

Researchers obtained access to their study participants through teachers, club

leaders, and coaches. For the Berkeley County West Virginia students and STEM Club

participants, approval and recruiting of participants was through Dr. Robert Myers,

Principal of Spring Mills High School, Spring Mills High School Science Teacher, Mrs.

Angela Hollida, Mr. Michael Withrow, Berkeley County 4H Extension Officer and

various 4H STEM Club leaders, Mr. and Mrs. Charles Engle, Mrs. Nikki Welch and Ms.

Heather Riker Johnson.

For Kansas ECO-Meets, students were asked to participate in the survey by their

ECO-Meet coach. Since IRB approval was received after the 2018 ECO-Meet season,

coaches from the 2018 season were contacted by email and asked if they would be

willing to gather permission slips and administer the survey to students from their

ECOMeet teams. Thirty-two coaches were contacted and thirteen coaches returned

surveys for this research. Those returning surveys were Evan Brandt (Shawnee Mission

North HS), PJ Born (Shawnee Mission South HS), Chris Ollig (Blue Valley North HS),

Denise

Scribner (Goddard HS), Matt Mosher (Salina South HS), Alison Pfeifer (St. Xavier

Catholic HS), Tarry Weese (Miltonvale HS), Curt Parry (Pike Valley HS), Cindy

Thompson (Riley Co. HS), Noah Bush (Manhattan HS), Donna Maus (Colgan/St. Mary's

Catholic HS), Jody Hadachek (Rock Hills HS), and Bailey Myers (Crest HS).

Selfaddressed stamped envelopes were mailed to each coach for the return of the surveys.

A total of 66 ECO-Meet participants took the survey. This was short of the desired 100

surveys of ECO-Meet participants so additional high school students involved in similar

curriculums were recruited. Jay Super, ECO-Meet coach at Maize High School did not

have an ECO-Meet team in 2018 due to a restructuring of the science program, so Mr.

Super offered to survey his Intro to Agriculture class and Animal Science class. These

courses provide an introduction to the flora and fauna of Kansas similar to an ECO-Meet.

Another non ECO-Meet group that was surveyed was the Eco Club at the Topeka Zoo.

The purpose of Eco Club is to increase environmental awareness similar to the

curriculum of ECO-Meet. Dennis Dinwiddie, Education coordinator at the Topeka Zoo

surveyed the club. Eco Club presents a wider range of ages but only secondary students’

responses were recorded.

Contact with college students was obtained through East Central College science

course instructors. Each instructor who was contacted agreed to participate in the study.

Consent forms and surveys were administered during regular class times to ensure that

surveys were returned. The instructors included Keith Pulles and Parvadha

Govindaswamy from the Biology department, Isaiah Kellogg from Engineering, and

Matthew Monzyk from Chemistry. Surveys were administered in the regular classroom at

a time convenient to the instructor. Instructors determined the time and location for the

survey administration and Elizabeth Flotte delivered and collected the surveys.

CHAPTER 4: RESULTS

The aim of this mixed methods study was to explore the concept of environmental

science capital and to investigate how environmental science capital, connectedness to

nature, STEM interest, environmental identity, meaningful nature experiences, and role

models influence the pro-environmental behavior of rural youth. The first phase of this

explanatory sequential mixed methods study involved the administration of a survey

measuring these variables, along with demographic information. In the second phase,

focus groups were conducted to build upon the findings of the quantitative phase. We will

present the results in a similar order, with the first section reviewing the results of the

quantitative phase and the second section reviewing the results of the qualitative phase.

Phase I: Quantitative Results from Surveys

Quantitative data were collected using paper surveys and analyzed using SAS

Studio. Surveys included between 15 and 19 questions on demographics and

programspecific information, depending on the study population. There were 101 Likert-

style questions assessing the independent and dependent variables, and 3 open-ended

questions regarding their most meaningful nature experience, identifying role models

who are unrelated adults or public figures, and their most important role model.

Study Population Overview

Participants were students between the ages of 13 – 22 and were affiliated with a

school or club in Franklin County, Missouri, Berkeley County, West Virginia, or the state

of Kansas. Participants were chosen based on their participation in a science club or class,

with preference for those that focus on environmental science. Students at East Central

College in Franklin County, Missouri were chosen based on enrollment in science courses

that have an inherent environmental focus or would provide access to a variety of science

majors. Following is a list of courses that were surveyed, with the likely academic major

of those students in parentheses: Introduction to Animal Science

(agriculture), Human Anatomy and Physiology I (health science), General Ecology

(various majors), Environmental Science (various majors), General Chemistry II (STEM

majors), and Introduction to Circuit Theory (Engineering). Participants from Berkeley

County, West Virginia were chosen based on their enrollment in an environmental science

class, high school science club, 4H STEM club, or 4H Animal Science club.

Participants from Kansas were chosen based on their participation in a class, club, or

ECO-Meet competition. Survey dates, times, locations, and response rates are shown in

Table 4.1. A total of 252 surveys were collected, with an overall response rate of 57.80%.

The 252 surveyed participants included 100 individuals from Kansas, 100 from

Missouri, and 52 from West Virginia (39.86%, 39.86%, and 20.63%, respectively). The

average age of the population was 17.31 years (±2.16 SD), with 43.60% of the population

in the 16 to 18 year age range. The participants were 55.56% female, 87.25% white, and

45.60% rural (Table 4.2). Further details about the population demographics are provided

in Table 4.2. The distribution of age, gender, race, and self-identified geographic region

are graphically represented in Figures 4.1 and 4.2.

Table 4.1

Survey Response Rates for Study Populations and Sub-Populations

Surveys Response

Subpopulation Date

Location

Received

Rate

College Students - Missouri

Human Anatomy and

Physiology I

01/24/19

East Central College

Union, MO

100%

Introduction to Animal

Science

01/28/19

East Central College

Union, MO

100%

General Chemistry II

01/28/19

East Central College

Union, MO

100%

General Ecology

01/31/19

02/07/19

East Central College

Union, MO

88.89%

Environmental Science

01/31/19

East Central College

Union, MO

61.9%

Introduction to Circuit

Theory

02/04/19

East Central College

Union, MO

83.33%

High School Students – West Virginia

St. Xavier High School EcoMeet Team

2/9/19 St.

Xavier

High School,

Junction City, KS

Shawnee Mission North

High School Eco-Meet Team

2/4/19

Shawnee Mission High

School,

Overland Park, KS

68.7%

Shawnee Mission South

High School Eco-Meet Team

2/1/19

Shawnee Mission South High

School,

Overland Park, KS

45.45%

Pike Valley High School Eco-

Meet Team

2/18/19

Pike Valley High School,

Scandia, KS

100%

Crest HS Eco-Meet Team

2/1/19

Crest High School, Colony,

50%

Miltonvale High School Eco-

Meet Team

2/22/19

Miltonvale High School,

Miltonvale, KS

100%

Salina South High School Eco-

Meet Team

2/20/19

Salina South High School,

Salina, KS

25%

Manhattan High School

EcoMeet Team

2/14/19

Manhattan High School East

Campus,

Manhattan, KS

37.5%

Colgan/ St. Mary's High

School Eco-Meet Team

1/30/19

Colgan St. Mary's High

School,

Pittsburg, KS

100%

Rock Hills High School Eco-

Meet Team

2/15/19

Rock Hills High School,

Mankato, KS

66.67%

Spring Mills High School

Science Class

02/14/19

Spring Mills High School Spring

Mills, WV

41%

Spring Mills High School

Science Club

02/14/19

Spring Mills High School Spring

Mills, WV

53%

4H STEM Club

02/08/19

United Methodist Church

Martinsburg, WV

4H Animal Science Club

Secondary Students - Kansas

02/10/19

Shepherd’s Whey Farm

Martinsburg, WV

100%

Junction City High School

Eco-Meet Team

01/28/19

Milford Nature Center, Junction

City, KS

25%

ECO-Club, Topeka Zoo

01/29/19

Topeka Zoo, Topeka,

73.33%

Goddard High School EcoMeet

Team

1/30/19

Goddard High School, Goddard,

66.67%

62.5%

Maize High School Animal

Science Class

2/27/19

Maize High School, Maize,

12%

Maize High School

Introduction to Ag Class

2/27/19

Maize High School, Maize,

31%

Riley County High School

Eco-Meet Team

2/28/19

Riley Co High School, Riley,

58.8%

Blue Valley North High School

Eco-Meet Team

2/11/19

Blue Valley North High

School,

Leawood, KS

50%

Total

252

57.80%

Note: Times are not included because they are not available for some subpopulations.

Table 4.2

Summary Demographic Data

Demographic Characteristic

Variable Response

Number of Respondents

Count Percentage

State

Kansas

100 39.68%

Missouri

100 39.68%

West Virginia

52 20.63%

Age

13 to 15 years old

60 24.00%

16 to 18 years old

109 43.60%

19 to 22 years old

81 32.40%

Gender

Female

140 55.56%

Male

108 42.86%

Non-binary

1 0.40%

Prefer not to say

1 0.40%

Self-describe

2 0.79%

Race

American Indian or Alaska Native

1 0.40%

Asian

1 0.40%

Black or African American

9 3.59%

More than one race

14 5.58%

Other

7 2.79%

White

219 87.25%

Hispanic

228 92.31%

Yes

19 7.69%

Self-Identified

Geographic Region

Rural

Suburban

114 45.60%

105 42.00%

Urban

31 12.40%

Political Identity

Do not know Republican

74 30.58%

68 28.10%

Independent

36 14.88%

Democrat

41 16.94%

Other

20 8.26%

Figure 4.1. Demographics of Study Population by Age and Race.

Figure 4.2. Demographics of Study Population by Gender and Self-identified Geographic

Region.

Relationships between Independent Variables and Pro-Environmental Behavior

In addition to the demographic survey questions, the survey included seven

Likert-style questions assessing six independent variable categories, with seven questions

for connectedness to nature, 18 for meaningful nature experiences, 19 for role models,

nine for environmental identity, 12 for STEM interest, and 21 for environmental science

capital. Cronbach’s alpha tests indicated that all variable categories had internal

consistency due to alpha scores above 0.7, so these constructs were analyzed individually

and within variable groups. The alpha values were 0.837, 0.852, 0.856, 0.808, 0.786, and

0.856 for connectedness to nature, meaningful nature experiences, role models,

environmental identity, STEM interest, and environmental science capital, respectively.

These questions were compared to the individual’s mean score for 15 pro-environmental

behavior questions, seven of which measured individual pro-environmental behavior

while eight measured collective pro-environmental behavior.

Initially, we were interested in two sub-categories within our dependent variable

of individual pro-environmental behavior and collective pro-environmental behavior.

However, analyses of the survey data did not demonstrate differences in how these two

sub-categories of pro-environmental behavior relate to the independent variables. When

we used simple linear regressions to analyze the relationships between independent

variable category means and the dependent variables of individual pro-environmental

behavior, collective pro-environmental behavior, and combined (individual and collective

pro-environmental behavior), we did not see different results based on the type of

proenvironmental behavior (Table 4.3). P-values and R2 values were similar regardless of

the dependent variable analyzed.

Table 4.3.

Results of Simple Linear Regressions for Independent Variable Category Means by Each

Type of Pro-Environmental Behavior

Individual PEB Collective PEB Combined PEB

p-value R2 p-value R2 p-value R2

Environmental

Science Capital

<0.0001

0.34

<0.0001

0.27

<0.0001

0.25

STEM Interest

<0.0001

0.31

<0.0001

0.3

<0.0001

0.35

Environmental identity

<0.0001

0.33

<0.0001

0.35

<0.0001

0.37

Connectedness to

Nature

<0.0001

0.08

<0.0001

0.08

<0.0001

0.1

Meaningful Nature

Experiences

<0.0001

0.17

<0.0001

0.13

<0.0001

0.18

Role Models

<0.0001

0.09

0.0001

0.05

<0.0001

0.04

Additionally, the Cronbach’s alpha for the mean of all pro-environmental behavior

questions (0.890) was stronger than that of the individual (0.757) or collective (0.837)

subsets alone. Thus, the entire set of pro-environmental behavior is a more reliable

construct than either sub-category of pro-environmental behavior. Due to the results of

the Cronbach’s alpha tests, we used all 15 questions in our pro-environmental behavior

variable for the rest of the analyses presented in this chapter. Descriptive statistics and

one-way ANOVA tests were calculated for each survey question to examine the

relationship between each question response and pro-environmental behavior. The results

of all of these analyses are provided in Appendix D.

When answering the individual survey questions about connectedness to nature,

meaningful nature experiences, role models, environmental identity, STEM interest, and

environmental science capital, participants were able to indicate their agreement to a

statement based on a 5-point scale with the following options: strongly disagree,

somewhat disagree, no opinion, somewhat agree, strongly agree. Meaningful nature

experiences and the first set of role models questions had answer choices on a 5-point

scale ranging from “Not at all Important” to “Very Important” and the second set of role

model questions ranged from “Does not describe my role model at all” to “Describes my

role model very well”. Pro-environmental behavior questions had a 5-point scale ranging

from “Never” to “Always”. In each case, participants checked a box and that information

was recorded as a “1” for the first box (Not at all Important, Strongly Disagree, Never) up

to a “5” for the fifth box (Very Important, Strongly Agree, Always), before entering the

data into a spreadsheet. Answer choices of 1, 2, 3, 4, and 5 represent the five categories

that were compared in each ANOVA tests. Appendix D shows the results of the one-way

ANOVA tests for each survey question.

Following is an example of how all variables in Appendix D were analyzed.

Figure 4.3 demonstrates an example of the relationship between responses to the first

environmental science capital question (ESC1) and participant mean score for

proenvironmental behavior. Responses of 1, 2, 3, 4, or 5 meant “strongly disagree”,

“somewhat disagree”, “no opinion”, “somewhat agree”, and “strongly agree”,

respectively. When individual participant responses to the first environmental science

capital question are compared to their average score in the mean pro-environmental

behavior category using a one-way ANOVA test, it is evident that there is a relationship

between the two variables. When observing the bar graph of the data, that relationship

seems to be positive; as agreement with the statement increases, pro-environmental

behavior increases.

In this case, the dependent variable in the one-way ANOVA test was the

participant’s mean score for pro-environmental behavior, and the independent variable

was the participant’s response to the survey question “Learning about the environment

helps prepare me for my future job”. A bar graph showing the pro-environmental

behavior score for those who answered 1, 2, 3, 4, or 5 to this question is shown in Figure

4.3. The one-way ANOVA test returned a p-value <0.0001, indicating that there is a

statistically significant difference between the pro-environmental behavior means of

participants in each ESC1 response category.

When analyzing the ANOVA results for individual questions, Levene’s Test for

Homogeneity was used to test for homogeneity of variances. In this example, the

Levene’s test returned a p-value of 0.2991. Because this number is above 0.05, we accept

the null hypothesis that variances are equal, thus fulfilling an assumption for the ANOVA

test. In all cases where ANOVA results yielded a Levene’s Test p-value below 0.05, the

Welch’s ANOVA p-value was reported. In this case, the original p-value stands. The

assumption of normality is also evidenced by the relatively normal distribution displayed

in the quantile-residual plot and residual-percent plots (not shown).

Figure 4.3. Relationship between Participant Response to Environmental Science Capital

Survey Question 1 and Mean Score for Pro-Environmental Behavior. Error bars represent

a 95% confidence interval (One-Way ANOVA p < 0.0001, N = 248).

Scores from the independent variable categories of connectedness to nature,

environmental identity, STEM interest, environmental science capital, role models, and

meaningful nature experiences as measured through self-reported survey responses were

averaged to calculate each participant’s mean score for all six categories. Linear

regressions were used to examine the relationship between these combined scores for the

six independent variable categories and the dependent variable of pro-environmental

behavior (Table 4.3, Figures 4.4 – 4.6). Table 4.4 contains the summary statistics for each

independent variable category. The means for each category are above three, indicating that

on average, responses were on the positive side.

Although the univariate model is statistically significant for all six independent

variables and their relationship to pro-environmental behavior, the R2 values are highest

for environmental science capital, STEM interest, and environmental identity.

Comparatively, connectedness to nature, meaningful nature experiences, and role models

explain less of the variation in how respondents reported their pro-environmental

behavior.

Table 4.4.

Summary Statistics for Independent Variable Category Means

Connectedness to

Nature

3.72

0.76

1.00

5.00

252

STEM Interest

3.75

0.70

1.00

5.00

249

Environmental

Identity

3.42

0.61

1.92

4.83

250

Environmental Science

Capital

3.68

0.60

1.76

4.90

249

Role Model

3.30

0.85

1.00

5.00

252

Meaningful Nature

3.68

0.64

1.24

5.00

252

Experiences

Variable

Mean

Std Dev

Minimum

Maximum

It is possible that interactions between these independent variables could result in

statistically significant p-values even if the given variable is not an important contributor

to the overall relationship when all variables are taken into account. Thus, as a follow-up

to these univariate tests, a multivariate linear model that also includes demographic

variables, was necessary to understand how these variables interact to facilitate

proenvironmental behavior.

Fit plots show that while the variables STEM interest, environmental identity,

environmental science capital, and connectedness to nature have positive relationships

with mean pro-environmental behavior, the distributions of STEM interest and

environmental identity are closest to the fitted regression line, with R2 values of 0.37 and

0.37. Environmental science capital fits less strongly with an R2 of 0.24 and

connectedness to nature even less at an R2 of 0.10.

Figure 4.4. Fit Plot for Univariate Analysis of the Relationship between

ProEnvironmental Behavior and Environmental Identity. N = 247, R2 = 0.37.

Figure 4.5. Fit Plots for Univariate Analyses of the Relationship between Pro-

Environmental Behavior and STEM Interest and Environmental Science Capital. N = 246

and R2 = 0.37 for STEM Interest. N = 247 and R2 = 0.25 for Environmental Science

Capital.

Figure 4.6. Fit Plot for Univariate Analysis of the Relationship between Pro-

Environmental Behavior and Connectedness to Nature. N = 248, R2 = 0.09.

Once the relationship between all independent variables and pro-environmental

behavior had been examined using univariate analyses, the next step was a multivariate

linear regression including all important variables from the linear model. All of the six

major independent variable categories were used in the model, but some of the

demographic data could not be used in the analysis, and some needed to be transformed

in order to add those data to the linear model. Below is a summary of how each

demographic variable was analyzed and the reasoning for why and how it was included in

the multivariate linear regression model.

Age, Gender, Race, and Ethnicity

Age was entered into the regression directly because it was collected as an ordinal

variable. Hispanic ethnicity was already a dichotomous variable and did not require any

change. Gender was expressed in the model as male or female because the low number of

individuals who did not choose male or female were excluded from this analysis. The

variable of race was also not included because of the low number of participants who

were not white. Thus, this study is unable to explore the effects of race or the perspectives

of those who do not identify as male or female.

Political Identity

The survey contained four different questions to address participant political

identity. However, only one of those questions was present on the survey for every

population, due to the differences in age between populations. All three study populations

were asked to indicate their political affiliation with the options of “Republican”,

“Independent”, “Democrat”, “Other” or “I don’t know”. We assumed that enough

students over the age of 12 would identify with one of these categories that we could

draw some inferences from political identity data. For the youngest of the participants, we

expect that some of them may simply be identifying as they know their family or

community members would identify, but that is likely how their own political identity

would also be formed, at least at this young age. Older participants at the college level

were also asked three additional questions that were presumed not age appropriate for the

younger participants. College students were asked to indicate their political ideology

regarding social issues, with the answer choice options of “conservative”, “moderate”, or

“liberal”. They were also asked to indicate their political ideology regarding economic

issues, with the same answer choice options. Last, college students were asked to indicate

the extent to which their own political ideology was more or less conservative than their

parents, on a 5-point scale from “much less politically conservative” to “much more

politically conservative”.

When the political party affiliation question responses were compared to scores

for pro-environmental behavior using a one-way ANOVA test, there is a significant

relationship (p<0.0001). A bar graph of these data, excluding the “I don’t know” and

“Other” responses, indicates that those who identify as “Democrat” have the highest

proenvironmental behavior, followed by Independents in the middle, and Republicans

with the lowest pro-environmental behavior. When adding this variable to the

multivariate linear regression model, participants who chose any answer other than

“Republican”,

“Democrat”, or “Independent” for political party affiliation were excluded from the

analysis. This was done in order to not exclude independents, but also not emphasize

them in the analysis because independent is a broad category including liberal,

conservative, and moderate groups. Also, dividing in this way resulted in two groups with

a larger sample size (68 Republican and 77 Non-Republican). This is supported by

comparing the mean pro-environmental behavior scores of these groups (Figure 4.7),

which shows that the pro-environmental behavior mean for Independents is closer to the

Democrat mean than the Republican mean. Thus, the data were divided into

“Republican” and “Not Republican” categories, which pools Democrats and

Independents together. The “Republican” variable was added to the model as Republican

= 1, and Non-Republican = 0.

Figure 4.7. Relationship between Political Party Affiliation and Pro-Environmental

Behavior. Error bars represent a 95% confidence interval.

Political ideology regarding social issues and political ideology regarding

economic issues were both significant when compared to pro-environmental behavior

using a one-way ANOVA test (P = 0.004 and P = 0.017, respectively, N = 93 for both

tests). In both cases, the mean pro-environmental behavior score is highest for those who

identify as liberal and lowest for those who identify as conservative. Participants who

indicated that they are somewhat less politically conservative or much less politically

conservative than their parents had slightly higher pro-environmental behavior scores

than those who indicated that they were somewhat more politically conservative than

their parents or shared political views that were more or less the same as their parents (P

= 0.05, N = 96). Interestingly, none of the 96 respondents indicated that they were much

more politically conservative than their parents. All of these political identity questions

demonstrate a tension between having a politically conservative or Republican political

identity and pro-environmental behavior.

Collecting additional data on the older Missouri population allowed for additional

insights into the political identities of the population in general, especially when

comparing responses to the political party question with responses to the ideology

questions. Only 55 of the participants in the college population chose a political party,

with 31 Republicans, 13 Independents, and 11 Democrats. The remaining 45 either chose

“Other” or “I don’t know” or did not answer the question. In the “Other” category, three

participants wrote in “Libertarian”, and there was one occurrence of each of the

following: “politically unaffiliated liberal”, “no party affiliation”, “anarchist”,

“democratic socialist”, and “issue-by-issue”. This is also the part of the survey that had

the most unsolicited notes written in the margins, as if participants wanted to make sure

the reader knows that they do not identify with the options provided. For example,

“neutral in all political and government matters”, “government will lead to our downfall”,

“not into politics” and “conservative but geez not a radical nutcase that believes

everything that supports my views” were written into the margins of four different

surveys.

Conversely, 93 of the 100 participants responded to both political ideology

questions. There were 27 conservatives, 47 moderates, and 19 liberals regarding social

issues. There were 30 conservatives, 43 moderates, and 20 liberals regarding economic

issues. Based on these data, it appears that there are a substantial number of politically

moderate youth who do not identify with a particular political party. Also, more

participants identified as “liberal” than “Democrat”, but the same pattern is not seen with

conservatives and Republicans. It is possible that conservative, moderate, or liberal

ideology is a better measure of political identity for the youth in this study. However,

political party affiliation was the only political identity measurement collected for all

participants, including the younger West Virginia and Kansas populations. Thus, only the

political party affiliation question was added to the multivariate linear regression to avoid

reducing the sample size of the overall model.

Geographic Region

On the survey, participants were able to self-identify as rural, urban, or suburban,

and also provided their zip code so that we could determine their geographic region in

other ways, if needed. While analyzing the data, we quickly noticed that participants from

the same zip code often self-identified differently, with one seeing their zip code as

“urban”, for example, while others saw the same zip code as “suburban”. We also

recognize that “rural” may not have the same meaning in Kansas as it does in West

Virginia. Due to the subjective and relative nature of these terms, we used the zip code for

each participant and entered it into a database to indicate whether the US Census

Bureau considers that zip code to be an “urbanized area”, an “urban cluster”, or a rural

area. These distinctions varied widely from how one self-identified, especially across

states. For example, many Missourians who saw themselves as rural are considered to be

in “urban clusters” and many West Virginians who saw themselves as suburban are

considered “urban” by Census Bureau standards (Figure 4.8).

One-way ANOVA tests were used to examine the relationship between geographic

region and pro-environmental behavior. The relationship is not significant regardless of

whether self-identified or Census Bureau definitions are used (p = 0.23 for self-identified;

p = 0.2419 for US Census Bureau definition). However, it is possible that a more nuanced

way of classifying rural identity is necessary to truly explore the influence of this

variable. We were interested in determining whether the variable was significant when

part of a multivariate analysis, so we changed it to a dichotomous variable and added it to

the model. When viewing the mean scores for pro-environmental behavior for urban,

urban cluster, and rural participants, urban cluster and rural are more similar (Figure 4.9).

Thus, urban cluster and rural were pooled in the “Rural” category for this analysis, with

urban individuals in the “Non-Rural” category. The “Rural” variable was added to the

model as Rural = 1 and Non-Rural = 0.

Socioeconomic Status

Survey questions for socioeconomic status (SES) were obtained from the National

Center for Educational Statistics, which asks five different questions to indirectly

determine socioeconomic status. Participants are asked to indicate how many books are in

their home, with increasing categories that count on a 1 – 4 scale, whether they have a

computer at home (0 = no, 1 = yes), whether they have a list of up to 5 different

appliances in their home, the level of their father’s education using Likert-style questions

from 1 – 4, and the level of their mother’s education from 1 – 4. All of these responses are

totaled, with possible totals ranging from 0 – 18. In order to analyze these data using a t-

test, participant responses were categorized into low, medium, and high. The National

Center for Educational Statistics categorizes low as a score of 0 - 5, medium as 6 - 13,

and high as 14 - 18.

Figure 4.8. Geographic Region by Population (State) According to US Census Bureau

versus Participant Self-Identification.

Figure 4.9. Relationship between Pro-Environmental Behavior and Geographic Region,

as Defined by the US Census Bureau. Error bars represent a 95% confidence interval.

None of the participants in this study scored below a 5 (Figure 4.10), so scores

were placed into medium and high categories. A t-test that compared the two categories

shows no significant difference in their Pro-Environmental Behavior, due to a p-value of

0.4260 (Figure 4.11). However, it is possible that differences would be observable if there

were respondents with low socioeconomic status. The lack of respondents with low

socioeconomic status is a limitation of this study and prevents us from making inferences

regarding the influence of this variable.

Figure 4.10. Frequency of Socioeconomic Totals. Socioeconomic total is a count of

responses to questions regarding number of books in the home (1-4), computer in the

home (0-1), amenities in the home (0-5), mother schooling (1-4) and father schooling (1-

4) up to a total of 18 maximum.

Figure 4.11. Relationship between Socioeconomic Status and Pro-Environmental

Behavior (p = 0.4260, N = 217)

Socioeconomic status was not added to the multivariate linear regression model

because a large number of participants skipped or answered “do not know” to one or

more of the socioeconomic status questions. Thus, we could not calculate an overall SES

score for many participants. Including this variable in the model would have greatly

reduced the sample size of the overall regression.

Academic Major

The study population of college-age students were asked some additional

questions regarding their education that were not relevant or appropriate for the younger

populations. These were not added to the multivariate linear regression model because

they would greatly reduce the overall sample size. However, the results are of interest

when interpreting the results from the overall study. College students were asked to

indicate the number of years that they had been in college and their academic major.

While number of years in college did not have a significant relationship with

proenvironmental behavior when analyzed using a one-way ANOVA test (p= 0.30), there

was a significant relationship between academic major and pro-environmental behavior

(p = 0.008) When comparing the pro-environmental behavior score means for those in

each academic major, it is evident that Biology majors had the highest pro-environmental

behavior and Agriculture majors had the lowest pro-environmental behavior (Figure

4.12). Results from this analysis indicate that differences in the experiences, knowledge,

attitudes, identities, or interests of students in various major tracks may lead to

differences in pro-environmental behavior.

Figure 4.12. Relationship between Academic Major and Pro-Environmental Behavior.

Results of the Multivariate Linear Regression Model

After analyzing the influence of each variable using univariate methods, we build

the multivariate linear regression model. Other than the six mean categories, we included

age, geographic region based on the Census Bureau definition (Rural = 1), political

identity (Republican = 1), gender (Female = 1), and Hispanic ethnicity (Hispanic

Ethinicity = 1) in the multivariate linear regression. The results (Table 4.5) indicate that

the model is significant (p < 0.0001) and that the independent variables included explain

50.52% of the variation in pro-environmental behavior, as projected by the model. The

adjusted R2 of 0.51 is used because this is a multivariate analysis. The Slope of

Relationship column (Table 4.5) provides information regarding the extent and direction

of the relationship of each variable to pro-environmental behavior, the standard error for

that relationship, the significance of that relationship (t and p-value), the relative

importance of each variable (Standardized Estimate), and the potential for collinearity

(Variance Inflation). The values returned indicate that only political identity (p < 0.0001),

environmental identity (p < 0.0001), STEM interest (p < 0.0001), and environmental

science capital (p = 0.0126) are significant contributors to the model.

Standardized estimates show that the greatest contribution comes from STEM

interest (0.308) then environmental identity (0.28844), then political identity (-0.20163),

and environmental science capital (0.157). Note that the relationships are positive for

environmental identity, STEM interest, and environmental science capital but negative for

political party affiliation. This means that as environmental identity, STEM interest, and

environmental science capital increase, so does pro-environmental behavior.

Table 4.5.

Results of Multivariate Linear Regression Model

Variable

Slope of

Relationship

Standard

Error

t Value

p-Value

Standardized

Estimate

Variance

Inflation

Factor

Intercept

0.65663

0.42248

1.55

0.1215

Age

-0.0393

0.0214

-1.84

0.0677

-0.10974

1.67981

Gender

0.01169

0.07286

0.16

0.8727

0.00788

1.13478

Hispanic Ethnicity

-0.16952

0.12498

-1.36

0.1764

-0.06421

1.05401

Political Identity

-0.33052

0.0817

-4.05

<.0001

-0.20163

1.16829

Geographic Region*

0.0027

0.09311

0.03

0.9768

0.0018

1.815

Connectedness to

Nature

-0.06082

0.05326

-1.14

0.2548

-0.06259

1.4131

Environmental

Identity

0.30555

0.07274

4.2

<.0001

0.28844

2.21752

STEM Interest

0.37846

0.07646

4.95

<.0001

0.308

1.82126

Environmental

Science Capital

0.19719

0.07839

2.52

0.0126

0.157

1.83206

Role Models

0.02236

0.04593

0.49

0.6269

0.02569

1.30968

Meaningful Nature

Experiences

0.08157

0.07448

1.1

0.2746

0.06845

1.83681

*Note: Geographic region uses the US Census Bureau definition of “rural”.

However, being Republican results in lower pro-environmental behavior than not

being Republican. Variance Inflation values are low, indicating that interaction effects are

not a problem in this model.

The plot of observed by predicted values also shows a good fit between the model

and the data (Figure 4.13). The assumptions of normality and constant variance are met,

as shown by a lack of homoscedasticity in the residual plots, and the presence of a normal

distribution in the Q-Q plot and residual distribution plot. Compared to results of

univariate analyses, the importance of environmental identity, STEM interest, and

environmental science capital are confirmed by this model, while the connectedness to

nature scale, role model and meaningful nature experience variables are no longer

significant. Those three variables lost their significance when included in the multivariate

model. This is not surprising as these were the three independent variables with the

lowest R2 values when analyzed individually (Table 4.3). Their individual significance

could have been due to slight interactions with other variables in the model, rather than

having a direct effect on their own.

Figure 4.13. Observed versus Predicted Values from Multivariate Linear Model.

Meaningful Nature Experiences

Meaningful nature experiences and role models did not appear to be important

contributors to the model, but that may be due to the way that these variables were

measured. Unlike the other independent variables that assessed attitudes or values by

asking participants to rank their agreement with a set of statements, the questions that

make up these variables were written with the specific intent to compare to previous

studies. For example, the meaningful nature experiences questions involved a list of

activities (exploring the outdoors alone, time spent working with animals, learning about

the environment in school, etc.) and participants were asked to rank how important those

activities were in influencing their feeling of connectedness to nature. The initial intent

was to determine which experiences were most important to participants, and not to create

a mean score for a construct that would be put into a multivariate linear regression model.

Individuals with a high mean score in the overall meaningful nature experiences category

have had many different types of experiences that they deem important. Comparatively,

someone who has had a few very important experiences would have a low overall score

in this category, but those few types of experiences might have influenced them just as

much as the many types of experiences influenced the person with a high meaningful

nature experiences score. Thus, this question is more useful for characterizing what

experiences are important rather than describing the extent of one’s

meaningful experiences in nature as a broader construct.

Although the meaningful nature experiences mean was not a significant

contributor to the model, most of the individual questions had significant relationships

with pro-environmental behavior. When analyzed using one-way ANOVA tests, most

experiences had statistically significant relationships with pro-environmental behavior.

Individual survey items that do not show a statistically significant relationship with

proenvironmental behavior were directly related to hunting and fishing. Specifically,

hunting with others (MNE 11), hunting alone (MNE12), and fishing alone (MNE 14) did

not have significant relationships with pro-environmental behavior but fishing with others

(MNE 13) did show a significant relationship.

This was very surprising as earlier studies linked hunting and fishing to

antecedents of pro-environmental behavior (Peterson, 1982). In our own survey, 35

written answers given by participants specifically mentioned hunting and fishing as

meaningful nature experiences. For these reasons, it seems these archetypical rural

activities deserved further analysis. To analyze whether there was a specific relationship

between these experiences and pro-environmental behavior, analysis of variance was

conducted after the data was adjusted using an indicator variable for hunting and fishing.

Respondents were given a "1" if their answers to any of the four hunting and fishing

questions were either a "4" (important) or "5" (very important). Other responses were

coded as a zero. A category of "ALL" was created for those who had a "1" for all four

questions and a category of "BOTH" was created if they only indicated importance for

hunting and fishing while with others.

From our respondents, people who fish showed higher environmental science

capital (ESC) means, connectedness to nature (CNS), and science identity than those who

did not fish. (Table 4.6) The ESC mean for those participants that hunt or both hunt and

fish was higher than for those who did not participate in these activities. The CNS mean

was also significantly higher for those who fish and the subset of those that fish and hunt,

either alone or with others. The Science Identity mean was significantly higher for those

that fish as well as those who both hunt and fish, but not for those that only responded with

hunting.

Another surprising result is the gender of those respondents who indicate hunting

and fishing is an important activity. More females than males (52%) indicated that fishing

(with others) was an important or very important meaningful experience. Females

accounted for 49% of those who viewed fishing alone as a meaningful nature experience.

Indeed, 38% of all those who found both hunting and fishing a meaningful nature

experience were female. The research of Stedmen & Heberlein (2001) indicated that rural

upbringings can foster an increase in hunting, especially for males, but females in our

study valued hunting with others (39%) or by themselves (38%) as important

experiences.

These numbers are startling when compared to the nationwide number of females

that fish and hunt. Of the subset of Americans that fish, 27% are female. (USFWS, 2016)

Of the subset of Americans that hunt, only 10% are female (USFWS, 2016). Even more

restrictive is that only 7% of all females in the U.S. fish and only 1% of all females hunt.

(USFWS, 2016). In this context, the fact that 52% of our respondents who value fishing

and 39% of those who value hunting as a meaningful experience (39%) were female is

unexpected.

The higher than normal percentage of female fisherpeople may explain why the

category of fishing with others (MNE 13) initially showed a significant relationship with

PEB. Females tend to display more pro-environmental behavior and report stronger

environmental attitudes and concern than men (Meyer, 2015; Blocker & Eckberg, 1997;

Uitto & Saloranta, 2010). There may also be traits that are associated with fishing that

are different from those associated with hunting that leads to this difference in PEB.

The non-significant correlation between hunting and PEB might reflect the more

anthropocentric view that many rural residents possess. Often rural residents see the

environment as existing to fulfill their human needs (anthropocentric view) and do not

put the needs of the environment above their human needs (ecocentric view)(Bjerke

&Kaltenborn, 1999; Ruawald & Moore, 2002; Huddart-kennedy, Beckley, McFarlane, &

Nadeau, 2009). The new environmental paradigm (NEP) developed by Dunlap &Van

Liere, (1978) taps “primitive beliefs” about the nature of the earth and humanity’s place

in it making it an accepted measurement of the ecocentric view (Dunlap, Van Liere,

Mertig & Jones, 2000). Using the NEP instead of the connectedness to nature scale may

have yielded a different correlation or made clear that anthropocentric views were an

important reason why hunters did not have high PEB.

Role Models

The role model questions on the survey were designed to assess which role

models are most important to the participants and what qualities those role models

possess. This causes a similar issue to that described above with meaningful nature

experiences, in which a high mean score denotes that the person has many types of role

models that they deem important, not that role models were important in forming their

values and attitudes in general. So this variable also does not provide a useful averaged

value. However, responses to these questions demonstrated that fathers/stepfathers were

the most important role models, with the highest mean of 3.98.

Table 4.6

P-values for ANOVA analysis of Hunting and Fishing Questions regarding Meaningful

Nature Experiences

Variable

CNS Mean

ESC Mean

STEM Identity

Hunting w/others

0.4555

.0002

0.1001

Fishing w/others

0.0072

<.0001

0.0130

BOTH

Hunting and Fishing w/

Others

0.0872

<.0001

0.0275

Hunting Alone

0.3748

0.0028

0.1452

Fishing Alone

0.0535

0.0007

0.0105

ALL

Hunting and Fishing with

others or alone

0.0142

0.0001

0.0028

However, mother/step-mother was not far behind with a mean of 3.85. After that,

the following role models’ mean rating of importance from highest to lowest is: female

teacher (3.68), female friend (3.57), male friend (3.55), male teacher (3.47), other male

relative (3.32), other female relative (3.18), other unrelated known male (2.92), other

unrelated known female (2.84), public male figure (2.72) and public female figure (2.45).

For traits of role models, friendly/personable had the highest mean of 4.45 and

knowledgeable had the lowest mean of 4.03.

Phase II: Qualitative Data Analysis

The goal of the qualitative phase of this mixed methods study was to build upon

the quantitative findings for a greater depth of understanding. Qualitative data were

obtained through open-ended questions on the quantitative survey and qualitative focus

group discussions. Since previous research studies show meaningful nature experiences

and role models as significant predictors of pro-environment behavior, these were the

variables the open-ended questions addressed on the quantitative survey. The open-ended

questions were:

Open-Ended Survey Questions

1. Which of your experiences has been most meaningful? What about it was so

meaningful?

2. If you stated that an unrelated adult who you know personally or a public figure

who you do NOT know personally was an important influence on your

connectedness to nature, who were you referring to?

3. Of all the role models who have influenced your connectedness to nature, which

role models would you consider to be the most important?

This initial focus on meaningful nature experiences and role models in the

openended portion of the survey helped to characterize the lived experiences of our

participants. However, our focus shifted to environmental identity and STEM interest for

the qualitative focus groups. This shift was necessary because environmental identity and

STEM interest were the most important variables in our multivariate analysis, yet we had

not yet directly collected qualitative data on these variables. Thus, qualitative focus group

discussions were conducted to obtain more details regarding the participants’

environmental identity, pro-environmental behavior, and how they believe their club

and/or science course has influenced their pro-environmental behavior.

While a focus group protocol and questions were developed (Appendix C),

researchers asked questions and follow up/probing questions depending on the

participants’ flow of conversations. This allowed the participants to lead the discussion.

However, some questions were asked in all focus group discussions to examine

similarities and differences among groups. Participants were also asked to answer certain

questions individually to allow for time for self-reflection without the influence of their

friends and peers. Researchers followed up with these questions during the group

discussion to gain further insight. These questions were:

Opening Focus Group Questions

1. What is your very favorite thing to do when you think about playing in the

outdoors and nature?

2. Is there someone you enjoy spending time with outdoors and/or in nature?

Who and why?

3. Do you consider yourself:

a. A “science” person? Yes ____ No ____

b. An “outdoors” person? Yes ___ No ____

c. A “nature” person? Yes ____ No ____

d. An “Environmentalist?” Yes ___ No ____

e. Why or why not?

4. Has this club/course helped you with taking actions for the environment

(recycling, trash pick-up, voting, public support, etc.)?

5. Do you feel more confident to tackle environmental problems after

participating in (name of club/course)? What about (club/course) made you

feel more comfortable?

Four different focus groups were conducted: the Spring Mills High School

Science Club, the 4H Animal Science Club, the Colgan/St Mary's ECO-Meet Team, and

the East Central College Introduction to Animal Science course. Details about each focus

group are provided below.

Spring Mills High School Science Club

This focus group occurred on Monday, February 25th at Spring Mills High School

with their Science Club. Eight out of the fifteen students enrolled in Science Club

participated in the focus group. The focus group lasted approximately an hour. The first

half of the focus group involved the collection of written responses assessing certain

facets of the participants’ science identity.

4H Animal Science Club

This focus group occurred on Friday, March 15, 2019 at the Shepherd’s Whey

Farm in Berkeley County, West Virginia. The focus group lasted approximately an hour.

The first half of the focus group involved the collection of written responses assessing

certain facets of the participants’ science identity.

Colgan St. Mary's High School ECO-Meet Team

This focus group took place on March 20, 2019 in the conference room at

Colgan/St. Mary's High School in Pittsburg, Kansas. Nine of the 12 students that returned

surveys participated in the focus group which lasted approximately one hour. Participants

began by writing down answers to several questions regarding their time outdoors and

how they view their science identity.

East Central College Introduction to Animal Science Class

This focus group took place on Monday, April 8th, 2019 in the regular classroom

meeting location for the Introduction to Animal Science course at East Central College.

Ten students took part in the focus group, which lasted approximately one hour.

Participants began by writing down answers to several questions regarding their time

outdoors and how they view their science identity.

Qualitative Findings

Participant responses from both open-ended questions from quantitative survey

and qualitative focus group discussions were compiled in one shared document. From that

document, researchers sorted responses using the research variables (connectedness to

nature, meaningful nature experiences, role models, STEM interest, environmental

identity, environmental science capital and pro-environmental behavior) as broad

categories. Once responses were sorted, each researcher identified patterns and themes

from participant responses. Themes were communicated and agreed upon between

researchers to gain inter-coder agreement. Sixteen themes were identified and described

below along with representative quotes. A short table of themes is provided in this chapter

(Table 4.7) and a full table with themes and quotes can be found in Appendix F. The study

populations were chosen based on the assumption that individuals who participate in

science or environmental clubs or courses would exhibit high levels of environmental

science capital. This was evident in many of the responses regarding their day-to-day

interactions with science and the environment and certain experiences that not all youth

can access. In general, the participants have access to environmental clubs, learning

experiences inside and outside of the classroom, and environmental media, all of which

were cited by the participants as important.

Table 4.7

List of Themes from Qualitative Analysis

Theme One

Environmental clubs, learning experiences, and media can provide a social avenue for

building environmental science capital.

Theme Two

Outdoor recreation, working outdoors and/or with animals, and solitary experiences tend

to connect participants with nature.

Theme Three

Participants often exhibit awareness of environmental problems, yet engagement in pro-

environmental behavior is lacking or limited to individual actions.

Theme Four

Participants tend to lack environmental identity or experience conflicts related to their

environmental identity.

Theme 1: Environmental clubs, learning experiences, and media can provide a

social avenue for building environmental science capital.

When asked about their most meaningful experiences, participants often

mentioned the clubs or groups in which they participate, inside or outside of school. For

example, an ECO – Meet participant from Goddard High School alluded to the

importance of being a contributing member of the ECO-Meet team at their school.

Just being able to contribute to the ECO-Meet team. Making stronger bonds

with friends, old and new.

Boy and Girl Scouts were specifically called out by many participants as being

particularly meaningful. One member of the Riley County High School ECO-Meet team

described how experiences as a Boy Scout were impactful because of the social

experience and memories formed with others.

Boy Scouts has been pretty impactful and I think spending time in outdoor

areas with my friends falls into that. The memories you gain with that is

what makes it important.

Another ECO-Meet participant from Shawnee Mission North High School

suggested meaningful experiences is just implied with being a Boy Scout.

Being an eagle scout. I think enough is said there.

Environmental clubs provide significant learning experiences. In fact, participants

frequently described certain experiences as meaningful simply because of their

educational value. While it was not surprising that participants enjoyed these experiences,

it was surprising how often educational (rather than recreational) experiences were

mentioned. Some participants, such as an ECO-Meet participant from Shawnee Mission

North High School identified their innate love of learning as the catalyst for their

meaningful experiences.

I don't think there's been any one thing. I'm naturally very curious and I

love learning. Science is always changing, so there's constantly something

new to learn. I also grew up appreciating the small details of nature.

One ECO-Meet participant from Manhattan High School mentioned learning about

environmental issues in school, partly because of the social aspect of learning alongside

classmates. This sentiment was reflected in various other quotes regarding meaningful

nature experiences.

Learning about the environment in school. It was the most meaningful

because we got to learn every aspect of environmental issues and got to

learn with peers.

Sometimes, participants mentioned specific school field trips that were

meaningful, such as the description of a Biology class field trip described by an

ECOMeet participant from Colgan/St. Mary’s High School.

Outdoor field trips with Biology class to go water wading in creeks. I got

to spend time with friends and experience the beauty of God's work while

learning more about the natural environment.

In addition to the day-to-day, hands-on exposure with environmental clubs and

learning experiences, exposure to environmental media was also important for

participants. Sometimes, media was mentioned as a meaningful experience.

Books that I have read. This is meaningful because, for me, books allow me

to envision a new and better world. Books allow for the world to be seen

from another person's perspectives. This includes nature and the feelings

surrounding it.

Other times, media had the effect of educating the participant on an environmental

issue and inspiring them to care about the issue. An East Central College student in a

Chemistry class described one such example regarding pollution.

I believe the most meaningful experiences are seeing how humans have

damaged the Earth with your own eyes. Reading about it vs. seeing it

makes it feel so much more real. I had recently watched a movie and an

underwater scene showed pollution and dumped cargo crates from boats.

The pollution had little to do with the movie but it really made you think.

Sometimes, the environmental media was powerful enough to inspire participants

to act. One East Central College student in an Animal Science class explained how

environmental media inspired them to choose their career path.

When I first saw the before and after of Antarctica's melting ice caps, my career

was decided.

Social experiences, such as with clubs, are meaningful to participants because they can

bond and form memories with others. Two participants from the Topeka Zoo ECO-Club

share their thoughts on how outdoor experiences have become family traditions and that

during these times, they feel present.

Fishing and visiting zoos with my family, because both of those things have

been kind of like traditions for my whole life and so I have strong

connections to them.

The most meaningful were the experiences with my friends and family

because it is a time we can disconnect from technology and be in the

moment.

Two participants from the East Central College Anatomy and Physiology class mentioned

using time outdoors as a way to bond and build stronger relationships.

Spending time in the outdoors with my family, because it brings us closer in

our relationship with each other because we have all learned, experienced,

and created memories concerning the same event.

Spending time outdoors with friends and family. It is meaningful because it

forms a bond by interacting with each other.

Theme 2: Outdoor recreation, working outdoors and/or with animals, and solitary

experiences tend to connect participants with nature.

When recalling meaningful outdoor experiences, participants described both

consumptive and non-consumptive types of outdoor recreation. As an example of a

nonconsumptive activity, a participant from East Central College Animal Science course

explained how being outdoors benefits her well-being.

The most important and meaningful experiences to me are the ones that

keep you craving more. I love camping, sleeping on the ground in a tent

because it's relaxing. I crave camping/hiking/anything outdoors. I have

very low vitamin D so alongside taking vitamin D pills, something that

helps me get along stress free is spending most of my time outdoors. To

breathe fresh air in and feel nature swallowing you feels like a dream.

Along with non-consumptive recreational activities, participants also mentioned

consumptive activities such as hunting and fishing. One participant from East Central

College Anatomy and Physiology class mentioned the significance of providing for your

family while also benefitting wildlife.

Hunting with others; because you are taking from nature to feed your

family while also allowing nature to flourish due to less overpopulation of

animals.

Another East Central College Animal Science student relays how their childhood

experiences and family influenced their connectedness to nature through hunting,

working outdoors and fishing.

Spending time hunting, working, and fishing outdoors alone and with

family is what I feel has influenced my connectedness to nature the most.

I've been outdoors all my life and was taught to sit back and

enjoy/appreciate nature since I was young. Being out there gives you a

sense of purpose in your life.

Participants described working outdoors and/or with animals as meaningful experiences

that connect them to the environment. An ECO-Meet participant from Salina South High

School mentioned how this started their interest in conservation.

Time spent working with animals was the most meaningful to me because it

sparked my passion for the environment. It started my interest in conservation

and how I could do my part to help.

Another ECO-Meet participant from St. Xavier High School expressed their love

for animals and how that love of animals inspired their career choice.

Working outdoors and with animals because I love being outdoors and I love

animals. I have a job where I work outside and we see a lot of wild animals.

I love seeing these beautiful creatures.

Other participants just expressed the intrinsic appreciation of animals. One West

Virginia 4H Animal Science member happily exclaimed her joy of taking care of animals.

Taking care of goats, dogs and cats. That’s my life!

When analyzing participant responses, we noticed that both the solitary and social

aspects of these experiences are important. Some experiences are more meaningful when

they occur alone, and some are more meaningful when shared with others. Further,

participants describe the different benefits gained from solitary experiences versus those

that occur with others.

Although there has been a focus on friends and family with prior research on

connectedness to nature, many participants described solitary experiences as being

meaningful. An East Central College student describes a sense of calmness and belonging

when spending time alone in nature.

Going on a hike and then fishing by myself was probably most meaningful.

Walking through the woods to the pond was a unique experience for me. There

were no sounds except for my footsteps and the birds. It made me feel calm and

like I belonged there.

An ECO-Meet participant from Shawnee Mission South High School agrees by

adding that the connection to nature is deeper and more personal when experienced alone.

Spending time outdoors alone is a great way to connect to nature on a

deeper and more personal level.

Some participants mentioned they enjoyed spending time outdoors alone to

disconnect from “life” and reconnect to nature. A Spring Mills Hill School student in

West Virginia mentioned being able to enjoy the beauty of nature without distractions.

Spending time outdoors alone has made me feel very connected to nature

because it’s nothing but me and my surroundings. There is no distractions to

take me away from the beauty of nature.

Another student from the Colgan/St. Mary’s High School ECO-Meet team shared

that same sentiment of being able to de-stress and disconnect.

Time spent outdoors while alone has allowed me to particularly examine the

world around me without thinking of other needless stress. It is primarily a

disconnecting experience.

Although many of the nature experiences described by participants are regular

occurrences in their lives, sometimes participants described unique or awe-inspiring

experiences that brought them closer to nature or changed their perspective. Some

participants described experiences that may seem mundane but had quite an impact.

When I was little my cousins and I would pick up toads to look at them.

I have had a baby bird 'fly' out of its nest and land on me ...TWICE! Once

a baby cardinal and once a baby Robin. It showed me up close the true

beauty of nature. It demands respect, but also unity with all creation.

In contrast, some participants mentioned experiences in faraway locations and/or

exotic wildlife.

I have been to Puerto Rico and was able to see the rainforest and several

other awesome places that made me fall in love with the outdoors.

Yellowstone and seeing the geysers. And buffalo that stop traffic.

Theme 3: Participants often exhibit awareness of environmental problems, yet

engagement in pro-environmental behavior is lacking or limited to individual actions.

It is clear that participants have positive feelings toward nature and the

environment. Numerous participant responses also indicated that participants generally

have an awareness of environmental issues and are inspired to act on those issues. One

participant noted that change begins with awareness while another participant states how

small actions add up. Both quotes are from Colgan/St. Mary’s High School students.

After seeing all the trash in the environment, you start to hear the stories

about pollution everywhere. That awareness is where change starts.

I think being in ECO-Meet/Science club has helped me because now I'm

more aware of how much the environment and all organisms that live

within are affected by our careless actions, and that my small steps to help

do amount to something.

Some participants mentioned that tackling these environmental issues as a group

made them feel more confident. For example, a Spring Mills High School student

contributes school and peer support for this increased self-efficacy.

I feel more able to confront environmental problems when supported by

friends and school organization.

Despite this apparent motivation to act, participant responses often indicated a

lack of engagement with pro-environmental behavior. However, when participants did

describe their pro-environmental behavior, it was usually limited to individual actions

opposed to collective actions. A participant from Spring Mills High School in West

Virginia offers this list of her actions.

I mainly follow the common advice: don’t litter, reduce your energy, don’t use

plastic if you can, use degradable [items], reuse things, etc.

Theme 4: Participants tend to lack environmental identity or experience conflicts related

to their environmental identity

Given the populations studied and their exposure to science clubs, science

courses, and day-to-day exposure to science, it is no surprise that participants in this study

indicated science identity and relevancy. Qualitative results confirmed this initial

assumption. One East Central College student in an Animal Science course explained

why they are a science person during the focus group interview.

I'm a science person because I always ask why, how, what, who and when.

I love learning about our world and hopefully how to make a difference

and sharing humanity's ecological footprint.

A participant from Spring Mills High School in West Virginia mentioned that

science is important because of its relevance to society.

I’m interested in science because it helps other people.

This sentiment was echoed in the statement of another East Central College

Animal Science student.

The more you examine an object's state, makeup, and origins, the more

complex it becomes. Science is the most powerful tool humanity has at its

disposal.

Another participant from Spring Mills High School acknowledges that science

and technology is relevant even beyond a science career.

I’m not interested in a science job but using science and technology within

my job.

Although science, in general, seems to be accessible to the population in this

study, and participants seem to be pro-science, we were especially interested in

environmental science and whether attitudes and behaviors toward the environment are

influenced by archetypal “rural” experiences. Open-ended survey responses revealed the

importance of outdoor recreation and working outside and/or with animals to the study

participants.

These findings highlight the gap between pro-environmental attitudes and

collective pro-environmental behavior in this population. Although this gap could exist

for a multitude of reasons, one potential reason stands out in participant responses in this

study. Participants often indicated that they lack an environmental identity or that their

environmental identity exists in tension with the other identities that they hold. Two East

Central College students offer reasons as why they do not identify as environmentalists.

I don't practice any rituals that are environmentally friendly. I believe we

should all get on board to help the planet but I don't actively recycle or

anything like that.

I do not see myself as an environmentalist, simply because it is not my

main priority all the time. I do things that help the environment, but I do a

lot of things that hurt it too.

One participant from Spring Mills High School suggested that advocacy is a part

of being an environmentalist.

I don’t consider myself an environmentalist because while I don’t do harmful

things to the environment, I don’t often speak up about environmental issues

to other people.

One participant from East Central College student even expressed a rather

antienvironmentalist sentiment.

I'd say I'd probably take diesel trucks and cow farts over being

subconsciously nervous about the environment.

This lack of environmental identity exists despite a generally high level of science

identity. Thus, the two types of identities have different meanings for participants. We

learned more about these meanings by noticing the types of language that participants

used to describe these identities, along with “outdoors” and “nature”-related identities. In

the following themes, participants expand more on how they identify or not with being a:

science person, outdoors person, nature person and environmentalist.

When participants described why they see themselves as being “science” people,

they often described conventional traits of scientists that suggest “braininess.” For

example, one student from Spring Mills High School said he liked science because he

was logical. A 4H Animal Science member said she saw herself as being a “science”

person because she wants to know how things work and recalls the objective nature of

science.

I want to know why things work and facts versus opinions!

Other participants mentioned that they saw themselves as “science” people

because they liked science activities such as experimenting and the challenges that come

with those activities. The challenge with this conventional stereotype of scientist being

“brainy” is that some participants don’t identify with “brainy” or “clever.” For example,

one student expressed their frustration with participating in science.

I don't because I am horrible at science and I think it takes me longer to

understand it, either that or I was never taught it well. It stresses me out a

bit and it’s hard for it to keep my focus long enough for me to understand it.

When describing themselves as “outdoors” people, participants described more

active experiences such as camping, hiking and exploring. One participant from East

Central College mentions their craving for the outdoors.

I crave to be outdoors 24/7. When I'm inside all I can think about is going outside.

Camping was mentioned often as a popular outdoor activity.

Both of these quotes are from members of the 4H Animal Science club, however,

one participant evokes adventure by expressing overcoming fear.

Me and my family go camping every year up and down the east coast.

I like camping but I’m kind of afraid of camping!

Similar to an outdoors identity, participants also used action terms when

describing environmentalists. However, participants framed these action terms around

advocacy. Examples of these advocacy terms include: passionate, speak up, encourage,

protect and responsibility. During the Spring Mills High School focus group, participants

discussed that they see themselves as “environmentalists” because they advocate behavior

through their club.

[Spring Mills Science Club] encourages us to use other options, such as eco-

options as a consumer.

Encourage family members such as not to waste water.

Another student recognizes that her Mom is an environmentalist due to her

passion and that her Mom engages her daughter through conversations.

Mom is passionate about it [environment] and talks to me.

When describing themselves as “nature” people, participants used words such as:

beauty, art, peace, harmony and happiness as the enjoyable aspects of nature. One student

from the Spring Mills High School explains that she is a “nature” person because she

draws inspiration from nature for her art.

I’m an artist and I get inspired by nature. I draw from it and how it makes

people feel good.

Two other students from East Central College expand on this image of a “nature”

identity as someone that values the “nurturing” concepts of nature such as harmony, peace

of mind and internal happiness.

Nothing is more beautiful than nature in complete harmony.

Something about the outdoors brings me peace of mind and internal

happiness.

CHAPTER 5: DISCUSSION

This chapter discusses the quantitative and qualitative results of this study within the

context of environmental science capital and pro-environmental behavior. We present the

major findings of this study and examine the ways in which the present study supports or

contradicts the current literature on pro-environmental behavior in order to better

understand the study populations. This chapter also addresses implications for educational

practitioners, limitations of this study, and recommendations for future research. The

findings of this study build upon the science capital research by helping to define and

characterize the environmental science capital of rural youth.

Determinants of Pro-Environmental Behavior

This study explored how a variety of factors influence the pro-environmental

behavior of rural American youth aged 13 - 22 who participate in environmental clubs,

competitions, or courses. We assessed the determinants of pro-environmental behavior by

surveying youth from Kansas, West Virginia, and Missouri and measuring

proenvironmental behavior, STEM interest, environmental identity, socioeconomic status,

political identity, geographic region, role models, meaningful nature experiences, and

connectedness to nature. Multivariate linear regression analysis indicated that STEM

interest, environmental identity, and political identity are the major determinants of

proenvironmental behavior in the study population (Table 4.5).

STEM Interest

Although the relationship between STEM interest and pro-environmental behavior

may seem obvious in the context of this study, STEM interest is not typically a variable

considered in the pro-environmental behavior literature. The education research regarding

STEM issues is generally separate from that of environmental issues; STEM research

aims to increase participation in technical careers, while pro-environmental research aims

to increase environmentally sustainable behaviors. We were interested in the overlap

between these two fields, so we based our STEM interest variable off of questions from a

study exploring citizen science identity (Wallace, 2018) that explored the concept of

conservation and environmentally-minded STEM (CEmSTEM). CEmSTEM is a useful

way of measuring interest in people who aspire to STEM careers for environmental

reasons. CEmSTEM questions and traditional STEM questions were combined in our

STEM interest section of our survey. Having a high STEM interest means that students

are interested in science careers, believe in the ability of science to solve environmental

problems, and are externally motivated to participate in STEMrelated or agricultural

careers. Wallace (2018) demonstrated that citizen science projects increase STEM interest

levels, and this study demonstrates that higher STEM interest levels are related to higher

pro-environmental behavior. This finding is encouraging because it suggests that certain

STEM-related educational programming can lead to environmental behavior change.

Environmental Identity

Our research indicates that environmental identity is the second most important

factor influencing pro-environmental behavior and political identity is third. Although

initial studies of pro-environmental behavior focused on attitudes and values, recent

studies show that identity may be more important (Gatersleben, Murtagh, & Abrahamse,

2014). Falk (2011) cites that most studies of identity have focused upon the qualities

related to the big divisions of humanity such as race, religion, or national origin

(higherorder identities), however, his work contends that lower-order identities have a

great impact on day-to-day decision making, including those involving leisure decisions

like visiting a museum (Falk & Storksdieck, 2005). Lower-order identities may include

one’s sense of being a member of a family, a good friend, or even a valued employee.

(Falk, 2011). In this context, environmental identity would behave as a lower-order

identity and influence the day-to-day behaviors to act pro-environmentally to a greater

degree. One could argue that deciding to act pro-environmentally is similar to the process

of making leisure decisions. Neither decision is being forced upon a person nor is no one

held accountable for these decisions through laws or policies.

The environmental identity section of the survey measured how participants

identify regarding various subtypes of environmental identity, from “outdoorsy person” to

“environmentalist” to “someone with unique knowledge about nature”. This set of

questions was original to this study and was developed because self-identification and

being seen as a “science person” by others was shown to have importance within the

science capital literature. The aim of these questions was to identify which types of

identity were important in environmental science capital, as opposed to general science

capital. When each survey item was analyzed separately, the categories of environmental

identity that had the three highest means were: someone who values protecting the

environment (4.26), someone who values the conservation of nature (4.13) and an

outdoorsy person (4.02). The categories with the two lowest means were: “someone with

unique knowledge about nature” (3.35) and “someone who has a special relationship with

the land” (3.32). This suggests the concerning trend of youth lacking vernacular

knowledge and a decreased sense of place. David Orr defines vernacular knowledge as

the “knowledge that people have of their places” and notes that while some areas of

knowledge are increasing (such as the more lucrative sciences), others are decreasing

such as Aldo Leopold’s science of land health (Orr, 2004).

The results of the multivariate linear regression analysis guided the design of

focus group interviews. After quantitative analysis revealed the importance of identity,

focus groups were conducted to learn more about the relationship between identity and

pro-environmental behavior. The survey could not explain why participants might see

themselves as science people and outdoorsy people, for example, but not as

environmentalists. Qualitative data helped to further explore this phenomenon. This was

the primary goal of the focus group interviews conducted after the quantitative data were

analyzed, for that very reason. Qualitative analysis also explored responses to open-ended

survey questions, though these focused on meaningful nature experiences and role

models. All participant responses were recorded and coded, resulting in the emergence of

16 themes. Four of those themes related to STEM interest, environmental identity, and

political identity:

• Participants tended to have a strong science identity and perceive science as

relevant to their lives.

• Participants often lacked environmental identity or experienced conflicts

related to their environmental identity.

• Participants generally seemed aware of environmental problems and motivated

to solve them.

• Participants tended to participate in individual pro-environmental actions,

rather than collective actions.

These themes indicate that the study population has high levels of science

interest and identity. This is not a surprising result, given that participants were chosen

based on their participation in science clubs or courses. During the focus group

discussions, participants were asked why they do (or do not) identify as a science person,

nature person, outdoors person, and environmentalist. Out of the total 35 focus group

participants, 30 identified as a nature person, 29 identified as a science person, 28

identified as outdoors person and 20 identified as environmentalists. This supported our

quantitative findings that fewer participants identified as environmentalist which ranked

in the bottom three lowest means (3.35). More importantly, participant responses to these

questions helped to elucidate what being a “science” person means to them, versus

“nature”, “outdoors”, or “environmentalist”. The language used in those responses

demonstrate that “science” carries a (brainy/logical) identity, “nature” is more about how

it makes the person feel, “outdoors” is for activities and enjoyment, while

“environmentalist” is all about pro-environmental action.

Although many participants of this study identified as “scientists” some

participants did not. Those students said, “I don't really like all the formulas and equations

but I like animals, anatomy, and the nature aspect.” and “I don't because I am horrible at

science and I think it takes me longer to understand it, either that or I was never taught it

well. It stresses me out a bit and it’s hard for it to keep my focus long enough for me to

understand it.” With these participants it is demonstrated that in order for these

individuals to engage with science, the scientist = braininess link is a barrier (Archer et al.

2015). From their quotes, it is shown that they enjoy some aspects of science but this

limited mindset of science is preventing them from identifying as scientists. The focus

group questions pertaining to identity also shed light on why participants might exhibit

low pro-environmental behavior scores, despite their apparent positive attitudes toward

nature, the outdoors, science, and the environment.

Multiple participant responses indicated a lack of environmental identity or

conflicts with environmental identity and other identities that they hold. Either these

conflicts were related to the fact that the person does not engage in pro-environmental

behaviors, or it was a conflict with their rural or agricultural identity. This is

demonstrated by the striking difference between these two quotes: “(I am a scientist

because) I have always been fascinated with trying to see why things are the way they

are” and “ I'd say I'd probably take diesel trucks and cow farts over being

subconsciously nervous about the environment”. This is supports that science capital and

environmental science capital are separate concepts with different antecedents.

We also found that the participants were generally aware of environmental

problems and motivated to solve them. Respondents often indicated a passion for

protecting the planet and knowledge that they’ve gained on how to do so. Yet, many

participants described a lack of engagement with pro-environmental behavior, and those

who did describe their pro-environmental behavior described individual rather than

collective actions.

These results demonstrate the knowledge-action gap that is well documented in

the literature. Despite their awareness and motivation to solve environmental problems,

participants do not exhibit high levels of engagement with environmental issues. Given

the previously described relationships between pro-environmental behavior and political

or environmental identity, it is possible that tensions between environmental identity and

rural/agricultural or political identity contribute to that gap.

While examining focus group responses, we also noticed that participants used

different language to describe different types of identity. Participants were asked if they

see themselves as a “science person”, an “outdoors person”, a “nature person”, and an

“environmentalist”. As a probing question, they were asked why they do or do not

identify in these ways. The language that participants used in their responses helped to

characterize the identities of these types of people, from the participants’ perspectives.

The following themes emerged from analysis of participant language when describing

their identities:

• Participants often described “science” people as being smart, logical, and

clever.

• Participants often described “outdoors” people as being active and adventurous.

• Participants often described “environmentalists” as being action and advocacy

based.

• Participants often described “nature” people as enjoying nature because it

offers beauty, peace, harmony, and happiness.

Most participants identified in more than one of the above ways, but their

language when describing why they identify in that way was most telling. They identified

as science people because they are logical and inquisitive. They identified as outdoors

people because they enjoy recreational activities outdoors. They identified as nature

people because of a deep connection or feeling gained from being outside in nature.

Those who identified as environmentalists did so because they feel the need to protect the

environment, but when participants did not identify as environmentalists it was because

they do not typically engage in pro-environmental behavior.

Analysis of identity language revealed not only a difference between these

identities, but also a special requirement of action or advocacy in the case of

environmental identity. For example, whether participants viewed themselves as scientists

seemed to be based upon innate qualities or interests. Yet, whether people viewed

themselves as environmentalists was most often based on what actions (or lack thereof)

they take toward the environment. It seems that participants believe they cannot be

“environmentalists” unless they are contributing substantially to the solving of

environmental problems. Simultaneously, the survey results indicate that participants are

less likely to behave pro-environmentally if they lack an environmental identity. This

suggests a positive feedback cycle between environmental identity and proenvironmental

behavior. In essence, having an environmental identity leads one to behave pro-

environmentally, but the act of engaging in pro-environmental behavior leads to the

development of a pro-environmental identity.

Our findings related to identity and pro-environmental behavior support the theory

that the lack of past engagement in pro-environmental behavior is itself a barrier to future

engagement in pro-environmental behavior. Previous studies have alluded to the

importance of developing social, cultural, and personal norms when seeking behavior

change (Gifford and Nilsson, 2014), and even the Kollmus and Agyeman study (2002)

shows old behavior patterns as the greatest barrier preventing the flow from

environmental knowledge to environmental action. These studies characterize this

problem as an issue with breaking old habits or a threat to motivation but we posit that

past behaviors pose a threat to identity.

More research is necessary in order to determine the most significant barriers to

pro-environmental behavior in rural American youth. This problem requires more

research into the pro-environmental behavior of rural youth in general, investigation of a

positive feedback cycle between identity and behavior, and further exploration of the

rural-environmental and political-environmental tensions. However, it is clear from this

study that any activities or experiences that involve reducing these tensions, building

environmental identity, or engaging youth in pro-environmental behavior could be

effective avenues for increasing environmental science capital. Exploring what activities

or experiences are successful in building environmental science capital is important for all

youth, even those who are seemingly pro-science and pro-environmental. Because as this

study demonstrates, even rural American youth with high science capital may not have

high enough environmental science capital to facilitate engagement in proenvironmental

behavior.

Political Identity

Political Identity was the only demographic variable that had a significant

relationship with pro-environmental behavior. In fact, it was the third most significant

variable in the multivariate linear regression analysis. The pro-environmental behavior

score for those who identified as having a Democrat or Independent political affiliation

was significantly higher than the pro-environmental behavior of those who identify as

Republican. The Missouri subpopulation answered two additional questions regarding

political identity that were also significantly correlated with pro-environmental behavior.

When participants were asked to indicate their political ideology regarding social and

economic issues, having a liberal ideology resulted in greater pro-environmental behavior.

The relationship between relatively liberal (versus conservative) political identity has

been demonstrated in previous studies (Dunlap, Xiao, & McCright, 2001; McCright &

Dunlap, 2011). This finding could be a result of the way environmental issues have been

politicized in the media, or it could be based upon the tendency of people who are

politically conservative to have less environmental concern and hold less ecocentric

worldviews (Dunlap et al., 2000). Given the importance of political identity in this study,

it would have been useful to utilize a metric for ecocentric worldview, such as the new

environmental paradigm, to test whether political conservatives are experiencing

cognitive dissonance between their environmental and political identities, or whether they

are simply less ecocentric.

Our findings regarding political identity and low collective pro-environmental

behavior support Chawla and Cushing’s (2007) advocacy for a political model of

environmental education. With collective actions being more effective, Chawla and

Cushing (2007) identify antecedents of political action. They include: Democratic

parenting style, collaborative decision-making in everyday life, teachers creating

opportunities for open discussion, and family members as critical role models for public

issues, prosocial values and social justice. The most effective way for youth to learn

about government and politics is to engage with public issues at the local level where

they can see their efforts are taken seriously by others.

“Behaviors with the largest potential of benefits for the environment

require political engagement. Although private actions for the

environment are important the most effective actions are collective, when

people organize to pressure government and industry to act for the

common good” (Gardner & Stern, 2002.)

Characteristics of Environmental Science Capital

We define environmental science capital as the sum of all of the environmental

science-related experiences that one builds up over a lifetime. We are interested in the

potential of this term to help explain why certain people are more likely to engage in

environmental issues and behave pro-environmentally. The term “environmental science

capital” is introduced in the present study, and is based upon the concept of science

capital. The concept of science capital is defined as what you know about science, who

you know that influences your views on science, your values and attitudes toward

science, and your engagement with science in daily life (Archer et al., 2015). One of our

major goals was to explore this concept and characterize the environmental science

capital of rural American youth in this study.

Quantitative analysis indicated that certain aspects of environmental science

capital are particularly important determinants of pro-environmental behavior. For

example, having an interest in science and the environment builds environmental science

capital. Having a science or environmental identity builds environmental science capital.

Since the most important independent variables contributing to pro-environmental

behavior in this study were identity and interest, it is clear that those aspects of

environmental science capital facilitate pro-environmental behavior. We also included a

set of questions in our survey in the environmental science capital category, which

included questions based off of Archer’s (Archer et al., 2015) science capital questions.

Many individual questions within this category demonstrated significant relationships

with pro-environmental behavior, and the category mean was significant when measured

as a univariate or in multivariate analysis.

Other aspects of environmental science capital would include the people you

know who influence your views on environmental science and the daily engagement that

you have with environmental science. These two types of capital were indirectly assessed

using individual questions and the set of role models and meaningful nature experiences

questions. As previously explained, both the role models and meaningful nature

experiences question categories were not designed well to calculate meaningful category

means. Thus, qualitative methods were more helpful than quantitative methods when

analyzing the importance of these variables. Qualitative thematic analysis resulted in the

emergence of the following themes related to environmental science capital:

• Environmental clubs or groups provide meaningful experiences for

participants.

• Learning, both in school and outside of school, is a meaningful and enjoyable

experience.

• Environmental media is a source of meaning and inspiration for participants.

• Outdoor recreational activities such as hunting, fishing, camping, and hiking

connect participants to nature.

• Working outdoors and/or with animals connects participants to nature and

builds their identities and interests.

• Participants feel a sense of peace, solitude, calmness, and connectedness to

nature when in nature alone.

• Social experiences in nature are meaningful to participants because they can

bond and form memories with others.

• Unique close-encounters with nature inspire wonder in participants.

These themes describe activities or experiences that build the environmental

science capital of the participants in this study. From these themes, we can assume that it

is important that rural youth have access to environmental clubs, media, and educational

opportunities. Outdoor experiences are valuable to these participants, including outdoor

recreational activities and time spent working outdoors and/or with animals. Regular

access to both social experiences and nature experiences in solitude are important, but it

is also beneficial for youth to have occasional unique up-close encounters with nature.

Revised Conceptual Model

After our research, we revisited our conceptual model proposed in Chapter One

(Figure 5.1). The variables supported by the quantitative results and qualitative findings

remain while variables not supported by results and findings were removed. The variables

environmental science capital, environmental identity, STEM interest and political

identity (highlighted in purple) are supported through quantitative results and the

variables connectedness to nature, meaningful nature experiences, and role models

(highlighted in orange) are supported through qualitative findings. With the quantitative

findings, the numbers within the arrows represent the strength of the relationship based

on Pearson’s Correlation Coefficients. Since qualitative findings are not backed by

statistical tests, we cannot claim relationships, however, participant responses suggests a

relationship. This is shown by arrows outlined with dashed lines.

Figure 5.1. Revised Conceptual Framework Based on Study Findings.

Purple boxes represent findings from quantitative analysis and orange boxes represent

findings from qualitative analysis. Numbers within purple arrows indicate Pearson’s

Correlation Coefficient between the variable and pro-environmental behavior. The

relationship between green shapes is conceptual.

Variables marked with * have significance of p<0.0001.

Conclusions

Based on the quantitative results and qualitative findings, here are the conclusions

that address our research questions.

1. How does the concept of environmental science capital help to explain

proenvironmental behavior of rural youth in this study? The recent concept of science

capital is a theoretical lens to examine the differential patterns of aspiration and

educational participation of science among youth. Similarly, our proposed expanded

concept of environmental science capital can help explain the pro-environmental

behavior of rural youth in this study by shedding light on why this population engages

(does not engage) within the environmental sciences.

a. What is the relationship between environmental science capital and

proenvironmental behavior? Based on the quantitative results, there is a positive

relationship between environmental science capital and pro-environmental

behavior.

b. Which aspects of environmental science capital best predict pro-environmental

behavior? Quantitative results suggest that STEM interest and environmental

identity may be the best predictors of pro-environmental behavior. Although the

univariate analysis shows statistical significance for all six independent variables

in this study, the R2 values are highest for STEM interest and environmental

identity. Additionally, multivariate analysis shows that the greatest contribution

comes from STEM interest (0.308) then environmental identity (0.28844). This

suggests that as STEM interest and environmental identity increase, so does

proenvironmental behavior.

2. How do the following factors influence PEB in our sample populations?

• Meaningful Nature Experiences

The meaningful nature experience (MNE) mean score was significant with

PEB within the univariate analysis with a positive relationship between

MNE and PEB. Meaningful nature experiences were not significant in the

multivariate analysis with PEB.

• The Influence of Role Models

Influence of role models was significant in the univariate analysis,

showing a positive relationship with PEB, but was not significant in the

multivariate analysis.

• Connectedness to Nature (CNS)

Connectedness to nature (CNS) mean score was significant with PEB in

the univariate analysis with a positive relationship between CNS and

PEB. The CNS score was not significant in the multivariate analysis.

• Environmental Identity

Environmental identity was significant with PEB in both the univariate

analysis and the multivariate analysis. This relationship was positive and

accounted for 29% of the variance in the multivariate analysis making it

the second most significant variable in the multivariate analysis.

• STEM Interest

STEM interest was significant with PEB in both the univariate analysis

and the multivariate analysis. STEM interest was the greatest contributor

to PEB in the multivariate analysis at 31% of the variance accounted for

by STEM interest.

3. How do described experiences of freshman and sophomore college students enrolled in

science courses help to explain patterns observed in quantitative surveys? Quotes from

college students confirmed that this group of participants had high science identity and

interest as expected and indicated in quantitative surveys. However, some students

indicated a lack of pro-environmental behavior or environmental identity.

a. What personal success stories emerge from descriptions of rural college students

who have overcome barriers to environmental science capital and

proenvironmental behavior? They did not describe overcoming barriers to

proenvironmental behavior, but their responses provided insights into what those

barriers might be. For example, tensions between environmental identity and rural

identity were evident from participant responses. Past behaviors seem to also act

as a barrier to pro-environmental behavior.

b. What life experiences do students perceive as most important in shaping their

interests and environmental actions? What is the importance of role models?

Participants generally described their interests in science or nature as intrinsic

qualities that they possess. When they did mention activities or experiences that

influenced them, they mentioned solitary and social experiences in nature,

especially hunting, fishing, or working outdoors with animals. Often their role

model was a father figure who took them hunting or fishing.

c. What themes emerge in the lived experiences of students with different courses,

academic majors, or career plans? Qualitative findings are relatively consistent

across academic major or course, aside from the agriculture students who seemed

to stick out from the rest. They provided thoughtful responses to open-ended

survey questions that sounded quite pro-nature, despite their lower

proenvironmental behavior scores.

4. How do described experiences of high school STEM and environmental club

participants help to explain patterns observed in quantitative surveys? When

describing high school STEM and environmental club experiences: participants use

different language for different identities, express tension between their environmental

identity with other self- described identities, list individual actions opposed to

collective environmental actions and enjoy both solitary and shared nature experiences.

These described experiences add depth and “color” to the quantitative results.

a. How do their lived experiences and descriptions explain or contradict the findings

from the quantitative study? Participants’ self-described experiences relating to

STEM interest and environmental identity support the quantitative results of these

two variables having a significant relationship with pro-environmental behavior.

Although meaningful nature experiences and role models were not significant in

the multivariate analysis, participants did describe shared experiences and support

of friends and family. Due to this contradiction, we believe that the low

significance is due to the way that these two variables were measured in our

survey instrument.

Implications for Practitioners

Based on the quantitative results and qualitative findings, here are four implications for

practitioners which could include formal and non-formal educators, environmental and/or

science club leaders and community leaders.

Provide Frequent Experiences with Support from Parents and Peers

Research on science capital shows that daily experiences and support from parents

and peers can contribute to an increase in science aspirations and participation among

youth. This study supports that, similar to science capital, frequent experiences and

support from family and peers also increases aspirations and participation in

environmental science among this study population. These daily experiences of being a

part of environmental clubs and taking an environmental science class coupled with

shared experiences with family and friends contribute to making participating in

environmental sciences a norm. Our recommendation for practitioners is to include

parents/guardians, siblings, and influential others within your programming in order to

build this shared supportive culture.

Encourage Place-Related Connections

Another recommendation for practitioners is to emphasize sense of place into

educational programing. Sense of place incorporates both place attachment, how strongly

people are attracted towards places, and place meaning, the reasons for attraction. In fact,

Kudryavtsev, Stedman and Krasny (2012) posit that the strongest influence on

proenvironmental behavior via a place-related connection may be through a combination

of place attachment and ecological place meaning.

Provide Solitary Experiences

Shared experiences in nature are viewed as meaningful in past research and

supported in this research, however, many participants also mentioned spending time in

nature alone. For these participants, being alone in nature allowed them to disconnect and

to have a deeper more personal connection to nature. Perhaps this is due to the desire to

unplug from our ever-increasing busy lives. Our recommendation is to incorporate these

solitary experiences in educational programs such as setting aside time for self-reflection

in a solitary yet comfortable space.

Move Beyond Individual Behavior to Collective Behavior

During our focus groups, participants expressed more individual actions for the

environment such as not littering, recycling, and reducing energy and water consumption.

While this is encouraging, collective actions have more impact. Our recommendation is

to encourage and demonstrate more collective actions such as: addressing and engaging

in local environmental issues, voting for environmental policies and writing letters to

their representatives. One increasingly popular activity that has the potential to address

local community issues and build science/environmental engagement is citizen science,

also referred to as community science.

Ease Tensions with Environmental Identity

Within this research, we found that participants hold different identities

simultaneously, however, they use different language to describe these identities. For

example, when talking about being a “science” person, language that evokes “braininess”

and “logical” is often used versus when they express being a “nature” person. For a

“nature” person, they use language that suggests more “heart” and “warmth” such as

calm, happiness and harmony. Surprisingly, we found significant tension between

participants’ environmental identity with some other identities that they hold such as a

rural and/or political identity. Much like how the link between “braininess” and scientist

is a barrier, this tension between identities can prevent youth from engaging within the

environmental sciences and/or pro-environmental behaviors. Solutions to this challenge is

beyond the scope of this study. However, embracing and demonstrating a “both/and”

mindset and setting clear ground rules of respect can help set the tone within clubs and

classes.

Acknowledgement of Limitations

This study was limited by the following:

• The results of this study cannot be generalized beyond the study population

since sampling was not random.

• There was a lack of respondents with low socioeconomic status and racial

diversity so the effects of race and socioeconomic class could not be

studied.

• While every effort was made to replicate survey questions from published

survey instruments, some questions were modified for our population. Our

survey combined questions from different survey instruments and it may be

that the reliability and validity of the original survey instruments was

impacted.

• The ages of our respondents were typically older than the respondents of

the published science capital research. This discrepancy may have an

impact on comparisons of our findings to the previous science capital

research.

• A more robust connectedness to nature scale may have produced different

results. Due to the desire to keep the number of questions manageable, the

shorter connectedness to nature scale was used. More published research

exists using the longer new ecological paradigm (NEP) scale and it is

possible that a different measure of connectedness to nature would have

yielded different outcomes.

• As with any self-reported survey, respondents may answer questions in a

way that is not truthful but makes them look more favorable. There is also

the possibility that some question meanings were not clear and the

information collected was not valid.

• The quantitative data set is large and many more relationships from this

data have not been thoroughly studied due to time constraints.

Future Research

Based on the findings of this study, future research might include a study that

looks at how the factors we examined (STEM interest, environmental identity,

socioeconomic status, political identity, geographic region, role models, meaningful

nature experiences, and connectedness to nature) influence the pro-environmental

behavior of a random sample of American youth. This would allow for generalization of

these results to better examine environmental science capital in American youth.

Research might also examine the new environmental paradigm scale to assess an

ecocentric worldview in opposition to the connectedness to nature scale. We propose this

might lead to a greater understanding of the differences we discovered between those that

hunt as opposed to those individuals that fish when looking at pro-environmental

behavior.

Future research could explore how practitioners in formal and informal education

could emphasize collective pro-environmental behavior (i.e. addressing and engaging in

local environmental issues, voting for environmental policies and writing letters to their

representatives) in an effort to assess the impact of these collective behaviors on

environmental science capital and pro-environmental behavior.

Another issue that could be explored is the different identities that were observed

in this study. We found tension between the different identities held by rural youth

(scientist, nature person, outdoors person and environmentalist) and the other identities

they hold such as rural or political party member. Research into effective strategies to

break the stereotypes of "brainy scientists" and "environmental activist" could increase

the environmental science capital and pro-environmental behavior of American youth.

Rural identity is of particular interest because defining rural for this study was a

far more complicated subject than it appears on the surface. The popular definition of

rural put forth by the US Census Bureau is quantitative in nature and defines rural in

terms of the number of people in a given area but by this definition, our survey

respondents "misidentified" their geographic residence as "rural" 22% of the time and

over half (53%) incorrectly identified their place of residence as rural or urban.

If respondents believe they live in a rural area, are they rural? This is the question

brought to light by discussions for this research. Rural is not easy to define and educators,

legislatures, and other policy makers have struggled with this question of "What is rural?"

for decades (Rios, 1988). Rural means different things to different people and doesn't

look the same in all parts of the country (Rios, 1988). Someone from "rural" Kansas does

not live the same experience as someone from "rural" West Virginia, yet people from both

areas feel what it is to be rural. Some qualitative features of past definitions of rural have

been simple life, agricultural lifestyle, smallness, homogeneity, and even dullness, but

according to Blakely (1984), these also fail to describe much of rural America.

Though time prevented us from probing deeper into this topic of ruralness, it is

our belief from this study that the definition of rural must include both qualitative and

quantitative elements. The belief that a person lives in a rural area impacts their

viewpoints just as firmly as if they live in a rural area by zip code. Rural encompasses a

mindset and lived experiences as well as a person's physical location. For our study, we

concluded that the self-identified designation of rural spoke more of the mindset of being

rural and probably gave us a better insight into what the participant believed than just

using the Census Bureau definition. Rural, we believe, is an identity and it is likely that a

rural identity should be explored in future research instead of looking strictly at

geographic location.

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