Deriving Core Principles of Social Ecology

Deriving Core Principles of Social Ecology

3

Chapter 2 traced historical developments in ecological research from its roots in biology to more recent conceptions of social ecology and the global ecosystem. Each branch of ecology addresses a unique set of concerns—from bioecologists’ emphasis on the struggle for existence in plant and animal biomes, and human ecologists’ interest in the spatial distribution of health problems in urban areas, to social ecologists’ studies of people’s relations with their physical, biological, sociocultural, and virtual surroundings. The focus of this book is not on the substantive differences between these schools of ecology but rather on the assumptions they share. In this chapter, I identify core principles of social ecology and show how they can be applied to gain a broader understanding of people’s relationships with their environments. A basic assumption in all branches of ecology is that organisms’ encounters with their surroundings are influenced by contextual factors. The greater the diversity of aquatic species found in a marine ecosystem, for example, the better the chances that members of each species will survive and thrive in that environment [1,2]. In human communities, people’s reactions to high population density are influenced by cultural norms for dealing with crowding in public and private places [3,4]. Rather than focus exclusively on a narrow set of predictor and outcome variables such as high population density and its impact on behavior, ecologists consider how the relationships among them change across different contexts—for instance, how people’s responses to density vary across particular cultures and settings (e.g., libraries, athletic events, social gatherings). My own awareness of how people alter their behavior across different situations dates back to my middle school years when I began to navigate the sometimes rocky shoals of adolescent friendships. I especially recall one Sunday afternoon when I was in eighth grade and living in Miami. On weekends, I often rode my bike over to the athletic field at an elementary school near my home where I would join in touch football pickup games. On that afternoon, I pedaled to the field, locked my bike, and climbed over the padlocked fence. As I jumped onto the field, I spotted a group of about 15 guys mostly my age. A few of them were classmates at my middle school, others I did not recognize and were probably in high school. Aside from them, there was no one else on the field that day. I walked over to the group to see if they were interested in organizing a football game. As I approached them, Robert who sat in front of me in algebra class walked over and threw a punch at me. It was a halfhearted punch that glanced off of my upper arm. This turn of events took me by surprise—I did not know him well, but I had never had any hassles with Robert before. I threw an obligatory punch back and then shouted at him about what he was doing as he stood blank-faced and said nothing. I also turned to the others in the group and confronted them for goading Robert into picking a fight with me Social Ecology in the Digital Age. http://dx.doi.org/10.1016/B978-0-12-803113-1.00003-X Copyright © 2018 Elsevier Inc. All rights reserved.

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while they stood around silently, looking on. I was outnumbered and could have been pummeled by the whole group but I felt compelled to confront them. There was no further physical contact between Robert, me, or anyone else in the group. I walked back to the fence, climbed over it, and rode my bike home, perplexed by the whole experience. Besides being taken aback by my encounter with Robert and his buddies at the schoolyard, I wondered why someone who otherwise seemed like an easygoing guy at school would suddenly pick a fight with me in a different setting. I saw Robert in class the next day and asked him about what had happened that weekend. He explained that he was “pledging” a fraternity for middle and high school students, and that one of the tasks assigned to him by club members was to start a fight with me when I approached the group. He said he was under pressure from them to walk over to me and throw a punch, but that I should not think much more about it given the circumstances. We got along fine after that, but I continued to think about the incident after it occurred. Was there something about Robert’s personality that might explain his behavior at the schoolyard? Would other classmates have acted similarly in that situation when coaxed by friends to pick a fight with an outsider? I also questioned whether Robert would have thrown a punch at me in a more public place, such as during algebra or gym class at school with some adults and other classmates around. And I pondered what would lead someone to act friendly in one setting and unexpectedly hostile in another. My experience with Robert and his friends at the schoolyard was not as dramatic as the violent confrontations we sometimes hear about in local and national news reports. No actual fight broke out between us, and no one was physically injured. The event lasted for at most 30 minutes from the time I arrived at and left the field. Yet the schoolyard incident turned out to be a memorable event for me as it forced me to confront the reality that a person’s behavior can vary dramatically across different settings. Adolescents are certainly impressionable when it comes to their relations with peers and, indeed, this event stuck with me well beyond my teen years. In fact, I suspect that it influenced the development of my values about group interactions with outsiders and possibly even my decision to pursue graduate studies in social psychology several years later. From my middle school and high school years onward, I have been intrigued by the pervasive impact of environments on behavior. Whether it was studying the effects of ambient temperature and lighting on sea anemones during my high school years or observing my own reactions to starkly different neighborhoods as I moved from Miami to Chicago for college, I became increasingly interested in learning about the effects of physical and social environments on behavior. My fascination with people’s responses to their surroundings grew as I took courses in human ecology during college and graduate school and later on after I joined the faculty in Social Ecology at University of California, Irvine (UCI). During my years at UCI, I have organized much of my research and teaching efforts around a core concern of social ecology—namely, the study of environmental contexts and how they affect behavior and well-being.

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From Isolating Variables to Putting Them in Context My graduate training at the University of North Carolina (UNC) was in social psychology. My interests in ecology and urban environments also led me to take minors in Sociology, City Planning, and to work with faculty in Public Health but for the most part, my coursework at UNC focused on theories and methods of social psychology. My classes covered the fundamentals of group dynamics, attitude change, social cognition, and extolled the benefits of laboratory experiments as the most rigorous method for studying social behavior. When conducting experiments, efforts are made to isolate the main predictor and outcome variables from other situational and personal factors. Participants are randomly assigned to different conditions to lower the likelihood that unknown outside factors such as personal differences among study participants will change the relationships between independent (predictor) and dependent (outcome) variables [5]. Lab experiments are preferred by social psychologists over alternative research designs (such as nonexperimental case studies) because of their greater power to test causal links between independent and dependent variables. While taking courses and doing research in social psychology, I also spent time reading books and articles about environmental and ecological psychology—newly emerging areas of research during the 1970s [6–11]. I was interested in learning more about how psychological research could help remedy problems such as urban crowding, environmental pollution, poverty, and crime. I liked the fact that environmental psychologists looked at people’s reactions to their physical as well as social surroundings, and how these different facets of environments affect behavior and well-being. I also resonated with environmental psychology’s interdisciplinary approach and its integration of behavioral science with other fields such as architecture, urban planning, geography, and sociology. Rather than relying solely on laboratory experiments, environmental psychologists also use case studies, archival data, community surveys, and quasi-experimental studies conducted in naturalistic field settings to gain insights about people’s relationships with their surroundings. The elective courses I took in sociology, planning, and public health at UNC piqued my interests in interdisciplinary research. In fact, my hybrid academic interests are largely what led me to choose a faculty appointment in Social Ecology at UCI over a more traditional job in social psychology when I graduated from UNC. Even so, when I first arrived at UCI I identified myself mainly as an experimental social psychologist with strong interests in environmental psychology, rather than as a “social ecologist.” In retrospect, my understanding of and identification with the field of social ecology evolved gradually over several years through a series of research projects I conducted at UCI described below.

Taking Environmental Stress Research From the Laboratory to the Freeway My move from North Carolina to Southern California presented ample opportunities to study the behavioral effects of urban stressors “in the wild.” I was eager to take the research methods I had learned in graduate school and apply them to the study

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of environmental problems in naturalistic settings. One such problem I encountered firsthand after arriving in Irvine was rush hour traffic on California’s freeways. Roadway congestion is an environmental stressor to the extent that it strains a person’s adaptive capacity [12,13]. By slowing down one’s movement between two or more places, congested traffic can provoke negative emotions, behaviors, and physiological responses. Earlier lab studies by Glass and Singer [14] and Sherrod [15] showed that the adverse effects of stressors such as noise and high density are greater when individuals perceive them to be uncontrollable or unpredictable. Negative stimuli that are unpredictable in their onset or beyond one’s control evoke greater stress and are more threatening than those that are predictable or, even better, controllable [16,17]. Much of the research on environmental stress at that time was based on experiments conducted in laboratories, but there were few published studies of people’s encounters with stressors in naturalistic settings [18]. My UCI colleague and fellow psychologist, Raymond Novaco, graduate student, Joan Campbell, my wife and clinical psychologist, Jeanne Stokols, and I decided to conduct a field experiment to test the links between commuters’ experience of traffic congestion and their physiological and psychological stress [19]. We approached two large companies in Irvine about our study, and each authorized their employees to participate in the research. We recruited 100 volunteers from the two firms and sorted them into groups ranging from higher to lower commuting distances and times. We referred to these as the high, intermediate, and low-impedance groups. We wanted to know how chronic exposure to different levels of congestion affects commuters’ emotions, blood pressure, and task performance, so we did what experimental psychologists usually do—we selected our independent variables, distance and duration of the rush hour commute, and designed a study to test how they affect dependent measures such as drivers’ mood, physiology, and performance. All of the employees in our sample (61 males and 39 females) worked the day shift, were solo commuters (rather than ride-sharers), and had traveled the same route to and from work for  eight months or more. We of course were not able to assign commuters randomly to high or low distance/ duration commutes—they already had decided to live close to or far from their workplace. So we did the next “best” thing—we conducted a quasi-experiment that included naturalistic comparison groups (based on individuals’ commuting distances and times) but without randomly assigning participants to those conditions [20]. We also administered a personality measure of the Type A Coronary-Prone Behavior Pattern [21,22] and gathered other demographic data to control for personal differences that might affect the outcome measures. The study was designed to isolate the major independent and dependent variables and examine their links while controlling statistically for possible effects of other factors (like commuters’ residential and work experiences, vehicle size) on the findings. We predicted that the most negative ratings of mood, highest blood pressure levels, and poorest task performance would be found in the high-impedance group—and that moderate and low levels of stress would be exhibited by the medium and low-impedance commuters, respectively. We also expected that high-impedance Type A commuters would have the highest blood pressure and least tolerance for frustration on tasks, given their greater sense of time urgency compared to the Type B (noncoronary-prone) drivers [22].

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Figure 3.1  Photo of Novaco and Stokols testing a commuter’s blood pressure in the company parking lot.

Assignment of participants to the low, medium, and high-impedance groups was based on their reported commuting distances and times.1 We contacted participants by mail to let them know they had been selected for the study and also sent them some background and personality questionnaires to fill out, including the Jenkins Activity Survey (JAS), a measure of the Type A behavior pattern [21]. Individuals were classified as either Type A or Type B based on their JAS scores. A few weeks later, they were contacted by phone to schedule their participation times. Each person participated in the study for  one week and completed daily commuting logs reporting the actual distances and times traveled each day and subjective impressions of the journey (e.g., perceived traffic congestion, temperature inside the vehicle). These logs were completed on arrival at work and at home for the morning and afternoon commutes. When participants arrived at work on Monday, Wednesday, and Friday, they drove to a testing station in the parking lot where we recorded their systolic and diastolic blood pressures using an electronic recorder (see Fig. 3.1). On Tuesday and Thursday of the testing week, they reported to a company conference room about an hour and a half after arriving at work where they completed two brief tasks to assess their performance speed and accuracy [23,24]. Our initial analyses showed that greater commuting distance, time, and months on the commute were correlated with higher systolic and diastolic blood pressure. These results supported our assumptions about the stressful effects of traffic congestion, 1 Individuals

in the bottom 25% of both distributions were assigned to the low-impedance group. The 27 persons in this group traveled less than 7.5 miles between their homes and workplaces and spent less than 12.5 minutes on the road in either direction. The medium-impedance group included 22 individuals in the middle 30% of the distributions for distance and time who traveled between 10 and 14 miles between home and work and spent approximately 17–20 minutes on the road each way. The high-impedance participants were those in the top 25% of the distance and time distributions. These 30 individuals drove between 18 and 30 miles between home and work and spent from 30 to 75 minutes each way.

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measured in terms of rush hour commuting distance and time. We then tested the separate and interactive effects of travel impedance and the Coronary-Prone Behavior Pattern. Those analyses included the high, medium, and low-impedance commuters who had correspondent rankings on the distance and time distributions.2 We found that high-impedance drivers rated themselves as more tense and nervous than low-­ impedance commuters.3 Also, unexpected interaction effects of traffic impedance and personality revealed that Type B individuals in the high-impedance group had higher blood pressure than the Type A’s in that condition. As well, tolerance for frustration on tasks was lowest among high-impedance Type B commuters and greatest among the Type A’s in that group. These results challenged our assumption that Type A’s would be more stressed by high-impedance travel than the easier going Type B’s. To better understand the higher stress levels of Type B’s compared to Type A commuters in the high-impedance group, we decided to look beyond the distance and duration of the commute and examine the survey data we had gathered about participants’ home and work situations. Interestingly, we found that Type B individuals in the high-impedance group reported the least choice in deciding to live far from their workplace among all participants in the study and, like other Type B’s, were less involved with their jobs than Type A’s. These characteristics of Type B’s may have made them more sensitive to traffic congestion and less able to cope with the strains of commuting between a workplace and residence they viewed as uninvolving and constraining, respectively. Only by considering commuters’ ratings of their home and work environments were we able to detect their influence in moderating the effects of commuting distance and time on blood pressure and performance. Our quasi-experimental study of commuting stress generated more questions than it answered. Because participants were distributed nonrandomly across the different impedance groups, we could not determine whether travel distance and time in combination with their personality traits caused the observed variations in commuters’ stress responses. It was possible, for example, that employees in the high-impedance group were more stressed to begin with regardless of their exposure to rush hour congestion over several months. Yet, quasi-experimental field studies have some advantages over “true” experiments conducted in laboratories where participants are randomly assigned to treatment groups. Instead of narrowly isolating major independent and dependent variables from the naturalistic environments in which they are observed, field studies can adopt a more expansive approach by considering how the focal variables (in this case, travel impedance and stress) are embedded in broader facets of a person’s life situation [25]. Rather than screening out seemingly unimportant contextual factors from their research, field investigators often collect supplemental information (e.g., about situational and personal factors) to help explain the links between major independent 2 A

subset of the sample included other individuals who had noncorrespondent rankings on the distance and time distributions. They were assigned to the low distance/medium time and medium distance/high time groups. Data from these groups were combined with those from the other three groups in some of our analyses. 3 In these multivariate analyses of variance, measures of participants' age, weight, gender, number of months on the route (ranging from 8 to 86 months), and educational level were included to control for the effects of these variables on the results.

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and dependent variables. These data are not only used to statistically control for preexisting differences between comparison groups but they also offer opportunities to learn about how the target variables are influenced by contextual factors. By including measures of employees’ residential and job situations in our study, we arrived at a broader ecological view of commuting and stress in which a person’s transportation, home, and work environments are connected in space and time as part of a daily activity system [26–28]. These life domains are also psychologically linked as part of the individual’s perceived life situation [25,29]. Guided by this ecological approach, we conducted a follow-up study 18 months after our initial investigation with 82 individuals from the original research [30]. For the follow-up study we added a measure of subjective impedance (along with our objective measures of commuting distance and time) to obtain more information about drivers’ reactions to their trips to and from work (for instance, perceived unpleasantness of the commute, frequency of braking during morning and evening drives). We also gathered more data about commuters’ satisfaction with home and work, and their efforts to cope with the hassles of travel impedance (e.g., whether or not they had changed residence since the first study to lower their commuting stress). Our second study showed that the effects of one environmental domain (e.g., commuting) can spill over to other contexts (e.g., home and work) either positively or negatively. For example, we found that the experience of rush hour travel was linked to negative mood at home in the evening, and that physical impedance (commute distance and time) were linked to more frequent illness-related absences from work. These findings indicated that both commuters and work organizations were sustaining hidden costs associated with high-impedance computing. Reflecting back on our commuting studies, it is clear that they contributed in important ways to my development of a social ecological perspective. First, our findings underscored the multifaceted nature of people’s everyday surroundings, consisting of interconnected behavior settings and life domains. In trying to understand the impact of road congestion on commuting stress, we learned more about the links between these variables by viewing them in relation to nontravel domains (i.e., residential and workplace conditions) than by treating driving distance and time as separate from those settings. We discovered that the effects of commuting on behavior and stress depend on contextual factors such as employees’ involvement in their jobs and their decision to live near or far from work. We came to view the commuting environment as part of a person’s broader activity system that also includes residential, employment, recreational, and other domains [25–28,31]. Fig. 3.2 shows the hypothetical activity system of a person consisting of his or her dwelling, workplace, the distance traveled between home and work each day, and a bank and post office frequented from time to time [32]. This diagram illustrates how different life domains (e.g., home, travel, and work) are intertwined with each other and jointly influence a person’s health and behavior. Our research also suggested the value of describing individuals’ home, travel, and work environments in subjective as well as objective terms. We found that the effects of long-distance driving (an objective feature of one’s commute) are not uniform across employees and instead depend on commuters’ subjective reactions to their home and work situations. Also, individuals’ perceptions of commuting hassles were linked to their illness-related absences from work. These data underscore the importance of

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Figure 3.2  Diagram of a hypothetical person’s activity system. From Lenntorp B. A time-geographic simulation model of individual activity programmes. In: Carlstein T, Parkes D, Thrift N, editors. Human activity and time geography. New York: John Wiley & Sons; 1978. p. 162–80.

measuring both objective and subjective features of environments when studying their impact on behavior and well-being. Our follow-up study further showed that people’s relationships with their everyday environments are reciprocal and transactional [33]. Environments influence individuals and they, in turn, try to change their surroundings to better fit their needs [10,34]. We discovered, for example, that some long-distance commuters from our first study had moved closer to their workplace by the time we began the follow-up investigation (18 months later) to reduce the strains of rush hour traffic. By tracking participants over a two-year period, we extended the temporal scope of our research and were able to discover some of the steps that commuters took to cope more effectively with the constraints of their residential, travel, and work environments.

Effects of Chronic Exposure to Aircraft Noise on Children Besides studying traffic congestion in Southern California, I participated in another field study of environmental stress during my early years at UCI. This second project reinforced many of the same points about the value of a social ecological perspective that came up in our field investigation of commuting and stress. Like the earlier study, this one also was designed as a quasi-experiment (with self-selection of participants into the major comparison groups) to test the effects of an environmental stressor, aircraft noise, on health and behavior. The initial prompt for the research was a Los Angeles Times report in the 1970s on architectural modifications (e.g., acoustical treatments of ceilings and walls) that were planned for noise-impacted schools located near the Los Angeles (LA) International Airport. After reading the article, my UCI colleague, Gary Evans, fellow environmental psychologists, Sheldon Cohen and David Krantz (then at the University of Oregon and the University of Southern California), and I decided to meet with several elementary school principals in LA to explore the possibility of their staff and students participating in a study of children’s response to chronic noise exposure. Most of the administrators we contacted agreed to have their schools participate in the study. We

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Figure 3.3  Airplane landing over a Los Angeles Elementary School.

then obtained research funding enabling us to acquire and remodel a travel trailer that we used as a mobile testing station. Between the spring of 1977 and 1978, the research team drove to the participating schools where five groups of five children each were tested in the trailer throughout the day. The trailer was retrofitted to be soundproof so that children attending noisy schools could complete various research activities in a quiet environment (like those attending schools located further from the airport), free of distraction from the jet aircraft flying overhead on their approach to the runway a few miles away (see Figs. 3.3 and 3.4). As in the traffic congestion study described earlier, we were not able to randomly assign participants to the experimental groups. For obvious practical and ethical reasons, we could not require children to live and go to school near a major airport (with more than 300 overflights per day—approximately one flight every two and a half minutes during school hours) or in quieter areas of the city. Parents of the participating children had self-assigned themselves and their families to these different environments. So, we conducted a quasi-experiment with statistical control measures in which a sample of third and fourth graders at the four noisiest elementary schools near the LA International Airport were compared with those attending quieter schools in a different part of the city (see Fig. 3.5).4 We tried to ensure that participants were 4  The

study included children from all noise-impacted third and fourth grade classrooms at each noise school as well as children from an equivalent number of classrooms in quiet schools. Eighty-seven percent of the students in these classrooms received parental consent to participate in the study. A total of 262 children (142 and 120 from the noise and quiet classrooms) participated in the first phase of the study, and 163 of these students (83 from noisy and 80 from quiet classrooms) participated again during the one-year follow-up phase of the research.

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Figure 3.4  Child completing cognitive task in sound-insulated trailer.

Pacific Ocean

LOS ANGELES INTERNATIONAL AIRPORT

Runway Flight Path

Elementary School

Figure 3.5  Location of participating elementary schools near the Los Angeles International Airport. From Cohen S, Evans GW, Stokols D, Krantz DS. Behavior, health, and environmental stress. New York: Plenum Press; 1986.

similar to each other on as many criteria as possible, except for the acoustic differences between noisy and quiet schools. Using data provided by the school districts and 1970 Census records, we identified three quiet comparison schools in LA that were closely matched to the noisy schools on key demographic variables [35].5 5 Criteria

used to match the quiet and noisy schools included the ethnic and racial composition of children at each school, the occupations and education levels of their parents, and the percentages of children whose parents received assistance under the Aid to Families with Dependent Children program.

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Earlier studies found that adults exposed to uncontrollable noise adapted to the unwanted sound in the short run, but later displayed negative aftereffects, or delayed stress symptoms such as lower tolerance for frustration and diminished task performance that occurred once they moved from a noisy room to a quiet one [14]. Prior studies also showed that exposure to loud noise raises blood pressure and heart rate [36,37] but had not extended those findings from adults in noisy laboratory and field settings to children in school environments. Our study addressed those issues by testing the effects of school noise exposure on children’s blood pressure levels, accuracy and persistence on difficult tasks, and achievement scores for reading and math. Because we wanted to measure the chronic effects of noise that persist after leaving a noisy setting, we tested children in a soundproof environment. A subset of the children in our study participated at two time points—during the spring of 1977 before sound abatement materials were installed in the noisy schools that summer, and during spring 1978 after the architectural changes were made. This enabled us to test whether sound abatement treatments lowered blood pressure levels in the noise-school children compared to those observed in the previous year. Our initial analyses tested the effects of school noise on children’s health, task performance, and academic achievement. To confirm that the sound levels at schools inside and outside the airport’s noise contours were different, we measured the average (and peak) decibels in each of the participating classrooms using noise-analyzer machines.6 These data were gathered during two different phases of the study and corroborated the higher classroom noise levels at schools near the LA International Airport compared to those in quieter areas.7 Other characteristics of students and their families that might affect children’s blood pressure, task performance, and school achievement scores (aside from the noisiness of their classrooms) were included in our analyses [35].8,9 Analyses from the first phase of the study showed significant effects of school noise on children’s blood pressure. Third and fourth graders attending schools near the airport had higher systolic and diastolic blood pressures than those from quieter schools.10 These differences were greatest among students who had been enrolled in their schools for less than 2 years (see Fig. 3.6). The higher average blood pressures observed among students at noisy schools did not exceed normative levels for children 6 These

one-hour testing sessions occurred during morning and afternoon times when children were absent from the classroom. 7 The mean peak decibel (dBa) noise levels gathered during the second testing session of the longitudinal study (in 1978) were 91.50, 71.27, and 74.42 for the noisy, noise-abated, and quiet classrooms. The average decibel levels (LEQ) observed in the same classrooms were 70.29, 62.82, and 62.05, respectively. 8 All data analyses included statistical controls for the child's family size, grade in school, months enrolled in the school, and race. A measure of children's body size (weight/height3) also was included in the blood pressure analyses. These statistical covariates were entered into the regression equations first, followed by the noise-quiet predictor variable, and then an interaction term for noise level x months enrolled in school. 9 Students whose parents authorized them to participate in the study completed an initial hearing test. Six percent of the eligible noise-school children and seven percent of the quiet-school children failed the test and were excluded from the study to minimize possible effects of auditory impairment on the findings. 10  Children's blood pressure levels were measured on two different days using a Physiometrics automated recorder. Blood pressure data are based on the mean systolic and diastolic pressures for the two measurements.

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BLOOD PRESSURE (mm Hg)

60

92 91 90 89 88 87 86 85 84 83 82

NOISE (SYSTOLIC) QUIET (SYSTOLIC)

49 48 47 46 45 44 43

NOISE (DIASTOLIC) QUIET (DIASTOLIC)

less than 2

2–3.5

3.5–4

4 or more

YEARS EXPOSURE (enrolled in school)

Figure 3.6  Blood pressure levels for children in noisy and quiet schools. From Cohen S, Evans GW, Stokols D, Krantz DS. Behavior, health, and environmental stress. New York: Plenum Press; 1986.

of similar ages [38]. It is not known whether chronic noise exposure during childhood is a risk factor for adult hypertension [39]. In the second phase of the study, we retested the students who remained in the sample after they participated in the first testing session a year earlier. The retest sample was smaller than the first (163 children compared to the original 262) because several students changed schools prior to the second testing session. Analyses of the data from students tested at both time points enabled us to measure the effects of school noise on blood pressure and other outcomes over time, and whether the classroom sound abatement treatments (installed midway between the first and second testing sessions) had any effects on the students. We found no effects of the noise abatement strategies on blood pressure among children assigned to abated classrooms after the first testing session. Also, noise-related differences in blood pressure at time 1 were no longer evident by the second testing session. Further analyses showed that children with the highest blood pressures at time 1, especially among those at the noisy schools, were more likely to leave the study by the second testing session than those with lower pressures initially. The attrition of students with higher pressures after they participated at time 1 may explain the lack of blood pressure differences between the noise and quiet-school children remaining in the study at time 2. Along with measuring students’ blood pressure levels, we tested their puzzle solving and proofreading performance using tasks designed for third and fourth graders. We also tested for noise effects on children’s math and reading achievement scores. The task performance and achievement test findings for the longitudinal and replication

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studies are described in detail elsewhere [35,40,41]. Generally, exposure to aircraft noise at school, in and of itself, had little or no effect on school achievement in math and reading. However, noise-school children were more likely to fail, give up on, or take longer to solve a series of challenging puzzles than those from quieter schools. The analyses mentioned thus far focused on the main predictor and outcome variables while controlling for other factors (such as individual and family characteristics) that could affect children’s health and behavior. As we poured over the results of these analyses, we became more interested in exploring the possible influence of contextual variables on students’ reactions to school noise. We wondered whether students’ sensitivity to airplane noise at school might be moderated by other circumstances in their lives such as the noisiness and spatial density of their homes and the number of windows and density levels in their classrooms. Perhaps the stressfulness of loud aircraft at school might be compounded by children’s exposure to noise at home and by high density in those environments. We decided to explore these issues further by testing for interaction effects between school noise and other features of children’s classrooms and homes. At the beginning of the study, we had recorded the number of windows in students’ classrooms and asked teachers to complete a survey about their experiences teaching in those rooms. We also asked children to rate the noisiness and other qualities of their classrooms. We included these data in a new set of analyses along with information about children’s homes that we obtained from a background questionnaire completed by parents early in the study. Home noise levels were recorded for all noise-school children based on the location of their residences relative to the LA International Airport.11 By broadening the scope of our analyses, we were able to identify contextual influences on children’s reactions to school noise that had been overlooked earlier. First, we found that among children attending noisy schools, a larger number of classroom windows were associated with lower accuracy and speed on the proofreading task.12 For the children at quiet schools, the number of windows in their classroom was not linked to any performance differences. These findings suggest that a larger number of classroom windows may increase students’ sensitivity to aircraft noise at school and lower their performance on cognitive tasks. Second, although there were no effects of classroom density on any of the outcome measures, higher levels of residential density were related to poorer reading and math achievement scores and ability to discriminate between similar sounding words among both noise and quiet school children.13 11 Archival

information for home noise levels was available only for those children attending schools adjacent to the LA International Airport. The location of children's homes within the Community Noise Equivalent Level (CNEL) contours surrounding the airport determined each child's home noise score. Children's assignment to the low or high level of home noise was based on a median split of the residential CNEL scores. The median level of home noise for children attending the noise schools was 76 CNEL for the time 1 sample and 74 CNEL for the time 2 sample. 12 In these analyses, children whose classrooms had 0–4 windows were compared with those whose classrooms had 5–9 windows. 13 Classroom density was computed by dividing the number of students assigned to each classroom by the square footage of the room. Residential density was computed by dividing the number of children in each household by the number of rooms in the home. We hypothesized that higher levels of classroom and residential density would heighten students' sensitivity to school noise.

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Third, among the noise-school children, those living in noisier homes (relative to the airport’s noise contours) had lower reading achievement scores. Also, among children from quieter homes, those assigned to noise-abated classrooms had higher reading scores than those assigned to nonabated rooms after the first testing session. Fourth, noise-school children assigned to the abated classrooms were faster and more accurate on cognitive tasks than those assigned to nonabated classrooms. Finally, students’ and teachers’ subjective ratings of their classrooms predicted children’s blood pressure and task performance, even after controlling for actual levels of school noise. Children who rated the noise in their classroom as more bothersome had higher diastolic blood pressures, after controlling for objective measures of noise. Also, children whose teachers reported fewer noise-related interruptions in the classroom were more accurate proofreaders and made fewer auditory discrimination errors after controlling for objective noise levels.

Lessons Learned From Field Studies of Environmental Stress As in the study of rush hour commuting described earlier, analyses of the noise data shifted our attention from isolated independent and dependent variables toward a broader investigation of how the predictor variables and certain contextual factors jointly influence behavior. When we analyzed the combined effects of classroom and residential settings, we found that several features of those environments affect children’s responses to school noise. Task performance was poorer among children at noisy schools if they were assigned to classrooms with a large number of windows, but this aspect of classroom design was inconsequential for students at the quieter schools. By analyzing school noise in relation to the number of windows in classrooms, we discovered features of learning environments that accentuate the negative impacts of noise exposure. Also, when viewed in isolation from contextual factors, noise abatement strategies had no significant effects on children’s health or behavior. However, when considered in relation to the noisiness of children’s homes, noise-abated classrooms were associated with better task performance and reading ability for students living further away from the airport compared to their classmates from noisier homes. And although classroom density had no effects on students, high levels of residential density were linked to lower reading and math achievement scores at school, reflecting the impact of home environments on children’s academic performance. Taken together, the noise and commuting studies underscore the value of a contextual approach when studying environmental stress in adults and children. Both showed that the behavioral and health effects of environmental conditions (e.g., rush hour commuting, aircraft noise at school) depend on the broader contexts surrounding them. They also revealed the power of individuals’ perceptions in determining their reactions to the environment. Students’ and teachers’ reports of noise-related disruptions in their classrooms were linked to poorer task performance and higher blood pressure among third and fourth graders, even after controlling for actual noise levels. And just as harried commuters were more likely to move closer to work in the months following our initial testing session, children with the highest blood pressures at noisy schools were most likely to leave their school after the first phase of the study.

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The reasons that students left the noise-impacted schools are not conclusive, but one possibility is that parents of students with the highest blood pressures moved to a quieter school district to help alleviate their children’s stress.

Toward a More Systematic Approach to Social Ecology It took several years after arriving at UCI for me to realize that the contextual approach developed in our studies of traffic congestion and noise paralleled the principles of ecological analysis I learned in college and graduate school. Our contextual awareness grew gradually through exploratory analyses of commuting and noise-related stress. My involvement in those studies led me refamiliarize myself with the literature in biological and human ecology. I became interested in developing guidelines for contextual research that I could apply in future studies. In effect, I wanted to develop a more systematic ecological approach for framing hypotheses about contextual influences on health and behavior. Social ecology, as I have come to view it through my research and teaching, is a transdisciplinary field organized around certain descriptive, explanatory, and transformative goals. One goal of social ecology is to describe the structure of human environments, especially their natural, built, social, and virtual features, and the processes by which these different parts of our surroundings change over time and influence each other (for instance, the ways that new digital technologies are reshaping urban design, patterns of social interaction, and environmental sustainability). Social ecology also is concerned with identifying potential health, behavioral, social, and sustainability outcomes of environmental structure and change. In addition to describing environments and their effects on populations and ecosystems, social ecology strives to explain and predict how environmental circumstances in particular places and times affect specific individuals and groups. Explanatory models linking environmental conditions to various health, behavioral, and sustainability criteria are applicable to both micro- and macrolevel phenomena. For example, the studies of commuting stress and school noise described earlier examined microscale settings (e.g., schools, workplaces, and commuting routes in Southern California during the 1970s), whereas models of current Internet use and its projected impact on electricity consumption worldwide in the coming decades exemplify a more macro perspective [42,43]. The conceptual principles of social ecology presented in this book can be used to understand any facet of people’s relationships with their surroundings, from micro- to macroscales and over brief or extended time frames. The particular concepts, theories, and methods included in analyses of human environments depend on the phenomena under study. The topic of global sustainability, as one example, can be analyzed from the vantage point of political and regulatory models of environmental protection [44,45]. At the same time, efforts to persuade individuals and organizations to adopt more sustainable behaviors (e.g., curbing personal and collective energy use in households, workplaces, and hotels) can be made more effective by drawing on psychological theories of social influence, attitude formation, and behavior change [46,47].

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Finally, the descriptive and explanatory contributions of social ecology often are used to modify existing environmental conditions in ways that help mitigate mental and physical illness, poverty, unequal access to educational and economic resources, interpersonal violence, and war. These transformative goals of social ecology reflect its abiding commitment to action-oriented, translational research. Some fields closely allied with social ecology, such as translational medicine and sustainability science, promote transformative research directed not only toward scientific discovery but also societal improvement [48,49]. Similarly, the term transformational ecology refers to environmental science carried out collaboratively by scholars and community stakeholders who are committed to protecting and sustaining the natural environment [50,51]. The goals of transformative social ecology extend even further beyond environmental science and include any research aimed at improving the quality of people’s transactions with their built, social, and virtual surroundings as well as the natural world. The remainder of this chapter focuses on descriptive and explanatory goals of social ecology, especially its theoretical concerns and categories for describing the structure and functioning of human-environment systems. Also, contextual mapping is presented as an analytic framework for understanding the connections between different features and scales of environments, and their combined effects on health, behavior, and sustainability. Chapter 4 explores recent changes in the structure and functioning of human environments brought about by the rise of the Internet and its impact on our natural, built, and sociocultural surroundings. The transformative goals of social ecology are addressed more explicitly in later chapters that describe community interventions for improving personal and public health (Chapter 5), social behavior and societal well-being (Chapter 6), the sustainability of natural resources in an era of global climate change (Chapter 7), and the quality of built environments such as homes, workplaces, neighborhoods, and cities (Chapter 8). In each of these chapters, methods of contextual analysis are used to better understand and manage health, behavioral, and sustainability challenges.

Describing the Structure and Dynamic Qualities of Human Environments: Core Principles First, all branches of ecology assume that environments are made up of multiple interdependent features. Biological ecologists study the relationships between living and nonliving components of plant and animal biomes. The immediate locale of a plant or animal includes members of its own species as well as other life forms. Plants and animals also must adapt to nonliving (or “abiotic”) features of their environs such as geological and climatic conditions. Human ecologists studying urban environments pay close attention to geographic and economic influences on behavior and health. The environments that social ecologists study are comprised of material and symbolic features, physical and sociocultural components, natural and built (designed) elements, and place-based as well as virtual domains. These features of human environments

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exert a combined or synergistic influence on behavior and well-being. In the studies described earlier, the effects of classroom windows on children’s response to aircraft noise and of commuting stress on employees’ work and family situations revealed the joint impact of different environmental conditions on well-being. Social ecological analyses consider multiple environmental features (e.g., activity domains such as home, school, and work; perceived as well as objective qualities of places; virtual as well as physical reality) that affect people’s day-to-day interactions with their surroundings. Second, ecological analyses include multiple geographic, social, and temporal scales for understanding health and behavior. Environments are viewed as part of a nested structure where local situations and settings are embedded in larger, more remote regions (e.g., classrooms within schools within neighborhoods). Also, environmental participants vary in the complexity of their social structure, ranging from individuals, groups, and organizations, to larger communities, societies, and international entities. Some models of human and social ecology emphasize macrolevel transactions between society and nature (e.g., the impacts of political and economic forces on the natural environment [44,52]), whereas others combine individual, organizational, community, regional, and global scales of analysis (e.g., [31,53,54]; see also Figs. 2.4 and 2.5). From a temporal perspective, people’s encounters with their surroundings can be studied over short or long periods (e.g., from observing children at noisy elementary schools to retesting them years later to see if they developed adult hypertension). A multilevel contextual approach is essential for understanding the many sources of environmental influence on the behavior and health of individuals, groups, and populations. Yet because multilevel analyses of people’s relationships with their surroundings over extended periods are so broad, they pose significant challenges. Investigators must decide which contextual variables to include and how broadly to draw the spatial, temporal, and sociocultural boundaries of their research when analyzing human–environment transactions. Third, social ecological research relies heavily on the concepts and assumptions of systems theory [12,55–58] to explain how environments and people’s reactions to them change over time. The organization and functioning of environments continually evolve in response to both biospheric and behavioral forces (e.g., geologic and climatic shifts, humans’ consumption of fossil fuels, and development of new technologies, buildings, and transit systems). Environments and their inhabitants are dynamic systems where individuals and groups react to changes in their surroundings and, in turn, actively modify the environment to better suit their needs. In our study of commuters, many individuals with long-distance commutes eventually moved closer to their workplace to ease the strain of rush hour driving. Effective adaptation to environmental change improves the congruence or fit between people and their surroundings [27]. Individuals’ ability to cope with changing conditions depends on whether their personal attributes (e.g., genetic heritage, analytic and behavioral skills, perceived control over the environment) are well matched to the demands of the situation. When the fit between people and their environment is lower than desired, they experience physical strain, emotional stress, and other kinds of negative feedback [59]. In some cases, individuals fail to achieve a state of balance with their surroundings. Instead, the

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negative impacts of environmental demands become more severe through a process of deviation amplification [60]. Chronic exposure to environmental threats in those situations can lead to exhaustion and ultimately death [13]. Deviation amplification is an inherent property of both individual–environment and population–environment transactions. Whereas earlier analyses of ecosystems (cf., Ref. [61]) focused on homeostatic or equilibrium processes, later models have placed more emphasis on disequilibrium and destabilization processes in human environments [62–66].14 Gunderson and Holling’s [67] concept of panarchy integrates homeostatic and disequilibrium processes in ecosystems by suggesting that people and environments are linked in continual cycles of growth and change, where temporary phases of equilibrium are followed by deviation processes that push the system toward new “set points” of stabilization that are, in turn, impermanent [68,69]. These cycles of growth and change occur at multiple scales of a human-environment system. The interplay between temporary phases of stability and disequilibrium reflects a recurring pattern of dynamic nonequilibrium in people’s relationships with their immediate and more distant surroundings [70]. Fourth, because ecological research spans so many categories and levels of environmental influence, it is inherently transdisciplinary in its approach to understanding people’s encounters with their surroundings. In addition to combining concepts and theories from multiple fields, ecological studies use multiple methods and measures to study environment and behavior. Many of these investigations emphasize the transformative goals of social ecology mentioned earlier, in that they are intended to improve society as well as to promote scientific discovery [71–73]. Translating scientific knowledge into solutions for complex problems (e.g., reducing the impact of school noise on children through improved classroom design; public policies aimed at reducing fossil fuel consumption and promoting greater use of renewable energy sources) requires collaboration between scholars and community members representing diverse disciplinary, professional, and citizen perspectives. Social ecology emphasizes a transdisciplinary action research approach [74] where academic and nonacademic perspectives are combined to better understand and manage environmental challenges.

Analytic Goals of Social Ecology: Developing Guidelines for Contextual Theorizing and Research The distinctive features of ecological research—especially its interdisciplinary, multilevel, multimethod, systems-oriented, translational approach—are common to the various schools of ecology described in Chapter 2. These core principles or organizing assumptions of social ecology highlight the enormous breadth of ecological scholarship in the sense that it encompasses so many different facets and scales of 14  The

theoretical and empirical analyses of societal collapse presented by Tainter (1988) and Diamond (2005) emphasize destabilization processes and the tendencies for human societies to exceed stringent limits to growth (Meadows et al., 1972, 2004) and finite planetary boundaries (Stefen et al., 2015). These issues are discussed further in Chapters 7 and 8 on global environmental change and sustainability.

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Core Principles of Social Ecology

• Human

environments consist of multiple dimensions including natural, built, sociocultural, and virtual (cyber-based) features, some that are directly observable and others subjectively perceived • People’s transactions with their surroundings occur at multiple levels and are nested within bounded environmental contexts (such as, homes, workplaces, communities, regions, nations) that are interconnected across varying geographic, social, and temporal scales • Environments and their inhabitants are dynamic systems where individuals and groups react to changes in their surroundings and, in turn, actively modify the environment to better suit their needs • Social ecology is inherently transdisciplinary in its approach to understanding people’s relationships with their surroundings. It draws on concepts, theories, and methods from several fields and emphasizes an action research orientation by integrating academic and nonacademic perspectives to more effectively analyze and manage complex societal problems

the environment (see Table 3.1). Bronfenbrenner’s [31] and Barton’s [53] ecological models of human development and health exemplify this broad-gauged approach by tracing the influence of micro- and macroscale environments on individuals over their life span (see also Figs. 2.4 and 2.5). Social ecology examines how environmental contexts at varying scales affect human health, behavior, and the sustainability of our surroundings. Contextual influences on these phenomena emanate from at least four environmental spheres: (1) the natural environment comprising the plant and animal species living in a particular area, abiotic features including geologic and climatic conditions, and various resources produced through nature-based rather than human-initiated processes; (2) the built environment including physical resources designed and produced by people, such as their buildings, vehicles, and tools used to create other products: (3) the sociocultural environment encompassing organizational and institutional entities, political and economic structures, social norms, legal codes, and the social activities that people perform as members of groups and communities; and (4) the virtual environment composed of computing and mobile communications equipment, the World Wide Web, the Internet of Things, social media, virtual reality, and other digital technologies (see Figs. 1.1 and 1.2). This partial list of features within each environmental sphere is not exhaustive, but it conveys the variety of circumstances that influence people’s interactions with their surroundings. A major challenge in social ecological research is to identify from among myriad contextual variables those that are most crucial for understanding particular health, behavioral, and sustainability outcomes. The wide scope and complexity of ecological models pose advantages as well as disadvantages for researchers. On the one hand, ecological analyses facilitate discovery of contextual factors that affect important behavioral, health, and sustainability criteria. Ecological research takes into account situational factors that might be overlooked by narrower models. On the other hand, because ecological analyses subsume

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so many environmental dimensions and scales, they are not always conducive to developing parsimonious or efficient explanations of behavior and well-being. A theory is efficient if it includes only those variables that affect the phenomenon under study. Complex ecological models may lead researchers to include too many contextual variables in their studies that are negligibly related to the problem they are investigating. A disadvantage of this approach is that it sacrifices conceptual parsimony for broad contextual scope and inclusiveness. One strategy for resolving the tension between parsimony and broad scope is for researchers to make theoretically informed decisions about which variables to include in their studies. Rather than incorporating the widest possible array of situational factors in their research, investigators should focus on identifying the effective context— that subset of high-leverage situational variables most crucial for understanding a particular phenomenon. The selective search for high-leverage variables should be guided by researchers’ knowledge of prior theory, existing empirical evidence, and their intuition about which contextual factors shed most light on the topics they are studying. Generating hypotheses about the contextual moderators of a phenomenon can be challenging, especially when faced with so many potentially relevant variables. To reduce the complexity of this task, the search for situational factors that have the strongest influence on a phenomenon can be organized around the strategies of contextual analysis described below.

Contextual and Noncontextual Research Contextual research is rooted in the concept of embeddedness. Any behavioral, health, or sustainability outcome is situated within and potentially influenced by surrounding events. An initial step in contextual research is to identify the focal variables or target phenomenon to be studied—for instance, the possible links between children’s exposure to aircraft noise at school and their blood pressure or task performance. Once the target variables are selected, the next step is to identify contextual factors that may influence the relationships among them. Whereas noncontextual analyses focus entirely on the links between isolated predictor and outcome variables, contextual research examines additional factors in the immediate situation or more remote settings that may affect the relationships between designated target variables. In the traffic study described earlier, commuters’ driving distance and blood pressure levels were the target predictor and outcome variables, respectively, whereas participants’ satisfaction with their home and work situations were contextual factors that moderated the stressfulness of a long-distance commute. The distinction between contextual and noncontextual research raises challenging questions. First, how broadly should the contextual boundaries of a phenomenon be drawn? Second, what concepts and measures should be used to represent the context of the target variables? Any phenomenon can be viewed in relation to alternative situations and environmental domains. The number of situational factors that may influence a particular event (e.g., an incidence of aggressive behavior among teenagers at a schoolyard) is potentially infinite, so the effective context is never completely known or specifiable. However, the hypothetical notion of an effective

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context is useful because it prompts researchers to consider the plausible range of situational variables most likely to affect a phenomenon as it occurs in a particular time and place.

Developing and Evaluating Contextual Theories Identifying the effective context involves at least two stages at the outset of a research project: first, a contextual mapping phase where the selected target variables are examined within increasingly broad segments of a person’s (or group’s) surroundings; and second, a contextual specification phase in which the researcher selects (on the basis of the exploratory phase) certain situational factors deemed most crucial for understanding the phenomena under study. The exploratory mapping phase of research is often bypassed or curtailed. Narrowing the scope of a study too quickly, however, can prematurely exclude important contextual variables from further consideration. Exploring a phenomenon relative to different contexts is an essential step in generating contextual theories or those that predict cross-situational variations in the target phenomenon. A contextual hypothesis related to the schoolyard incident described earlier is that a hostile person is more likely to behave aggressively toward outsiders when prodded to do so by peers, especially in a secluded rather than public setting. A contextual theory would go on to explain why a hostile person is more likely to act aggressively toward an outsider when subjected to peer pressure in a secluded environment. Support for this proposition might be drawn from prior studies of conformity pressure and behavior [75], and others showing that the presence of onlookers and “guardians” in public places and neighborhoods lowers the risk of crime [76–78].15 The accuracy of a theory in explaining the relationships between a set of target variables and one or more situational moderators is referred to as its contextual validity.16 If a theory predicts that aggressive acts by a hostile person toward an outsider are more likely to occur in secluded settings where no neutral bystanders are present, and the available evidence from relevant studies fits the predicted pattern 15  Not

all theories identify cross-situational variations in target phenomena. Trait theories account for behavior and health outcomes primarily in terms of personal dispositions—for instance, the idea that individuals hostile by nature behave aggressively more often than nonhostile persons, irrespective of situational circumstances. Environmental theories view behavior primarily as a by-product of the immediate situation—for example, the fact that prolonged exposure to loud noise raises a person's heart rate and blood pressure. If it is assumed that loud noise always increases physiological arousal regardless of where and when it occurs, then there is no need to complicate the analysis by including contextual moderators of noise-related arousal. Most of the environmental, behavioral, and health issues examined in social ecological research, however, are too complex to be explained solely in terms of trait or environmental theories. These phenomena generally involve interactions between personal and environmental variables (e.g., a commuter's Type A personality and the distance of his or her drive to work). 16 Contextual validity is a subcategory of predictive validity, a criterion for judging the accuracy of theoretical predictions. Contextual validity pertains to a theory's accuracy in specifying cross-situational variations in a phenomenon. Predictive validity does not require cross-situational analysis. A noncontextual theory might demonstrate high predictive validity within a single setting but fail to identify important moderators of the phenomenon that would only become evident in alternative and as yet unobserved settings (Carmines and Zeller, 1979; Stokols, 1987).

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(cf., Refs. [76,77]), then the theory is assumed to be valid over the range of contextual circumstances it specifies. The contextual validity of a theory is low if it includes situational factors that have no bearing on the target phenomenon or affect the target variables in ways contrary to the predicted pattern (for instance, if aggression by a hostile person increases rather than decreases when bystanders are present due to a “diffusion of responsibility” to intervene in a conflict between strangers [79]).17 In some cases, a contextual theory may be valid but not amenable to empirical test. Certain theories are not testable when they are first proposed but acquire greater testability later on as evaluation methods become more sophisticated (e.g., Darwin’s theory of evolution and Einstein’s theory of relativity [80,81]). The testability of a theory requires that its main variables and the relationships among them can be empirically tested using reliable and valid measures. Another consideration when evaluating contextual theories is whether or not they provide powerful and efficient explanations of a particular phenomenon. The relative power of a contextual theory increases to the extent that it accounts for the full range of situational factors that affect the phenomenon under study. A theory may correctly identify some of those conditions but exclude several others. If the target variables are personality traits and aggressive behavior, a contextual theory may accurately explain the role of peer pressure in increasing the chances that a hostile person will act aggressively toward an outsider. But if other contextual variables such as the presence or absence of bystanders in a public or secluded location are also crucial for predicting aggressive behavior, then a theory focusing only on the moderating role of peer pressure would be less powerful than one that explains the influence of other environmental factors as well. Also, a contextual analysis may be too inclusive, incorporating situational factors that are minimally or not at all related to the target variables. In that case, the theory would be less efficient or parsimonious than one that excludes irrelevant contextual factors. The scientific and practical value of a theory also can be judged in terms of its generativity and applied utility. A theory’s generativity is its capacity to prompt new insights about a phenomenon that were not evident from previous research [82]. Generativity can be difficult to gauge in the short run and requires longer-term assessments of a theory’s influence on later explanations of a phenomenon. Regardless of the time frame required for its assessment, the generativity criterion is useful in emphasizing the value of novel ideas—even those that are not completely fleshed out—as a basis for gaining new insights about a phenomenon. Finally, the applied utility of a theory is its capacity to generate practical solutions to community problems and offer clear guidelines for translating scientific research into effective policies and interventions. Ideally, analyses of scientific and societal problems should be contextually valid, testable, powerful, efficient, generative, and have applied utility (see Table 3.2). In reality, broad-gauged contextual theories often pose trade-offs among these criteria. 17  As

noted earlier, many theories fail to specify contextual moderators of behavior altogether. These noncontextual theories are exemplified by statements such as “Hostile persons invariably behave more aggressively toward others than nonhostile individuals, regardless of contextual factors.” This assertion assumes that the links among target variables show the same pattern across all situations.

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Table 3.2 

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Criteria for Evaluating Contextual Theories

• Contextual

validity

• Testability • Relative

power (efficiency) • Generativity • Applied utility • Parsimony

For example, multiscale theories encompassing diverse environmental contexts (e.g., Bronfenbrenner’s [31] ecological model of human development) may generate novel insights about people’s interactions with their surroundings but may be difficult to test empirically because of the complexity of its assumptions and hypotheses. Narrowly drawn theories, on the other hand, may be more readily testable but less generative than broader models. Although trade-offs often are made between different standards for judging a theory’s value, social ecological research aims to create contextual theories that satisfy as many of these evaluative criteria as possible.

Mapping the Effective Context of People–Environment Transactions Social ecological inquiry advances by creating and evaluating contextual theories and translating scientific evidence into problem-solving strategies. Whereas reductionist theories zoom in to explain selected phenomena in terms of their most basic, microlevel components, contextual theories zoom out to explore the interplay between different levels of environmental influence on health, behavioral, and sustainability criteria. This exploratory “zooming out” process in ecological research can be organized around four basic dimensions of contextual analysis: namely, (1) the contextual scope of the analysis or the range of spatial, temporal, sociocultural, and virtual factors that are thought to influence the target phenomenon; (2) the joint use of both objective and subjective representations of the target and contextual variables; (3) the individual or aggregate level at which selected phenomena are examined; and (4) the representation of people and environments in terms of their partitive characteristics or composite qualities. For instance, “sustainability” is a composite quality of a person’s lifestyle that subsumes several partitive (or separate) behaviors such as recycling home waste products, adopting a low-meat diet, substituting pedestrian or bike transit for motorized travel, installing residential solar panels, and curtailing electricity and water use at home and at work.18 Any behavioral, health, or environmental phenomenon can be modeled in relation to these contextual dimensions (see Table 3.3). As a framework for organizing ecological research, the four dimensions can help systematize the search for contextual 18 See

also Chapter 8 for further discussion of sustainability and resilience at different levels of social ecological systems (e.g., individuals, organizations, buildings, neighborhoods, communities, regional, and global ecosystems).

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Table 3.3 

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Dimensions of Contextual Representation

• Contextual

scope—spatial, temporal, sociocultural, and virtual facets and subjective representations of target and contextual variables • Individual and aggregate levels at which selected phenomena are examined • Partitive and composite qualities of people and environments • Objective

influences on various phenomena. Rather then prematurely emphasizing either pole of each dimension (e.g., narrow vs. broad scope and the use of objective or subjective measures from either an individual or aggregate perspective), the researcher can consider a set of target variables in relation to different points along the four continua. The exploratory mapping of people–environment relationships along the four dimensions is valuable, especially during the early stages of theorizing, as a way of sensitizing researchers to important contextual variables that might otherwise be overlooked. The contextual variables included in a theory depend on the research questions being asked. For organizational scientists who study team effectiveness, the focal level of analysis is the team, situated in a particular corporate structure and the broader national and global economic systems in which the company participates. Individuals’ personal attributes in that case would lie at a more micro (intrasystem) level relative to the team or its organizational context [83,84]. Alternatively, for a psychologist studying the effects of group pressure on team members’ decision-making, the personal level of analysis would be focal, whereas the structure and norms of the group would be contextual factors that influence individuals’ thoughts and behavior [75]. When attempting to explain a phenomenon, it is useful to consider it from multiple analytic levels (i.e., from broadly contextual to more micro perspectives). However, in many fields there is a tendency to focus exclusively on the most basic or reductionist explanations of phenomena, while neglecting their broader geographic, historical, sociocultural, and virtual contexts [83,85]. The exploratory mapping of target phenomena relative to alternative contextual frames helps avoid reductionist bias and is valuable for gaining a more comprehensive understanding of scientific and societal problems.

Contextual Scope: Spatial, Temporal, Sociocultural, and Virtual Dimensions Contextual scope refers to the analytic breadth of a theory or research project. A set of target variables can be examined in relation to the immediate situation in which they occur or in the context of broader geographic regions and time intervals. In addition, a contextual analysis may include or omit sociocultural and virtual factors that influence the target phenomenon. Analyses of people’s transactions with their environments, thus, can be compared on at least four dimensions of contextual scope: spatial, temporal, sociocultural, and virtual. The spatial scope of an analysis increases to the extent that it represents places, processes, and events occurring within a broad rather than narrow region of the individual’s or group’s geographic environment. For instance, in our study of the effects of aircraft noise on children, the analyses incorporating residential as well as classroom

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Figure 3.7  Expanding the spatial scope of school noise research. From Cohen S, Evans GW, Stokols D, Krantz DS. Behavior, health, and environmental stress. New York: Plenum Press; 1986. SCHOOL NOISE EXPOSURE

HOME NOISE EXPOSURE

BLOOD PRESSURE AT SCHOOL

EMOTIONAL STRESS AT HOME

noise levels widened the spatial scope of the research beyond our initial focus on school sites alone (see Fig. 3.7). The temporal scope of an analysis increases to the extent that it includes places, processes, and events that occur within an extended rather than narrow time frame. The longitudinal design of our traffic congestion study broadened the temporal scope of the project beyond a single testing session and enabled us to identify several longdistance commuters who had moved closer to work after the first phase of the research to lessen their commuting stress [30]. The sociocultural scope of an analysis is greater to the extent that it considers facets of an individual’s or group’s sociocultural environment. Research on human crowding, for example, has shown that social and cultural norms play a pivotal role in moderating people’s reactions to high density over extended periods [3,4,86]. Crowding studies that measure the effects of these contextual factors are of broader sociocultural scope than those focusing more narrowly on the behavioral effects of spatial density, alone. The virtual scope of an analysis increases to the extent that it includes cyber as well as sociophysical influences on a particular phenomenon. Prior to the 1980s, the cybersphere was nonexistent. The emergence and rapid growth of the Internet and other digital technologies since the 1980s have given rise to our virtual ecology, distinct from

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yet closely intertwined with our natural, built, and social environments. Nowadays, cyber sources of information and experience are imported into and become embedded in place-based environments through occupants’ use of computers, mobile “smart” phones, virtual reality simulations, and other digital technologies [87–89]. These flows of digitized information and communication strongly affect people’s behavior and well-being in places such as homes, classrooms, workplaces, healthcare settings, and automobiles—partly because they divide people’s attention between competing demands of the immediate physical environment and the virtual experiences (via chat rooms, websites, computer-mediated conversations) that occur there. Cyber technologies now permeate many realms of environmental experience including transportation settings.19 Our studies of commuting stress, for example, were conducted in the 1970s before mobile phones became widely available [19,30]. If those studies were replicated today, they would likely address the ways that cell phone use moderates drivers’ stress by enabling them to call ahead and let coworkers or family members know if they are running late due to traffic delays.20 Also, many companies now permit their employees to “telecommute” to work using their home computers so that they can avoid the strains of rush hour commuting during the week. Analyses of travel constraints that consider the moderating effects of digital technologies would be of broader virtual scope than those that neglect their influence on commuting stress. The spatial, temporal, sociocultural, and virtual dimensions of people–­environment transactions suggest a research continuum ranging from narrow to broad contextual scope. At the narrow end of the continuum are analyses that are conceptually and methodologically reductionistic—i.e., the conceptualization and measurement of the phenomena under study are limited to particular events within a spatially, temporally, socioculturally, and virtually restricted situation. Analyses at the broad end of the continuum are those that consider the target variables in relation to a wider range of a person’s (or group’s) geographic, sociocultural, and virtual surroundings and within an extended rather than narrow time frame. Enlarging the contextual scope of research does not ensure that the most influential moderators of a phenomenon will be discovered. The spatial, sociocultural, temporal, and virtual dimensions of contextual scope are nonetheless useful as they offer analytic coordinates for mapping various phenomena in relation to alternative clusters of contextual variables. This exploratory mapping process can facilitate discovery of the effective context by revealing important geographic, temporal, sociocultural, and virtual aspects of a phenomenon that might otherwise be neglected.

Objective and Subjective Representations of Contextual and Target Variables Contextual and target variables can be represented in objective terms, independent of a person’s perception, or alternatively from the subjective vantage point of the individual or group. In studies of commuting stress, the drive between home and work can 19 The

rapid rise and pervasive impacts of the cybersphere are discussed in Chapter 4. the other hand, there is substantial evidence for the negative consequences of cell phone use in automobiles, including the safety hazards of texting while driving. See for example Caird et al. (2014).

20 On

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be characterized objectively according to its physical distance and duration or in terms of the driver’s subjective experience of traffic congestion along the route. Similarly, in our study of aircraft noise in school settings, decibel levels were measured objectively using electronic sound level meters and also by asking students and teachers to rate the noisiness of their classrooms. Our outcome measures as well included both objective recordings of students’ blood pressure as well as their subjective ratings of annoyance due to the noisiness of their classrooms. Rather than relying exclusively on either an objective or subjective approach, contextual analyses combine both perspectives to gain a broader understanding of environment–behavior relationships. As noted in Chapter 2, Firey’s study of the symbolic meanings of Boston neighborhoods broadened the field of human ecology by illuminating the powerful effects of symbolic as well as material features of environments on residents [90]. The value of considering subjective as well as objective facets of environments is especially salient today in the context of planetary warming, accelerating sea rise, and pollution. Confronting these global crises requires that scientists not only gather objective evidence of climate change but also raise public awareness of this problem so that citizens will be strongly motivated to adopt more sustainable behavior [91–94].

Combining Individual and Aggregate Levels of Analysis The principles of social ecology outlined in Table 3.1 highlight the importance of viewing human-environment systems from the vantage points of both individuals and groups. Combining individual and aggregate levels of analysis can help facilitate a broader understanding of people’s relationships with their surroundings. In the realm of transportation planning, individual commuters’ daily activities and their movements between different places can be aggregated to create a community-level map of traffic flows, “bottlenecks,” and peak hours of vehicle use along alternative routes [26,32]. Projections of travel demand based on individual and aggregate behavior patterns can help guide planning decisions about future land uses and transit routes [27,95]. Integrating individual and aggregate analyses is also useful for evaluating the effects of interventions intended to resolve organizational or community problems. Earlier studies on commuting stress suggest that corporate ride-sharing programs can be beneficial for individual employees, their work organization, and the community as a whole. A company-sponsored vanpool program, for example, might be evaluated differently depending on whether the targeted outcomes are individual commuters’ stress levels, corporate effectiveness and profitability, or quality of life at the community level [96]. The traffic congestion study mentioned earlier exemplifies an individually oriented analysis of commuting experiences that could be extended by comparing the stress levels of employees who drive their own cars to work with those who participate in the company’s ride-sharing program. At an organizational level, the cost-effectiveness of the vanpool program could be measured through aggregate criteria of employee morale, productivity, illness-related absence from work, and turnover, relative to the company’s financial investment in the program. And at a

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community level, the program’s effectiveness could be gauged by its impact on residents’ ratings of traffic congestion and noise in their neighborhood. Viewing the vanpool intervention in relation to its personal, organizational, and community impacts enables program evaluators to consider multiple criteria for judging the cost-effectiveness of the program.

Partitive and Composite Representations of People and Environments Environmental contexts of behavior and well-being can be described in partitive or composite terms. Partitive analyses represent the attributes of people and their environments as separate elements whose interactions influence health, behavioral, and sustainability criteria. In our study of aircraft noise and children, we analyzed the separate and interactive effects of school and home noise levels on children’s task performance and blood pressure. And in our research on rush hour commuting, we tested the direct and interactive effects of driving distance and the coronary-prone (Type A) behavior pattern on employee stress. Composite analyses, on the other hand, focus on enduring qualities of individuals’ and groups’ relationships with their surroundings, rather than on distinct, isolated attributes of people and places. Examples of composite terms for describing people–environment relations are Barker’s concept of behavior settings (i.e., recurring patterns of behavior linked to a particular place [9]); Michelson’s concept of intersystem congruence [10] or degree of fit between people’s activities, ethnic and cultural characteristics, and the environmental resources available to them (see also Chapter 2); and analyses of ecosystem services and sustainability [71,97–99] relative to current rates of resource consumption, replenishment, and pollution (discussed in Chapters 7 and 8). The decision to include composite terms in a theory or research project depends on the question/s being addressed. If the focus of a study is on the interactive effects of classroom and home noise and density levels on children’s stress, then a partitive analysis of may be most appropriate. However, if the question is whether or not schools located near undesirable land uses (e.g., airports, landfills) are more likely to be found in poor minority neighborhoods than in affluent white majority areas, then the concept of environmental injustice would be pertinent to the research. Environmental injustice refers to the inequitable exposure of low-income groups to toxins and stressors and the failure to enforce environmental protection laws in minority neighborhoods [100,101]. Environmental injustice is a composite term denoting a strained relationship between disadvantaged groups and their surroundings, rather than the separate attributes of either individuals or their environment. A composite analysis of noise exposure among K-12 students in Los Angeles might involve mapping the locations of all schools in the city relative to airports, freeways, and other noise sources to determine whether the schools in low-income districts are more likely to be found in noisy areas. These data would reveal whether minority students in the city as a whole are disproportionately exposed to noise as predicted by theories of environmental injustice. Rather than focusing on the responses of individual children to classroom noise, the target variables in the composite study would

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be LA school sites and their proximity to noisy areas. The hypothesized moderator of school locations relative to community noise sources is environmental injustice, a pervasive and largely invisible quality of people’s relationships with their surroundings. Determining whether environmental injustice is actually widespread in the community would require other corroborating evidence. For instance, the relative exposures of minority and white neighborhoods in LA to other kinds of stressors and toxins (e.g., radon contamination of residential buildings, proximity to incinerators and landfills) could also be assessed [100,102]. Composite analyses suggest that contextual influences on behavior are not always evident in the observable, material features of environments. The effective context is sometimes more aptly described in terms of abstract, nonvisible qualities of people– environment relations. A case in point is the famous Hawthorne Study of environmental conditions at work, employee productivity, and morale [103]. In that research, increases as well as decreases in illumination levels at employee workstations led to improved task performance. These unexpected findings were later attributed to the symbolic significance of the environmental changes and the fact that both interventions to raise or lower illumination were interpreted by employees as being part of a larger corporate effort to improve working conditions. The composite variable of management concern altered the symbolic meaning of the experimental treatment (changes in illumination) to the workers. In the context of a less structured social situation, or one where the management appeared to be unconcerned about employees’ welfare, the meaning of altered lighting levels and their effects on productivity might have been different [104].

Looking Back and Moving Forward Preceding sections identified core principles and analytic methods of social ecology that can be used to understand environmental contexts and their influence on health and behavior. Together, the organizing assumptions and mapping strategies outlined in Tables 3.1 and 3.3 provide a conceptual framework that can be used to analyze diverse research topics and societal problems at different environmental scales. Contextual analyses of personal and public health, social and environmental problems, and the design of sustainable communities are presented in Chapters 5–8. Social ecology as described here is an overarching analytic framework that can be used to gain broader understanding of how particular aspects of environmental contexts affect people’s day-to-day activities and well-being. In some respects, the core principles and mapping strategies described in this chapter might seem straightforward and obvious. Acknowledging the multidimensional and multilevel structure of environments would seem to be commonsensical. Yet, applying social ecological principles as a unified framework for understanding various research topics can be challenging for several reasons. First, some patterns of influence between people and environments (especially composite qualities of social ecological systems) are not readily visible to researchers. Identification of abstract contextual variables (e.g., overstaffing in a behavior setting, ecosystem sustainability,

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environmental injustice) often requires some theoretical imagination on the part of the investigator [105]. In fact, the contextual mapping dimensions described earlier highlight an important distinction between the ecological environment as it exists in reality and the environment as it is modeled in relation to a particular individual or group. Consider, for example, Fig. 3.2 that depicts a person’s home, work, and community environments in three-dimensional space. By comparison, Fig. 3.8 represents the same environment more abstractly as a person’s daily activity program or record of one’s time allocation across residential, transportation, employment, and commercial settings [32]. The activity program simulation is a selective representation of the person’s encounters with his or her environment because it describes only certain facets of those transactions—namely, the temporal and spatial distribution of one’s daily activities. Even as the scale of environmental units included in an analysis increases (e.g., from a focus on single to multiple activity domains), the actual number of contextual variables used to represent relevant environmental dimensions might remain relatively small. The challenge is to identify those contextual circumstances (from among a wide range of possibilities, both concrete and abstract) that afford greatest leverage for answering a particular research question—for instance, whether dropping

Figure 3.8  A time geographic model of a person’s daily activity program. The top portion of the diagram summarizes the time spent by an individual in his or her dwelling, workplace, commute between home and work, and visits to a bank and post office. From Lenntorp B. A time-geographic simulation model of individual activity programmes. In: Carlstein T, Parkes D, Thrift N, editors. Human activity and time geography. New York: John Wiley & Sons; 1978. p. 162–80.

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off and picking up one’s child at a day care center during the commute to and from work makes parents’ daily routines more stressful [27]. The search for the effective context of a scientific or societal problem—those situational factors most crucial for understanding it—poses another challenge for researchers and practitioners. The analytic strategies outlined here require that they devote sufficient time at the outset of a project to map the target phenomenon in relation to alternative sets of contextual factors. This iterative process of analyzing a phenomenon relative to different contextual frames is a crucial aspect of ecological research, as it enables investigators to develop working hypotheses and theoretical accounts of the links between target variables and situational factors. Many advances in science occurred because researchers had the foresight to imagine how contextual factors might alter a particular phenomenon. In health science, the Germ Theory posits a direct relationship between individuals’ exposure to microbes and their becoming ill a day or two later [106,107] (see Chapter 5). Alternatively, some scholars theorized that contextual factors such as social strain or psychological stress in people’s lives increase their susceptibility to disease after they are exposed to pathogens [108,109]. Cohen and colleagues’ experimental studies demonstrated that participants reporting high levels of psychological stress during the weeks preceding their laboratory exposure to a cold virus were more likely to become ill than low-stress individuals who remained well or developed relatively mild cold symptoms [109,110]. The exploratory development of contextual hypotheses at the outset of an investigation differs from some other research approaches. Grounded theory development, for example, requires that investigators immerse themselves in a field setting to glean inductive insights about a phenomenon as a basis for informing their subsequent theorizing [111,112]. The principles of contextual analysis proposed here, however, put greater emphasis on developing exploratory hypotheses about contextual moderators of a phenomenon even before gathering empirical data in a field or laboratory setting. Contextual theorizing enables investigators to look beyond an immediate research setting and to imagine other situations not readily observable that could substantially alter a target phenomenon—just as high and low levels of psychological stress moderate people’s susceptibility to infectious diseases; or how the absence of gravity in outer space alters the behavior of physical objects, a contextual variation that was inferred by theorists on the ground even before astronauts ventured into space. A third challenge that arises when searching for predictable patterns of cross-situational variations in target phenomena is the instability of contextual factors. A hallmark of the Anthropocene Epoch in the earth’s natural history is the rapid rate at which humans are altering regional and global ecosystems [113–115]. Our burgeoning consumption of fossil fuels since the Industrial Revolution has triggered a staggering cascade of atmospheric and geological changes on earth. Glaciers and sea ice are melting at unprecedented rates, necessitating the remapping of polar regions to due rapid changes in land mass there [116,117]. At the same time, the rise of the Internet, digital computing, and mobile communications have generated an entirely new dimension of human environments, namely, the cybersphere or virtual ecology [88]. People’s efforts to manage their place-based lives while simultaneously participating in virtual environments (e.g., online chat rooms, commercial and gaming sites, virtual reality

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simulations, social media) have created novel health and behavioral challenges (e.g., increased multitasking, information overload, identity theft [87,118]). In the next chapter, we take a closer look at how digital technologies are dramatically reshaping our physical and social world. The rapidity and ubiquity of environmental change means that the effective context of human–environment transactions is never entirely stable. The contextual moderators of a phenomenon can be expected to shift across times, places, and cultures. A key question is how rapidly and predictably these changes in the effective context occur. For phenomena that are relatively stable and enduring over time and place (for example, individuals’ cardiovascular responses to loud noise), efforts to identify consistent relationships between target and contextual variables are warranted. But in environments that are changing rapidly, trying to define the effective context of a phenomenon is likely to be impractical if not impossible. Many societal problems (e.g., poverty, crime, global climate change) are embedded in multiple interacting systems characterized by ambiguous causal links. To make matters more challenging, community members usually voice competing opinions about how to define the focal problem and the practical steps that should be taken to solve it. Some essential features of these “wicked problems” [119,120] are that they can be defined from many different and contested viewpoints, and there are no singular solutions for them.21 Yet even for these kinds of problems, the contextual mapping strategies described earlier can raise awareness of situational factors at different levels of a system that affect people–environment relationships. Although precise predictions about contextual moderators may not be feasible, it is better to recognize and grapple with the “wicked” features of complex social, environmental, and health problems rather than define them too simplistically by adopting an overly narrow vantage point within a community, societal, or global system [121]. One strategy for predicting the stability or instability of environmental contexts is the development of transformational theories—those that identify situations where people’s relationships with their surroundings are likely to undergo fundamental and rapid change [122]. For instance, Wicker’s theory of the life cycles of behavior settings, from their initial establishment to eventual dissolution, exemplifies a transformational theory that specifies when and why a particular setting is likely to arise, the circumstances that determine its longevity and those that trigger its demise [123]. In management science, Emery and Trist characterized the environments of business organizations as placid and predictable or turbulent and difficult to forecast [124]. Organizations must be able to adjust their operating strategies in response to abrupt changes in their immediate and remote environments. Building on Emery and Trist’s ideas, Terreberry offered a theory explaining why initially placid environments of organizations sometimes evolve toward greater turbulence [125]. Understanding how, when, and why human environments change may enable investigators to better

21 See

Rittel and Webber (1973) for more detailed discussion of 10 essential features of wicked problems and Levin et al.’s (2012) analysis of “superwicked problems” characterized by even greater time urgency and complexity.

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estimate the stability of hypothesized or observed relationships between a set of target variables and their contextual moderators. A fourth challenge of using contextual analysis to derive insights about research and societal problems is that it requires investigators to adopt cross-disciplinary approaches in their theorizing and research. Yet, many researchers are trained in the canon of a particular field and as such, they internalize discipline-centric (and often narrowly biased) criteria of what constitutes sound theory and methodology. It is one thing to advocate multilevel studies of environment and behavior relationships. It is quite another thing for a researcher or practitioner to be facile in viewing research and societal problems at different (e.g., micro, meso, macro) scales of analysis and using multiple methods to study a particular phenomenon (e.g., gathering both qualitative and quantitative data in a variety of field and laboratory settings). For investigators to be able to create theories that cross disciplinary boundaries and employ multiple methods in their studies, it is important that they be educated in transdisciplinary approaches to conducting action-oriented (translational) research early in their careers [73,126]. Strategies for training future generations of social ecologists are discussed in Chapter 9. At the beginning of this chapter, I traced the development of my interest in learning about contextual influences on people’s interactions with their environments. I arrived at UCI as a faculty member trained primarily in social and environmental psychology, but over the years adopted a broader social ecological orientation in my research. Although there are certain overlaps between these fields, they are different in notable respects. A widely used textbook in environmental psychology defines the field as “…the study of transactions between individuals and their physical settings” [127] where the term transactions refers to the reciprocal relationship between people and their everyday environments. Social ecology as conceived in this volume adopts a broader purview by considering not only individuals’ but also groups’ transactions with sociocultural and virtual as well as physical features of their environment. Social ecology’s concern with the structure, functioning, and transformation of human environments at micro- to macroscales extends well beyond a psychological perspective on individuals’ relationships with their surroundings. The remaining chapters illustrate how the core principles of social ecology outlined earlier can broaden our understanding of diverse research topics and societal problems.

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