Religiosity, Marijuana Use, and Binge Drinking: A Test of the Moral Community Hypothesis

Religiosity, Marijuana Use, and Binge Drinking: A Test of the Moral Community Hypothesis Abstract We use data from Wave 3 of the National Study of Youth and Religion (NSYR), a nationally representative study of adolescents and emerging adults, to examine the association between religiosity and marijuana use and binge drinking, as well as the importance of social context for these associations. Specifically, we test the moral community hypothesis, originally stated by Stark and colleagues, using a micro-level conceptualization of moral community. We find that higher levels of religiosity are associated with lower odds of engaging in both marijuana use and binge drinking, and that an individual’s level of integration into a moral community moderates the association between religiosity and both outcomes. Implications of our findings are discussed. BACKGROUND A substantial amount of research over the last 40 years has noted an inverse relationship between religiosity and criminal or deviant behavior (Baier and Wright 2001; Johnson 2012; Johnson et al. 2000; Kelly et al. 2015). This trend occurs for more serious crimes (Benda and Toombs 2000; Evans et al. 1995; Jang and Johnson 2001), but is particularly noticeable for less serious forms of deviance that violate religious rather than secular moral proscriptions (Benda 1997; Benda and Corwyn 1997; Cochran and Ackers 1989; Eitle 2011; Ulmer et al. 2012; Wallace et al. 2007). Although theoretical explanations of these findings vary (Baier and Wright 2001), the moral community hypothesis posits an interaction between personal religiosity and social integration into a religious community (i.e., “moral community”) in reducing deviant behavior (Stark 1996; Stark et al. 1982). These communities reinforce religious norms, thereby enhancing the protective effects of an individual’s religiosity. In statistical terms, integration into a moral community moderates the impact of personal religiosity on behavior. Research on the moral community hypothesis has been mixed; some studies find that religious environments strengthen the protective effects of individual-level religiosity (Finke and Adamczyk 2008; Gault-Sherman and Draper 2012; Lee and Bartowski 2004; Regenarus 2003; Stack and Kposowa 2006, 2011; Welch et al. 1991) while others do not (e.g., Bahr and Hoffman 2008; Eitle 2011; Sturgis 2010; Sturgis and Baller 2012). By operationalizing “moral community” through aggregate levels of religiosity within institutions, counties, geographical regions, and nations (e.g., Eitle 2011; Finke and Adamczyk 2008; Gault-Sherman and Draper 2012; Regenarus 2003; Stack and Kposowa 2006; Stark 1996; Stark et al. 1982; Sturgis 2010; Sturgis and Baller 2012), researchers presume that religious people living in predominantly religious areas are more integrated into a moral community than those living in irreligious areas. This approach negates personal connections between co-religionists that facilitate the expression and dissemination of social norms, or small groups that can function as a “moral community.” The following study accounts for this problem, and revisits the moral community hypothesis, by examining personal networks of adolescents and young adults to determine if the association between religiosity and binge drinking and marijuana use is moderated by the degree to which the individual is connected to religious others. Given that substance abuse is common and peers are particularly influential during adolescence and emerging adulthood (Brechwald and Prinstein 2011; Brown and Larson 2009; Smetana et al. 2006; Tucker et al. 2005), the impact of primary networks on the behavior of religious youth is an important topic. We rely on data from the National Study of Youth and Religion, a nationally representative study of religiosity among adolescents and young adults with rich measures of both personal networks and religious faith, to conduct our analyses. Moral Communities and Micro Moral Communities Stark et al. (1982) developed the moral community hypothesis to reconcile the findings of early studies that found religiosity had a significant effect on delinquency with those that did not (e.g., Burkett and White 1974; Higgins and Albrecht 1977; Hirschi and Stark 1969). Borrowing from Durkheim’s (1995) ideas that religion has an integrative effect on community members, Stark and colleagues hypothesized that the impact of individual religiosity on deviant behavior is stronger in “moral communities,” where religious commitment is the norm and religious influences on daily life are pervasive (Stark 1996; Stark et al. 1982). Early studies that found religion had no impact on delinquency relied on samples drawn from the more secular West Coast (Burkett and White 1974; Hirschi and Stark 1969), while the study noting a significant inverse relationship relied on a sample drawn from the South where religious commitment is more of a norm (Higgins and Albrect 1977). Since the South, and specifically Atlanta in the case of Higgins and Albrecht’s study, represents more of a “moral community” than does the West Coast, one would expect to find an effect of religiosity in the former but not the latter (Stark et al. 1982). The moral community hypothesis, therefore, argues that the extent to which a person is integrated into a moral community may influence the association between religiosity and delinquency. It predicts that where religion is pervasive and a religious sanctioning system is expressed in daily life (i.e., moral communities), the degree to which an individual adheres to this system, which is a direct reflection of his or her degree of religious faith, will influence the likelihood of engaging in deviant behavior (Stark et al. 1982). Environments with high concentrations of religious people provide the networks and shared beliefs that facilitate compliance to religious norms (Hoffman and Bahr 2006). Yet Stark (1996:164) notes that, “religion is empowered to produce conformity to the norms only as it is sustained through interaction and is accepted by the majority as a valid basis for action.” Where religion is not a prominent part of the cultural landscape, and a religious sanctioning system is not prevalent, the everyday expression of religious life will be stifled and religiosity will be less salient in a person’s life (Stark et al. 1982). Religious individuals isolated from a moral community may not experience the protective effect of religiosity. The moral community hypothesis is centrally concerned with social integration into religious communities, which is a complex process involving vague theoretical terms. Although “community” implies similarity, interaction, and geographical proximity between a set of individuals, the degree to which these elements must be present to establish a community is unclear (Leighton 1988). Some scholars determine the parameters of community by empirically identifying concentrations of interaction patterns within the broader social structure (Leighton 1988; Newman and Girvan 2004; Wellman 1979, 1996). Social integration is relational, denoting either a level of connectedness between individuals or the degree to which a specific person is connected to the surrounding network (Berkman et al. 2000; Brissette et al. 2000; House et al. 1988). Other researchers define and describe communities by relying on aggregated characteristics within a geographical area (Leighton 1988). This approach maintains the relational nature of integration by assuming the degree to which a characteristic is present within a setting predicts individual and community-level social connectedness. Stark et al. (1982) originally conceptualized moral communities as social groups with a religious sanctioning system that is expressed in daily life. However, their theoretical use of “group” or “community” is not clearly defined and includes both broad social ecologies where religiosity is prominent and small social groups of co-religionists (Stark 1996). Researchers have subsequently measured aggregate levels of religiosity between nations, U.S. regions, counties, schools, or prisons to determine the influence of moral communities (Eitle 2011; Finke and Adamczyk 2008; Gault-Sherman and Draper 2012; Regenarus 2003; Stack and Kposowa 2006, 2011; Stark 1996; Stark et al. 1982; Sturgis 2010; Sturgis and Baller 2012). These studies account for the relative similarity of religious beliefs (i.e., the degree of religious integration) within broad communities, but they do not explicitly measure the extent to which specific individuals are connected to co-religionists. They assume that religious individuals residing in generally religious areas are more integrated into a moral community than those living in less religious areas. But this assumption may be flawed. Religious individuals may be isolated in highly religious areas or integrated into small religious groups in irreligious areas, a point that has largely been ignored in studies that examine the moral community hypothesis. The integration of religion into geographical areas or bounded institutions may influence but not determine individual-level integration into moral communities. Where religiosity is more concentrated within broad communities, religious individuals will likely have more options to participate in smaller religious groups (Olsen and Perl 2010; Pescosolido 1990). Social networks are not geographically liberated, and one’s opportunity for interaction is limited by the surrounding social structure, circumstances, and availability of co-religionists (Fischer 1982; Pescosolido 1990; Wellman 1996). This does not mean that locations lacking religious prominence necessarily stifle religious expression, forcing individuals to compartmentalize their religious lives. Fischer’s (1982) analysis of social ties within the San Francisco Bay area noted that while people living in the urban center were less likely to be religious, those who were religious also reported a high involvement with religious others. There is some evidence to suggest in such circumstances the protective effect of religion may be stronger than it is in communities where religion is more pervasive (Tittle and Welsh 1983). An abundance of research also finds that personal commitment to a religious group increases as the prevalence of that group diminishes within the surrounding area (e.g., Hammond and Hunter 1984; Olsen 2008; Smith and Emerson 1998). Where religion is not a prominent feature of a geographical area, religious individuals appear to find ways to interact with each other and such interpersonal connections may influence behavior. Religion organizes social relationships so that networks of interacting co-religionists may thrive even in predominantly irreligious areas. Co-religionists are drawn to each other through similar belief systems and centralized organizations (DiPrete et al. 2011; Graham and Haidt 2010; McPherson et al. 2001; Olsen and Perl 2010). Churches provide a primary social setting for co-religionists to interact regardless of religious sentiments of the surrounding community. For example, Olsen and Perl (2010) find that the number of friends a person has within a given congregation is not influenced by the concentration of religiosity within a region. To explain such effects, some scholars argue that participation within a congregation structures one’s activities and social options to create dense, localized social networks for both adults and adolescents (Smith 2003). Others note that some theological orientations advocate in-group preference and out-group exclusion (Scheitle and Adamczyk 2009). Social ties within Evangelical Protestant denominations are particularly dense causing congregants to spend much of their social energy on church-related rather than community activities (Putnum 2000, see also Beyerlien and Hipp 2005). The effects of religiosity on network homophily also extend beyond churches, as religious teenagers are closer to and spend more time with religious peers within their school settings (Cheadle and Schwadel 2012). Such personal networks are an important facet of religious life, regardless of the level of religious integration in a region, county, or institution. Adolescent Personal Networks and Substance Use Network analysts manage the challenges of studying social integration by examining primary social networks, or small groups of intimately connected individuals, which allows them to conceptualize community from the micro-level and then, if desired, identify integration patterns in more expansive networks (Fisher 1982; Wellman 1996). Accounting for primary networks also allows researchers to determine the degree to which a person is connected to peers, the characteristics of those peers, the cultural activities of group life, and the influence of group life on personal conduct. These attributes of small group participation are relevant for determining how integrated a person is into a social group with a religious sanctioning system that is expressed in daily life, or a moral community (Stark 1996; Stark et al. 1982). Primary networks are especially influential during adolescence and emerging adulthood when peers become an important source for the establishment of cultural frameworks, social norms, and social identity (e.g., Brechwald and Prinstein 2011; Brown and Larson 2009; Smetana et al. 2006). Socialization is a collaborative process (e.g., Corsaro and Eder 1990), and adolescents engage in communicative events like gossip, storytelling, and teasing to construct and communicate moral evaluations, meaningful labels, and idealized identities that contribute to the normative expectations of group life (Bucholtz 1999; Fine 1986; Kyratzis 2004; Shuman 1986). The make-up of one’s primary network during adolescence thus matters, as do the types of conversations that occur within the group. What peers talk about can shape who they are and what they do. The influence of these group processes may translate to primary networks of co-religionists, especially if individuals participate in informal conversations about faith outside of formal religious settings. Although some scholars argue social ties function as a social control mechanism that reduces delinquency (Hirschi 2002), most studies find that both the degree of integration and the type of network one is connected to influences individual level delinquency (Haynie 2001; Reynolds and Crea 2015). Haynie (2001), for example, found that both network centrality and density with a delinquent peer network is associated with personal delinquency among adolescents. The existence of close social ties does not reduce delinquency independent of the cultural practices of group life. This finding also translates to the relationship between social networks and substance abuse. Research consistently finds an association between peer and personal use of illicit substances among adolescents and emerging adults (e.g., Andrews et al. 2002; Galea et al. 2004; Kirke 2004; Mason and Windle 2001), leading scholars to note that patterns of socialization within social networks partially explain these findings (Brechwald and Prinstein 2011). The influence of primary networks during adolescence and emerging adulthood suggests that participating in religious networks may enhance the protective effect of religiosity on individual-level deviance (i.e., substance abuse) through the reproduction of cultural norms. Extant research confirms both that religiosity and associations with religious peers reduces illicit substance use and abuse (e.g., Bartowski and Xu 2007; Thompson 2016). There is also evidence that religious reference groups (i.e., denominations) socialize congregants differently, which influences alcohol consumption (Beeghley et al. 1990; Bock et al. 1987; Cochran et al. 1992). Yet, the few studies that use network data to examine how primary networks intersect with personal religiosity to influence deviance yield mixed results. Although Adamczyk and Palmer (2008:731) find that friends’ born-again identity does not “strengthen the relationship between individual born-again identity and marijuana initiation,” which contradicts the moral communities hypothesis, other studies identify that religious primary networks enhance the protective effects of religiosity on deviance (e.g., Adamczyk 2012; Desmond et al. 2011). Hoffman (2014), for example, found reciprocal effects between religiousness, religious peers, and moral schemas that are inversely related to marijuana use during adolescence. Yet, the effects of religious peers on both personal religiosity and marijuana use weakened when individuals transitioned into young adulthood, leading Hoffman to suggest that religious identity and marijuana use may be less influenced by primary networks during post-adolescent years. The protective effect of moral communities may be strongest during adolescence when peers are particularly influential and substance use/abuse is especially high (Brechwald and Prinstein 2011; Brown and Larson 2009; Smetana et al. 2006; Tucker et al. 2005). Current Study Broad applications of community cannot account for individual-level integration into small networks of religious individuals or what we call micro moral communities. These are concentrated networks of interaction between religiously similar individuals in which religious norms are openly expressed. From the perspective of the individual, a micro moral community provides access to a small number of religious others who can share in the expression of religious ideas. Scholars examining moral communities have generally ignored the potential influence of personal communities on the link between religiosity and deviant conduct. The few studies that use ego centric network data to examine the intersection between religiosity, primary networks, and deviance provide important information, but they do not explicitly test the moral community hypothesis (e.g., Adamczyk 2012; Adamczyk and Palmer 2008; Hoffman 2014). Stack and Kposowa’s (2011) test of the moral community hypothesis provides a rare exception, finding that personal connections to co-religionists reduces the acceptability of suicide among religious individuals. However, there is a dearth of research examining the moderating influence of micro moral communities on the association between religiosity and deviance. This study addresses that problem by using personal networks of adolescents and emerging adults to measure the extent of integration in a moral community and then examining whether or not the associations between religiosity and binge drinking and marijuana use are moderated by integration into this micro moral community. Research Questions The current study is guided by two primary research questions: First, is individual religiosity associated with a reduction in binge drinking and marijuana use? Once such an association has been established, our second question involves a test of the moral community hypothesis: Are any associations between religiosity and binge drinking and marijuana use moderated by integration into a moral community, conceptualized here as a micro moral community? Specifically, does the negative association between religiosity and deviant behavior strengthen as integration into these micro moral communities increases? If the moral community hypothesis is to be supported, we would expect a negative association between religiosity and participation in deviant behavior, and that this association strengthens as integration into a micro moral community increases. METHODS Data The current study is based on data from Wave 3 of the National Study of Youth and Religion (NSYR). The NSYR is a multi-wave, nationally representative study of adolescents and emerging adults. Data collection included telephone surveys with individual subjects, containing questions on religious beliefs and practices, family and school life, and a range of developmental issues. Random digit dialing and in-home randomization of subjects were used to achieve a nationally representative sample. An oversample of 80 Jewish households that was included in the original data collection were removed from the current sample in the interest of maintaining national representativeness, and are not included in any demographics, descriptive statistics, or analyses reported from this point on. For a detailed description of the sample and data collection techniques of the NSYR, see Smith and Denton (2005) and Smith and Snell (2009). Wave 3 surveys were conducted between September 2007 and April 2008, when the subjects’ ages ranged from 18 to 23 years old and 6.3% of the sample was still in high school. The sample (n = 2,458) contained 48.7% male and 51.3% female respondents, and the racial and ethnic breakdown was 69.1% White, 16.0% Black, 9.9% Hispanic, and 5.0% Other. Wave 3 was chosen for analysis due to a combination of factors. First, this wave contains detailed social network items that will be used in the moral community measure that were not available at all previous waves. Second, the ages of respondents during Wave 3 made them more prone to drinking and marijuana use compared to the previous two waves when subjects were in their early to mid-teens. Prior research suggests the prevalence and frequency of both behaviors increase throughout the teenage years, and although they can plateau they often do not decrease until late 20s or early 30s (Bachman et al. 1997; Johnston et al. 2013; O’Malley et al. 1984). We chose to do a cross-sectional analysis for theoretical reasons. When predicting an association between religiosity and behavior, the implication is that an individual’s religiosity today, not 2 or 3 years ago, is what affects his current behavior. Although it is logical to expect that prior religiosity will also be related to current behavior, that effect would most likely be working through current religiosity. In other words, if an individual’s level of religiosity 2 or 3 years ago is related to his binge drinking and marijuana use today, it is logical to conclude that this is because prior religiosity is related to current religiosity, which in turn is related to current drinking and drug use. The same reasoning holds when adding the moral community measure into the mix, and investigating whether or not the effect of religiosity is moderated by the extent of one’s integration into a moral community. The implication of the moral community hypothesis is that the association between current religiosity and behavior is conditioned by current level of integration into a moral community. Thus, measuring religiosity, moral community, binge drinking, and marijuana use all in the same wave of the NSYR is more parsimonious and allows us to examine these contemporaneous relationships that are more in line with theory. One limitation to conduct a cross-sectional analysis is that any negative association we observe between religiosity and behavior could potentially be due to “reverse causation,” where the behavior in question comes first and is followed by a reduction in religiosity. However, there are two reasons why we feel this is not happening here to any significant extent, and that any associations we see are largely due to an effect of religiosity on behavior. First, although some prior research has found bi-directional effects between religiosity and delinquency (e.g., Benda and Corwyn 1997), other research has failed to find a reciprocal effect of behavior on religiosity (e.g., Meier 2003). Second, in writing about their own analysis of religiosity and a variety of outcomes, using Wave 1 of the NSYR, Smith and Denton (2005:237) conclude, “We do not believe, however, that the available quantitative and qualitative evidence support the conclusion that reverse causation explains most or all of the variance in outcomes among different religious types [indicative of different levels of religiosity].” Finally, in addition to these two reasons, it is difficult to imagine a situation where reverse causation could explain away support for the moral community hypothesis. Basically, for this to happen in relation to the current analyses, individuals who are highly integrated into a moral community and who engage in binge drinking or marijuana use would have to reduce their religiosity more than their counterparts who are less integrated into a moral community and who engage in these behaviors. There is no theoretical reason to expect this would be happening, nor are we aware of any empirical evidence for this in prior research. Thus, although we obviously cannot make causal claims based on the current cross-sectional analysis, we feel it is more in line with current theoretical reasoning and provides a better approximation of the mechanisms in operation. Dependent Variables Table 1 contains descriptive statistics for all variables used in the analysis. The current analyses focus on two outcomes measured at Wave 3—marijuana use and binge drinking. In terms of marijuana use, subjects were asked, “How often, if ever, do you use marijuana?” with the response set ranging from “Never” to “Once a day or more.” Due to the highly skewed distribution of the responses (more than 80% of subjects responded “Never” or “A few times a year”), we created a dichotomous variable reflecting whether or not the subject uses marijuana (0 = “No,” 1 = “Yes”) for use in subsequent analyses. To measure binge drinking, subjects were asked, “How many times, if at all, over the past two weeks have you drunk at least 5 drinks [4 for females] in the same night?” with the response set ranging from “Never” to “Five or more times.” As with marijuana use, the distribution of the responses was highly skewed (more than 82% of subjects responded “Never” or “Once or twice”), and thus we created a dichotomous measure reflecting whether or not the youth has drunk at least five (or four) drinks in one night in the past 2 weeks (0 = “No,” 1 = “Yes”). There is also theoretical justification for coding the outcomes as dichotomies, as prior research suggests religiosity has a stronger association with the prevalence as opposed to the frequency of substance use (e.g., Ulmer et al. 2012). Table 1. Descriptive Statistics Variable Mean SD Min. Max. N Dependent variables  Marijuana use (1 = yes) 0.30 0 1 2,443  Binge drinking (1 = yes) 0.47 0 1 2,445 Independent variables  Religiositya 1.31 0.76 0 3.41 2,285  Moral communitya 1.15 0.67 0 3.25 2,409 Control variables  Gender (1 = female) 0.51 0 1 2,458  Age 20.02 1.44 18 23 2,458 Race/ethnicity  Black 0.16 0 1 2,443  White 0.69 0 1 2,443  Hispanic 0.10 0 1 2,443  Other 0.05 0 1 2,443 No. of friends who drink or use drugs 1.68 1.69 0 5 2,440 Variable Mean SD Min. Max. N Dependent variables  Marijuana use (1 = yes) 0.30 0 1 2,443  Binge drinking (1 = yes) 0.47 0 1 2,445 Independent variables  Religiositya 1.31 0.76 0 3.41 2,285  Moral communitya 1.15 0.67 0 3.25 2,409 Control variables  Gender (1 = female) 0.51 0 1 2,458  Age 20.02 1.44 18 23 2,458 Race/ethnicity  Black 0.16 0 1 2,443  White 0.69 0 1 2,443  Hispanic 0.10 0 1 2,443  Other 0.05 0 1 2,443 No. of friends who drink or use drugs 1.68 1.69 0 5 2,440 aAfter construction, a constant equal to the scale’s minimum value was added to each case so the new minimum value would equal zero. View Large Table 1. Descriptive Statistics Variable Mean SD Min. Max. N Dependent variables  Marijuana use (1 = yes) 0.30 0 1 2,443  Binge drinking (1 = yes) 0.47 0 1 2,445 Independent variables  Religiositya 1.31 0.76 0 3.41 2,285  Moral communitya 1.15 0.67 0 3.25 2,409 Control variables  Gender (1 = female) 0.51 0 1 2,458  Age 20.02 1.44 18 23 2,458 Race/ethnicity  Black 0.16 0 1 2,443  White 0.69 0 1 2,443  Hispanic 0.10 0 1 2,443  Other 0.05 0 1 2,443 No. of friends who drink or use drugs 1.68 1.69 0 5 2,440 Variable Mean SD Min. Max. N Dependent variables  Marijuana use (1 = yes) 0.30 0 1 2,443  Binge drinking (1 = yes) 0.47 0 1 2,445 Independent variables  Religiositya 1.31 0.76 0 3.41 2,285  Moral communitya 1.15 0.67 0 3.25 2,409 Control variables  Gender (1 = female) 0.51 0 1 2,458  Age 20.02 1.44 18 23 2,458 Race/ethnicity  Black 0.16 0 1 2,443  White 0.69 0 1 2,443  Hispanic 0.10 0 1 2,443  Other 0.05 0 1 2,443 No. of friends who drink or use drugs 1.68 1.69 0 5 2,440 aAfter construction, a constant equal to the scale’s minimum value was added to each case so the new minimum value would equal zero. View Large Thus, we will be examining the association between religiosity and the odds of engaging in marijuana use and binge drinking, and whether or not the extent of a subject’s integration into a moral community moderates these associations. Although the age range of the current sample includes both late-adolescence and young adulthood, it is reasonable to consider these two groups similar to each other in terms of the outcomes being examined. As Bachman et al. (1997) note, although there are changes in life circumstances such as employment, marriage, family, and legal status over time, and thus some degree of change in substance use between adolescence and young adulthood, there is also quite a bit of within-individual stability in the prevalence of substance use between these stages. Thus, despite the potential weakening of the influence of primary networks on religiosity and drug use post-adolescence (e.g., Hoffmann 2014), actual rates of substance use appear to remain relatively stable. Nonetheless, as mentioned below, age will be used as a control in all multivariate models. Independent Variables We operationalized religiosity with the combination of six items from Wave 3. The first item, measuring how often the subject attends religious services, had a response set ranging from “Never” to “Once a week or more.” A second item measured the subject’s frequency of attendance at organized religious groups such as Bible study, prayer group, or another religious group, and had a response set ranging from “Never” to “More than once a week.” The next two items, measuring how often the subject reads the sacred scriptures of his or her religious tradition when alone and how often the subject prays alone, each had a response set ranging from “Never” to “Once a day or more.” A fifth item, measuring how distant or close the subject feels to God most of the time, had a response set ranging from “Very distant” to “Very close.” The final item, measuring how important religious faith is in shaping how the subject lives his or her daily life, had a response set ranging from “Not important at all” to “Extremely important.” We standardized the six items so they would all be on the same metric, and then calculated the mean of the items for each subject to arrive at an overall measure of religiosity (α = .858). We operationalized moral community with the combination of four items from Wave 3. The items include how many of the subject’s friends are religious, how many friends have beliefs about religion that are similar to the subject’s, how many friends are in a religious group to which the subject belongs, and how many friends does the subject talk with about religious belief and experience. Subjects were asked to think about up to five of their closest friends before this series of questions, therefore the response set for each of these items ranges from “0” to “5.” As with the religiosity scale, we standardized the five items and then calculated the mean of the items for each subject to arrive at an overall measure of moral community (α = .684). This measure reflects an individual’s integration into what we are calling a micro moral community, and provides a new way of both conceptualizing and operationalizing the concept of moral community. It adheres to network analysts’ emphasis on primary social networks (Fisher 1982; Wellman 1996), and reflects Stark et al.’s (1982:15) comment that, “ . . . [W]e experience membership in a moral community as an aspect of our immediate social setting.” To facilitate the examination of whether or not the effect of religiosity is moderated by the level of an individual’s integration into a moral community, we created a second measure of moral community by recoding the original scale into a three category variable. The categories reflect whether the subject was in the lowest 25%, middle 50%, or upper 25% of the moral community distribution, and are thus indicative of each subject’s relative integration into a moral community. Table 2 contains the mean and standard deviation for each of the items that comprise the moral community scale across the three categories. Table 2. Descriptive Statistics for Individual Moral Community Items across Scores on the Moral Community Scale Item Lowest 25% on Moral Community Scale (n = 618) Middle 50% on Moral Community Scale (n = 1,186) Highest 25% on Moral Community Scale (n = 605) Mean (SD) Mean (SD) Mean (SD) Number of religious friends 0.82 (0.90) 1.89 (1.34) 4.13 (1.08) Number of friends with similar beliefs to subject 1.06 (0.91) 2.52 (1.30) 4.08 (0.95) Number of friends in religious group with subject 0.04 (0.19) 0.34 (0.66) 2.03 (1.74) Number of friends subject talks with about religion 0.58 (0.82) 2.00 (1.56) 3.39 (1.59) Item Lowest 25% on Moral Community Scale (n = 618) Middle 50% on Moral Community Scale (n = 1,186) Highest 25% on Moral Community Scale (n = 605) Mean (SD) Mean (SD) Mean (SD) Number of religious friends 0.82 (0.90) 1.89 (1.34) 4.13 (1.08) Number of friends with similar beliefs to subject 1.06 (0.91) 2.52 (1.30) 4.08 (0.95) Number of friends in religious group with subject 0.04 (0.19) 0.34 (0.66) 2.03 (1.74) Number of friends subject talks with about religion 0.58 (0.82) 2.00 (1.56) 3.39 (1.59) View Large Table 2. Descriptive Statistics for Individual Moral Community Items across Scores on the Moral Community Scale Item Lowest 25% on Moral Community Scale (n = 618) Middle 50% on Moral Community Scale (n = 1,186) Highest 25% on Moral Community Scale (n = 605) Mean (SD) Mean (SD) Mean (SD) Number of religious friends 0.82 (0.90) 1.89 (1.34) 4.13 (1.08) Number of friends with similar beliefs to subject 1.06 (0.91) 2.52 (1.30) 4.08 (0.95) Number of friends in religious group with subject 0.04 (0.19) 0.34 (0.66) 2.03 (1.74) Number of friends subject talks with about religion 0.58 (0.82) 2.00 (1.56) 3.39 (1.59) Item Lowest 25% on Moral Community Scale (n = 618) Middle 50% on Moral Community Scale (n = 1,186) Highest 25% on Moral Community Scale (n = 605) Mean (SD) Mean (SD) Mean (SD) Number of religious friends 0.82 (0.90) 1.89 (1.34) 4.13 (1.08) Number of friends with similar beliefs to subject 1.06 (0.91) 2.52 (1.30) 4.08 (0.95) Number of friends in religious group with subject 0.04 (0.19) 0.34 (0.66) 2.03 (1.74) Number of friends subject talks with about religion 0.58 (0.82) 2.00 (1.56) 3.39 (1.59) View Large Comparing the association between religiosity and binge drinking and marijuana use across subjects in each of the three categories will allow us to test the moral community hypothesis, and provide the first test of this hypothesis with a measure of micro moral community. The hypothesis would predict that the association will be strongest for those in the upper 25% of the moral community distribution, and weakest for those in the lowest 25%. Recognizing that there is conceptual overlap between many of the items included in the religiosity and moral community scales, and as a further check on our decisions of which items to include in which scale, we conducted exploratory factor analysis with all ten items that comprised both scales (detailed results available upon request). Only two factors were extracted, and the makeup of these factors matched our two scales. Specifically, all six religiosity items loaded on the same factor, with loadings ranging from .523 to .859, and all four moral community items loaded on the same factor, with loadings ranging from .568 to 882. As another check on our decisions, we also ran two separate exploratory factor analyses—one for only the six religiosity items and a second for only the four moral community items. In both analyses only one factor was extracted, and the loadings ranged from .613 to .840 on the single factor extracted in the analysis of the religiosity items, and from .645 to .770 on the single factor extracted in the analysis of the moral community items (detailed results available upon request). Several control variables were used in each of the multivariate models. Specifically, the demographic variables of gender (0 = Male; 1 = Female), age (at Wave 3), and race and ethnicity were controlled. Race and ethnicity were measured with a series of dichotomous variables for Black, White, Hispanic, and Other (the variable for White was entered into each model). The number of friends who use drugs or drink heavily (maximum = 5) was also controlled based upon the subjects’ self-report in Wave 3. Plan of Analysis The analysis that follows consists of two sets of multivariate logistic regressions predicting binge drinking and marijuana use, respectively, and all procedures were weighted with the appropriate sampling weight. In order to establish associations between each of the outcomes and religiosity and moral community, each set of regressions began with the estimation of a model including religiosity, moral community, and all of the controls for the full sample. Following this, three separate models were run for each outcome, including religiosity and all controls, for subjects in the lowest 25% on the moral community measure, those in the middle 50%, and those in the upper 25%. A z-score for comparing coefficients across equations (Paternoster et al. 1998) was used to test for statistically significant differences in the religiosity coefficients across the three models. Constructing the trichotomous moral community variable and comparing the impact of religiosity across the three groups, as opposed to using a multiplicative interaction term between religiosity and the continuous version of moral community, allows us to test for differences across discrete levels of moral community, such as high, medium, and low. This reflects the expectation that the difference between someone who is high and someone who is low on moral community is more meaningful than differences along a more continuous scale such as, for example, between someone who has a 2.2 and someone who has a 2.4 on the moral community scale, which a multiplicative interaction term would be testing for. As Paternoster et al. (1998:860) note, this is a common approach for which “ . . . there has been considerable consensus in the criminological literature with respect to the appropriateness of this coefficient-comparison strategy in examining what is essentially an interactive effect . . .” As stated earlier, it is expected that the strength of the association between religiosity and each of the outcomes will increase as integration into a moral community increases. Thus, the religiosity coefficient is expected to be strongest in the model for those in the upper 25% of moral community, and weakest for those in the lowest 25%. RESULTS Table 3 contains the results for the set of multivariate logistic regressions predicting binge drinking. The results for model 1 reveal a statistically significant association between binge drinking and religiosity, controlling for each of the factors discussed in the previous section. Specifically, since odds ratios of less than one indicate a decrease in the odds of the outcome occurring, higher religiosity is associated with lower odds of having engaged in binge drinking in the past 2 weeks. The moral community measure was not statistically significant in model 1. Table 3. Odds Ratios from Multivariate Logistic Regression Models Predicting Binge Drinkinga Variables Model 1: full sample Model 2: Lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.393*** 0.848 0.443*** 0.224*** Moral community 0.991 — — — Nb 2,229 558 1,075 596 Model chi-square 208.09*** (df = 6) 30.25*** (df = 5) 155.48*** (df = 5) 75.07*** (df = 5) Variables Model 1: full sample Model 2: Lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.393*** 0.848 0.443*** 0.224*** Moral community 0.991 — — — Nb 2,229 558 1,075 596 Model chi-square 208.09*** (df = 6) 30.25*** (df = 5) 155.48*** (df = 5) 75.07*** (df = 5) aAll models control for the following variables: gender, age, race/ethnicity, and number of friends who use drugs or drink heavily. Detailed results for all controls are available upon request. bThe sample size differs from 2,458 in the full sample model, and from 25% or 50% of the full sample in subsequent models due to listwise deletion of missing cases. *p < .05; **p < .01; ***p < .001. View Large Table 3. Odds Ratios from Multivariate Logistic Regression Models Predicting Binge Drinkinga Variables Model 1: full sample Model 2: Lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.393*** 0.848 0.443*** 0.224*** Moral community 0.991 — — — Nb 2,229 558 1,075 596 Model chi-square 208.09*** (df = 6) 30.25*** (df = 5) 155.48*** (df = 5) 75.07*** (df = 5) Variables Model 1: full sample Model 2: Lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.393*** 0.848 0.443*** 0.224*** Moral community 0.991 — — — Nb 2,229 558 1,075 596 Model chi-square 208.09*** (df = 6) 30.25*** (df = 5) 155.48*** (df = 5) 75.07*** (df = 5) aAll models control for the following variables: gender, age, race/ethnicity, and number of friends who use drugs or drink heavily. Detailed results for all controls are available upon request. bThe sample size differs from 2,458 in the full sample model, and from 25% or 50% of the full sample in subsequent models due to listwise deletion of missing cases. *p < .05; **p < .01; ***p < .001. View Large Models 2 through 4 in table 3 contain results for the subsamples in the lowest 25%, middle 50%, and upper 25% on moral community, respectively. The coefficient for religiosity is statistically significant for those in the upper 25% and middle 50% of moral community, with higher levels of religiosity associated with lower odds of binge drinking for both subsamples, and is not statistically significant for those in the lowest 25%. With an odds ratio approximately 50–75% smaller compared to the other two subsamples, and since odds ratios further from one indicate potentially stronger associations, the association appears to be strongest for the subsample in the upper 25% on moral community. The z-score used to test for differences in the religiosity coefficients across models (table 4) reveals that the coefficient for the upper 25% subsample differs significantly from the coefficient for both the middle 50% and lowest 25% subsamples. Thus, the association between religiosity and binge drinking is indeed strongest for the upper 25% subsample. The difference between the coefficients for the middle 50% and lowest 25% subsamples was also statistically significant, indicating a stronger association between religiosity and binge drinking for those in the middle 50% compared to the lowest 25%. Table 4. Z-scores Comparing Religiosity Coefficient across Subsamples Reflecting Different Levels of Moral Community; Binge Drinking Models Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 3.20** 5.09*** 2.48* Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 3.20** 5.09*** 2.48* *p < .05; **p < .01; ***p < .001. View Large Table 4. Z-scores Comparing Religiosity Coefficient across Subsamples Reflecting Different Levels of Moral Community; Binge Drinking Models Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 3.20** 5.09*** 2.48* Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 3.20** 5.09*** 2.48* *p < .05; **p < .01; ***p < .001. View Large Figure 1 graphically represents the different magnitudes of association between religiosity and binge drinking across the levels of moral community. Specifically, the bars represent the estimated percent reduction in odds of binge drinking associated with a one unit increase in religiosity for each moral community subsample. The estimated percent reduction in odds was calculated by subtracting 1 from the odds ratio for the religiosity variable in the relevant model (Long 1997). FIGURE 1. View largeDownload slide Estimated percent reduction in odds of binge drinking with each one-unit increase in religiosity, by moral community subsample. FIGURE 1. View largeDownload slide Estimated percent reduction in odds of binge drinking with each one-unit increase in religiosity, by moral community subsample. Table 5 contains the results for the set of multivariate logistic regressions predicting marijuana use. The results for model 1 reveal a statistically significant association between marijuana use and religiosity, and no significant association between marijuana use and moral community, controlling for each of the factors discussed in the previous section. Specifically, higher religiosity is associated with lower odds of using marijuana. Table 5. Odds Ratios from Multivariate Logistic Regression Models Predicting Marijuana Usea Variables Model 1: full sample Model 2: lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.383*** 0.808 0.380*** 0.231*** Moral Community 1.144 — — — Nb 2,226 556 1,075 595 Model Chi-Square 167.22*** (df = 6) 35.21*** (df = 5) 77.05*** (df = 5) 82.31*** (df = 5) Variables Model 1: full sample Model 2: lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.383*** 0.808 0.380*** 0.231*** Moral Community 1.144 — — — Nb 2,226 556 1,075 595 Model Chi-Square 167.22*** (df = 6) 35.21*** (df = 5) 77.05*** (df = 5) 82.31*** (df = 5) aAll models control for the following variables: gender, age, race/ethnicity, and number of friends who use drugs or drink heavily. Detailed results for all controls are available upon request. bThe sample size differs from 2,458 in the full sample model, and from 25% or 50% of the full sample in subsequent models due to listwise deletion of missing cases. *p < .05; **p < .01; ***p < .001. View Large Table 5. Odds Ratios from Multivariate Logistic Regression Models Predicting Marijuana Usea Variables Model 1: full sample Model 2: lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.383*** 0.808 0.380*** 0.231*** Moral Community 1.144 — — — Nb 2,226 556 1,075 595 Model Chi-Square 167.22*** (df = 6) 35.21*** (df = 5) 77.05*** (df = 5) 82.31*** (df = 5) Variables Model 1: full sample Model 2: lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.383*** 0.808 0.380*** 0.231*** Moral Community 1.144 — — — Nb 2,226 556 1,075 595 Model Chi-Square 167.22*** (df = 6) 35.21*** (df = 5) 77.05*** (df = 5) 82.31*** (df = 5) aAll models control for the following variables: gender, age, race/ethnicity, and number of friends who use drugs or drink heavily. Detailed results for all controls are available upon request. bThe sample size differs from 2,458 in the full sample model, and from 25% or 50% of the full sample in subsequent models due to listwise deletion of missing cases. *p < .05; **p < .01; ***p < .001. View Large Models 2 through 4 in table 5 reveal that the religiosity coefficient is statistically significant for those in the upper 25% and middle 50% of moral community, with higher levels of religiosity associated with lower odds of marijuana use for both subsamples, and is not statistically significant for those in the lowest 25% of moral community. With the smallest odds ratio (approximately 40–70% smaller), the association appears to be strongest for those in the upper 25% on moral community compared to the middle 50% and lowest 25%. The z-scores (table 6) indicate that the coefficient for the upper 25% subsample does differ significantly from the coefficients for both the middle 50% and lowest 25% subsamples, indicating that the association between religiosity and marijuana is significantly stronger for the upper 25% compared to the other two subsamples. Further, the difference between the middle 50% and lowest 25% subsamples is also statistically significant, indicating the association between religiosity and marijuana use is stronger for the middle 50% versus the lowest 25% subsample. Table 6. Z-scores Comparing Religiosity Coefficient across Subsamples Reflecting Different Levels of Moral Community; Marijuana Use Models Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 2.26* 5.05*** 3.01** Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 2.26* 5.05*** 3.01** *p < .05; **p < .01; ***p < .001. View Large Table 6. Z-scores Comparing Religiosity Coefficient across Subsamples Reflecting Different Levels of Moral Community; Marijuana Use Models Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 2.26* 5.05*** 3.01** Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 2.26* 5.05*** 3.01** *p < .05; **p < .01; ***p < .001. View Large Figure 2 graphically represents the different magnitudes of association between religiosity and marijuana across the levels of moral community. Specifically, the bars represent the estimated percent reduction in odds of marijuana use associated with a one unit increase in religiosity for each moral community subsample. FIGURE 2. View largeDownload slide Estimated percent reduction in odds of marijuana use with each one-unit increase in religiosity, by moral community subsample. FIGURE 2. View largeDownload slide Estimated percent reduction in odds of marijuana use with each one-unit increase in religiosity, by moral community subsample. It is important to note that we ran multicollinearity diagnostics for all of the models predicting binge drinking and marijuana use, and there are no problems with multicollinearity as all variance inflation factors were under 1.9. DISCUSSION AND CONCLUSIONS The moral community hypothesis predicts that adherence to religious norms against deviance will be stronger when religious individuals participate in a moral community, or a social setting in which religious norms are accepted and reinforced through interactions (Stark 1996; Stark et al. 1982). Conceptualizing moral communities as religious regions, counties, or institutions does not allow researchers to account for the integration of religious individuals into small groups or micro moral communities. Religious individuals residing in irreligious communities may participate in micro moral communities or those living in highly religious communities may fail to meaningfully interact. We posit that integration into a micro moral community increases the protective effect of religiosity on deviant behavior. Social routines within these small groups may be the primary mechanisms for transferring and reinforcing religious norms prohibiting deviant behavior. Relying on data from Wave 3 of the NSYR, which contains rich measures of personal networks and religiosity, our measure of micro moral community accounts for both the number of religious friends a person has within a limited, egocentric network and the nature of interactions between these individuals. This approach maintains that social integration into a community is relational and can be captured by measuring social ties within limited and extended networks. Religious individuals participating in micro moral communities routinely interact with a small number of co-religionists who discuss matters of faith. Consistent with previous research (e.g., Kelly et al. 2015; Michalak et al. 2007; Ulmer et al. 2012; Willis et al. 2003), we find that religiosity has a significant inverse relationship with the likelihood of engaging in both binge drinking and marijuana use. We also find that greater integration into a micro moral community strengthens the inverse association between religiosity and both binge drinking and marijuana use. Thus, we find support for the moral community hypothesis for both outcomes. By using egocentric measures to account for social integration into micro moral communities, this study provides a new test of the moral community hypothesis. Since micro moral communities are positioned within variably religious broader communities, future research can build on this test with a closer examination of the interaction between these two “types” of communities. Integration into a micro moral community may be directly related to the existence of a broader moral community. In highly religious institutions, counties, or regions, co-religionists have more opportunity to closely interact and are more likely to participate in a micro moral community. These small groups of co-religionists reinforce religious norms and enhance the protective effect of religiosity against deviant behavior. The prominence of micro moral communities within highly religious areas may explain the general effect of moral communities at various levels of aggregation. However, micro moral communities are not just limited to highly religious areas, as co-religionists are likely to interact even in relatively irreligious areas (e.g., Fischer 1982). Some religious individuals may even strengthen their faith when immersed in more religiously heterogeneous settings (e.g., Hammond and Hunter 1984; Olsen 2008; Smith and Emerson 1998). In relatively irreligious institutions, counties, or regions, interactions between religious individuals are limited by opportunity, but micro moral communities in these settings may provide a stronger boost to the protective effect of religiosity than those in more religious areas. Contrasting norms between small religious groups and the surrounding social milieu may intensify social processes that maintain and reinforce religious norms. The integration of small religious groups/small group participants into the broader social fabric merits further attention. The relationship between broader moral communities and micro moral communities is more complex than accounting for the absence or existence of religious adherence. Even basic denominational or theological differences complicate efforts to understand small group integration into the broader social milieu. For example, studies on the relationship between binge drinking and denominational affiliation find substantial variations across denominations (e.g. Michalak et al. 2007). Some broad moral communities may be dominated by denominations or theological orientations that are more accepting of binge drinking while others may be more diverse or generally unaccepting of such activities. Micro moral communities may also vary by denominational or theological make-up, which influences small group norms about drinking. Social networks comprised primarily of Catholics may adhere to different norms than those involving Evangelical Protestants, Mainline Protestants, Mormons, or Muslims. Yet, personal networks for all such faiths variably exist within regions, counties, or institutions. Conceptualizing moral communities at both the broad community level and the micro level requires a more thorough examination of how these two levels of measuring community interrelate to influence norms of deviant behavior. There are a few limitations to the current study which should be noted, and which could be addressed with future research. First, although chosen for the theoretical reasons addressed earlier, the cross-sectional nature of the analysis prevents us from drawing causal inferences about the impact of religiosity on deviant behavior, and the moderating effect of moral community. Second, based on available data, the current outcomes do not include measures of serious crime, and instead are limited to two measures of substance use. Although these are certainly important, it would be useful to extend future analyses to include more serious forms of crime including index crimes and hard drug use. Third, the data does not test the effect of micro moral communities beyond emerging adulthood, and, given that the influence of group socialization on both religious norms and substance abuse may weaken with age (e.g., Hoffman 2014), our findings may be limited to a narrow age range. In addition to addressing these limitations, future research can also pursue two additional issues. First, it would be useful to examine denomination-specific models of the interaction between religiosity and moral community. Second, mapping the development of individual religiosity and integration into a moral community over time using techniques such as growth-curve modeling or group-based trajectory modeling (e.g., Nagin 2005) would be particularly informative. Researchers can then examine how the identified trajectories of both concepts inter-relate, and how developmental patterns of religiosity and moral community integration interact, if at all, in protecting against involvement in deviant behavior. ACKNOWLEDGMENTS The authors would like to thank the Editor and reviewers for their constructive and insightful comments on the manuscript. The National Study of Youth and Religion, http://youthandreligion.nd.edu/, whose data were used by permission here, was generously funded by Lilly Endowment, Inc., under the direction of Christian Smith of the Department of Sociology at the University of Notre Dame. REFERENCES Adamczyk , Amy . 2012 . “ Understanding Delinquency with Friendship Group Religious Context .” Social Science Quarterly 93 : 483 – 505 . Adamczyk , Amy , and Ian Palmer . 2008 . “ Religion and Initiation into Marijuana Use: The Deterrent Role of Religious Friends .” Journal of Drug Issues 38 : 717 – 41 . Google Scholar CrossRef Search ADS Andrews , Judy A. , Elizabeth Tildesley , Hyman Hops , and Fuzhong Li . 2002 . “ The Influence of Peers on Young Adult Substance Use .” Health Psychology 21 : 349 – 57 . 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Sandy . 2003 . “ Buffering Effect of Religiosity for Adolescent Substance Use .” Psychology of Addictive Behaviors 17 : 24 – 31 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Association for the Sociology of Religion. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sociology of Religion Oxford University Press

Religiosity, Marijuana Use, and Binge Drinking: A Test of the Moral Community Hypothesis

Sociology of Religion , Volume 79 (3) – Oct 1, 2018

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of the Association for the Sociology of Religion. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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1069-4404
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1759-8818
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10.1093/socrel/srx071
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Abstract

Abstract We use data from Wave 3 of the National Study of Youth and Religion (NSYR), a nationally representative study of adolescents and emerging adults, to examine the association between religiosity and marijuana use and binge drinking, as well as the importance of social context for these associations. Specifically, we test the moral community hypothesis, originally stated by Stark and colleagues, using a micro-level conceptualization of moral community. We find that higher levels of religiosity are associated with lower odds of engaging in both marijuana use and binge drinking, and that an individual’s level of integration into a moral community moderates the association between religiosity and both outcomes. Implications of our findings are discussed. BACKGROUND A substantial amount of research over the last 40 years has noted an inverse relationship between religiosity and criminal or deviant behavior (Baier and Wright 2001; Johnson 2012; Johnson et al. 2000; Kelly et al. 2015). This trend occurs for more serious crimes (Benda and Toombs 2000; Evans et al. 1995; Jang and Johnson 2001), but is particularly noticeable for less serious forms of deviance that violate religious rather than secular moral proscriptions (Benda 1997; Benda and Corwyn 1997; Cochran and Ackers 1989; Eitle 2011; Ulmer et al. 2012; Wallace et al. 2007). Although theoretical explanations of these findings vary (Baier and Wright 2001), the moral community hypothesis posits an interaction between personal religiosity and social integration into a religious community (i.e., “moral community”) in reducing deviant behavior (Stark 1996; Stark et al. 1982). These communities reinforce religious norms, thereby enhancing the protective effects of an individual’s religiosity. In statistical terms, integration into a moral community moderates the impact of personal religiosity on behavior. Research on the moral community hypothesis has been mixed; some studies find that religious environments strengthen the protective effects of individual-level religiosity (Finke and Adamczyk 2008; Gault-Sherman and Draper 2012; Lee and Bartowski 2004; Regenarus 2003; Stack and Kposowa 2006, 2011; Welch et al. 1991) while others do not (e.g., Bahr and Hoffman 2008; Eitle 2011; Sturgis 2010; Sturgis and Baller 2012). By operationalizing “moral community” through aggregate levels of religiosity within institutions, counties, geographical regions, and nations (e.g., Eitle 2011; Finke and Adamczyk 2008; Gault-Sherman and Draper 2012; Regenarus 2003; Stack and Kposowa 2006; Stark 1996; Stark et al. 1982; Sturgis 2010; Sturgis and Baller 2012), researchers presume that religious people living in predominantly religious areas are more integrated into a moral community than those living in irreligious areas. This approach negates personal connections between co-religionists that facilitate the expression and dissemination of social norms, or small groups that can function as a “moral community.” The following study accounts for this problem, and revisits the moral community hypothesis, by examining personal networks of adolescents and young adults to determine if the association between religiosity and binge drinking and marijuana use is moderated by the degree to which the individual is connected to religious others. Given that substance abuse is common and peers are particularly influential during adolescence and emerging adulthood (Brechwald and Prinstein 2011; Brown and Larson 2009; Smetana et al. 2006; Tucker et al. 2005), the impact of primary networks on the behavior of religious youth is an important topic. We rely on data from the National Study of Youth and Religion, a nationally representative study of religiosity among adolescents and young adults with rich measures of both personal networks and religious faith, to conduct our analyses. Moral Communities and Micro Moral Communities Stark et al. (1982) developed the moral community hypothesis to reconcile the findings of early studies that found religiosity had a significant effect on delinquency with those that did not (e.g., Burkett and White 1974; Higgins and Albrecht 1977; Hirschi and Stark 1969). Borrowing from Durkheim’s (1995) ideas that religion has an integrative effect on community members, Stark and colleagues hypothesized that the impact of individual religiosity on deviant behavior is stronger in “moral communities,” where religious commitment is the norm and religious influences on daily life are pervasive (Stark 1996; Stark et al. 1982). Early studies that found religion had no impact on delinquency relied on samples drawn from the more secular West Coast (Burkett and White 1974; Hirschi and Stark 1969), while the study noting a significant inverse relationship relied on a sample drawn from the South where religious commitment is more of a norm (Higgins and Albrect 1977). Since the South, and specifically Atlanta in the case of Higgins and Albrecht’s study, represents more of a “moral community” than does the West Coast, one would expect to find an effect of religiosity in the former but not the latter (Stark et al. 1982). The moral community hypothesis, therefore, argues that the extent to which a person is integrated into a moral community may influence the association between religiosity and delinquency. It predicts that where religion is pervasive and a religious sanctioning system is expressed in daily life (i.e., moral communities), the degree to which an individual adheres to this system, which is a direct reflection of his or her degree of religious faith, will influence the likelihood of engaging in deviant behavior (Stark et al. 1982). Environments with high concentrations of religious people provide the networks and shared beliefs that facilitate compliance to religious norms (Hoffman and Bahr 2006). Yet Stark (1996:164) notes that, “religion is empowered to produce conformity to the norms only as it is sustained through interaction and is accepted by the majority as a valid basis for action.” Where religion is not a prominent part of the cultural landscape, and a religious sanctioning system is not prevalent, the everyday expression of religious life will be stifled and religiosity will be less salient in a person’s life (Stark et al. 1982). Religious individuals isolated from a moral community may not experience the protective effect of religiosity. The moral community hypothesis is centrally concerned with social integration into religious communities, which is a complex process involving vague theoretical terms. Although “community” implies similarity, interaction, and geographical proximity between a set of individuals, the degree to which these elements must be present to establish a community is unclear (Leighton 1988). Some scholars determine the parameters of community by empirically identifying concentrations of interaction patterns within the broader social structure (Leighton 1988; Newman and Girvan 2004; Wellman 1979, 1996). Social integration is relational, denoting either a level of connectedness between individuals or the degree to which a specific person is connected to the surrounding network (Berkman et al. 2000; Brissette et al. 2000; House et al. 1988). Other researchers define and describe communities by relying on aggregated characteristics within a geographical area (Leighton 1988). This approach maintains the relational nature of integration by assuming the degree to which a characteristic is present within a setting predicts individual and community-level social connectedness. Stark et al. (1982) originally conceptualized moral communities as social groups with a religious sanctioning system that is expressed in daily life. However, their theoretical use of “group” or “community” is not clearly defined and includes both broad social ecologies where religiosity is prominent and small social groups of co-religionists (Stark 1996). Researchers have subsequently measured aggregate levels of religiosity between nations, U.S. regions, counties, schools, or prisons to determine the influence of moral communities (Eitle 2011; Finke and Adamczyk 2008; Gault-Sherman and Draper 2012; Regenarus 2003; Stack and Kposowa 2006, 2011; Stark 1996; Stark et al. 1982; Sturgis 2010; Sturgis and Baller 2012). These studies account for the relative similarity of religious beliefs (i.e., the degree of religious integration) within broad communities, but they do not explicitly measure the extent to which specific individuals are connected to co-religionists. They assume that religious individuals residing in generally religious areas are more integrated into a moral community than those living in less religious areas. But this assumption may be flawed. Religious individuals may be isolated in highly religious areas or integrated into small religious groups in irreligious areas, a point that has largely been ignored in studies that examine the moral community hypothesis. The integration of religion into geographical areas or bounded institutions may influence but not determine individual-level integration into moral communities. Where religiosity is more concentrated within broad communities, religious individuals will likely have more options to participate in smaller religious groups (Olsen and Perl 2010; Pescosolido 1990). Social networks are not geographically liberated, and one’s opportunity for interaction is limited by the surrounding social structure, circumstances, and availability of co-religionists (Fischer 1982; Pescosolido 1990; Wellman 1996). This does not mean that locations lacking religious prominence necessarily stifle religious expression, forcing individuals to compartmentalize their religious lives. Fischer’s (1982) analysis of social ties within the San Francisco Bay area noted that while people living in the urban center were less likely to be religious, those who were religious also reported a high involvement with religious others. There is some evidence to suggest in such circumstances the protective effect of religion may be stronger than it is in communities where religion is more pervasive (Tittle and Welsh 1983). An abundance of research also finds that personal commitment to a religious group increases as the prevalence of that group diminishes within the surrounding area (e.g., Hammond and Hunter 1984; Olsen 2008; Smith and Emerson 1998). Where religion is not a prominent feature of a geographical area, religious individuals appear to find ways to interact with each other and such interpersonal connections may influence behavior. Religion organizes social relationships so that networks of interacting co-religionists may thrive even in predominantly irreligious areas. Co-religionists are drawn to each other through similar belief systems and centralized organizations (DiPrete et al. 2011; Graham and Haidt 2010; McPherson et al. 2001; Olsen and Perl 2010). Churches provide a primary social setting for co-religionists to interact regardless of religious sentiments of the surrounding community. For example, Olsen and Perl (2010) find that the number of friends a person has within a given congregation is not influenced by the concentration of religiosity within a region. To explain such effects, some scholars argue that participation within a congregation structures one’s activities and social options to create dense, localized social networks for both adults and adolescents (Smith 2003). Others note that some theological orientations advocate in-group preference and out-group exclusion (Scheitle and Adamczyk 2009). Social ties within Evangelical Protestant denominations are particularly dense causing congregants to spend much of their social energy on church-related rather than community activities (Putnum 2000, see also Beyerlien and Hipp 2005). The effects of religiosity on network homophily also extend beyond churches, as religious teenagers are closer to and spend more time with religious peers within their school settings (Cheadle and Schwadel 2012). Such personal networks are an important facet of religious life, regardless of the level of religious integration in a region, county, or institution. Adolescent Personal Networks and Substance Use Network analysts manage the challenges of studying social integration by examining primary social networks, or small groups of intimately connected individuals, which allows them to conceptualize community from the micro-level and then, if desired, identify integration patterns in more expansive networks (Fisher 1982; Wellman 1996). Accounting for primary networks also allows researchers to determine the degree to which a person is connected to peers, the characteristics of those peers, the cultural activities of group life, and the influence of group life on personal conduct. These attributes of small group participation are relevant for determining how integrated a person is into a social group with a religious sanctioning system that is expressed in daily life, or a moral community (Stark 1996; Stark et al. 1982). Primary networks are especially influential during adolescence and emerging adulthood when peers become an important source for the establishment of cultural frameworks, social norms, and social identity (e.g., Brechwald and Prinstein 2011; Brown and Larson 2009; Smetana et al. 2006). Socialization is a collaborative process (e.g., Corsaro and Eder 1990), and adolescents engage in communicative events like gossip, storytelling, and teasing to construct and communicate moral evaluations, meaningful labels, and idealized identities that contribute to the normative expectations of group life (Bucholtz 1999; Fine 1986; Kyratzis 2004; Shuman 1986). The make-up of one’s primary network during adolescence thus matters, as do the types of conversations that occur within the group. What peers talk about can shape who they are and what they do. The influence of these group processes may translate to primary networks of co-religionists, especially if individuals participate in informal conversations about faith outside of formal religious settings. Although some scholars argue social ties function as a social control mechanism that reduces delinquency (Hirschi 2002), most studies find that both the degree of integration and the type of network one is connected to influences individual level delinquency (Haynie 2001; Reynolds and Crea 2015). Haynie (2001), for example, found that both network centrality and density with a delinquent peer network is associated with personal delinquency among adolescents. The existence of close social ties does not reduce delinquency independent of the cultural practices of group life. This finding also translates to the relationship between social networks and substance abuse. Research consistently finds an association between peer and personal use of illicit substances among adolescents and emerging adults (e.g., Andrews et al. 2002; Galea et al. 2004; Kirke 2004; Mason and Windle 2001), leading scholars to note that patterns of socialization within social networks partially explain these findings (Brechwald and Prinstein 2011). The influence of primary networks during adolescence and emerging adulthood suggests that participating in religious networks may enhance the protective effect of religiosity on individual-level deviance (i.e., substance abuse) through the reproduction of cultural norms. Extant research confirms both that religiosity and associations with religious peers reduces illicit substance use and abuse (e.g., Bartowski and Xu 2007; Thompson 2016). There is also evidence that religious reference groups (i.e., denominations) socialize congregants differently, which influences alcohol consumption (Beeghley et al. 1990; Bock et al. 1987; Cochran et al. 1992). Yet, the few studies that use network data to examine how primary networks intersect with personal religiosity to influence deviance yield mixed results. Although Adamczyk and Palmer (2008:731) find that friends’ born-again identity does not “strengthen the relationship between individual born-again identity and marijuana initiation,” which contradicts the moral communities hypothesis, other studies identify that religious primary networks enhance the protective effects of religiosity on deviance (e.g., Adamczyk 2012; Desmond et al. 2011). Hoffman (2014), for example, found reciprocal effects between religiousness, religious peers, and moral schemas that are inversely related to marijuana use during adolescence. Yet, the effects of religious peers on both personal religiosity and marijuana use weakened when individuals transitioned into young adulthood, leading Hoffman to suggest that religious identity and marijuana use may be less influenced by primary networks during post-adolescent years. The protective effect of moral communities may be strongest during adolescence when peers are particularly influential and substance use/abuse is especially high (Brechwald and Prinstein 2011; Brown and Larson 2009; Smetana et al. 2006; Tucker et al. 2005). Current Study Broad applications of community cannot account for individual-level integration into small networks of religious individuals or what we call micro moral communities. These are concentrated networks of interaction between religiously similar individuals in which religious norms are openly expressed. From the perspective of the individual, a micro moral community provides access to a small number of religious others who can share in the expression of religious ideas. Scholars examining moral communities have generally ignored the potential influence of personal communities on the link between religiosity and deviant conduct. The few studies that use ego centric network data to examine the intersection between religiosity, primary networks, and deviance provide important information, but they do not explicitly test the moral community hypothesis (e.g., Adamczyk 2012; Adamczyk and Palmer 2008; Hoffman 2014). Stack and Kposowa’s (2011) test of the moral community hypothesis provides a rare exception, finding that personal connections to co-religionists reduces the acceptability of suicide among religious individuals. However, there is a dearth of research examining the moderating influence of micro moral communities on the association between religiosity and deviance. This study addresses that problem by using personal networks of adolescents and emerging adults to measure the extent of integration in a moral community and then examining whether or not the associations between religiosity and binge drinking and marijuana use are moderated by integration into this micro moral community. Research Questions The current study is guided by two primary research questions: First, is individual religiosity associated with a reduction in binge drinking and marijuana use? Once such an association has been established, our second question involves a test of the moral community hypothesis: Are any associations between religiosity and binge drinking and marijuana use moderated by integration into a moral community, conceptualized here as a micro moral community? Specifically, does the negative association between religiosity and deviant behavior strengthen as integration into these micro moral communities increases? If the moral community hypothesis is to be supported, we would expect a negative association between religiosity and participation in deviant behavior, and that this association strengthens as integration into a micro moral community increases. METHODS Data The current study is based on data from Wave 3 of the National Study of Youth and Religion (NSYR). The NSYR is a multi-wave, nationally representative study of adolescents and emerging adults. Data collection included telephone surveys with individual subjects, containing questions on religious beliefs and practices, family and school life, and a range of developmental issues. Random digit dialing and in-home randomization of subjects were used to achieve a nationally representative sample. An oversample of 80 Jewish households that was included in the original data collection were removed from the current sample in the interest of maintaining national representativeness, and are not included in any demographics, descriptive statistics, or analyses reported from this point on. For a detailed description of the sample and data collection techniques of the NSYR, see Smith and Denton (2005) and Smith and Snell (2009). Wave 3 surveys were conducted between September 2007 and April 2008, when the subjects’ ages ranged from 18 to 23 years old and 6.3% of the sample was still in high school. The sample (n = 2,458) contained 48.7% male and 51.3% female respondents, and the racial and ethnic breakdown was 69.1% White, 16.0% Black, 9.9% Hispanic, and 5.0% Other. Wave 3 was chosen for analysis due to a combination of factors. First, this wave contains detailed social network items that will be used in the moral community measure that were not available at all previous waves. Second, the ages of respondents during Wave 3 made them more prone to drinking and marijuana use compared to the previous two waves when subjects were in their early to mid-teens. Prior research suggests the prevalence and frequency of both behaviors increase throughout the teenage years, and although they can plateau they often do not decrease until late 20s or early 30s (Bachman et al. 1997; Johnston et al. 2013; O’Malley et al. 1984). We chose to do a cross-sectional analysis for theoretical reasons. When predicting an association between religiosity and behavior, the implication is that an individual’s religiosity today, not 2 or 3 years ago, is what affects his current behavior. Although it is logical to expect that prior religiosity will also be related to current behavior, that effect would most likely be working through current religiosity. In other words, if an individual’s level of religiosity 2 or 3 years ago is related to his binge drinking and marijuana use today, it is logical to conclude that this is because prior religiosity is related to current religiosity, which in turn is related to current drinking and drug use. The same reasoning holds when adding the moral community measure into the mix, and investigating whether or not the effect of religiosity is moderated by the extent of one’s integration into a moral community. The implication of the moral community hypothesis is that the association between current religiosity and behavior is conditioned by current level of integration into a moral community. Thus, measuring religiosity, moral community, binge drinking, and marijuana use all in the same wave of the NSYR is more parsimonious and allows us to examine these contemporaneous relationships that are more in line with theory. One limitation to conduct a cross-sectional analysis is that any negative association we observe between religiosity and behavior could potentially be due to “reverse causation,” where the behavior in question comes first and is followed by a reduction in religiosity. However, there are two reasons why we feel this is not happening here to any significant extent, and that any associations we see are largely due to an effect of religiosity on behavior. First, although some prior research has found bi-directional effects between religiosity and delinquency (e.g., Benda and Corwyn 1997), other research has failed to find a reciprocal effect of behavior on religiosity (e.g., Meier 2003). Second, in writing about their own analysis of religiosity and a variety of outcomes, using Wave 1 of the NSYR, Smith and Denton (2005:237) conclude, “We do not believe, however, that the available quantitative and qualitative evidence support the conclusion that reverse causation explains most or all of the variance in outcomes among different religious types [indicative of different levels of religiosity].” Finally, in addition to these two reasons, it is difficult to imagine a situation where reverse causation could explain away support for the moral community hypothesis. Basically, for this to happen in relation to the current analyses, individuals who are highly integrated into a moral community and who engage in binge drinking or marijuana use would have to reduce their religiosity more than their counterparts who are less integrated into a moral community and who engage in these behaviors. There is no theoretical reason to expect this would be happening, nor are we aware of any empirical evidence for this in prior research. Thus, although we obviously cannot make causal claims based on the current cross-sectional analysis, we feel it is more in line with current theoretical reasoning and provides a better approximation of the mechanisms in operation. Dependent Variables Table 1 contains descriptive statistics for all variables used in the analysis. The current analyses focus on two outcomes measured at Wave 3—marijuana use and binge drinking. In terms of marijuana use, subjects were asked, “How often, if ever, do you use marijuana?” with the response set ranging from “Never” to “Once a day or more.” Due to the highly skewed distribution of the responses (more than 80% of subjects responded “Never” or “A few times a year”), we created a dichotomous variable reflecting whether or not the subject uses marijuana (0 = “No,” 1 = “Yes”) for use in subsequent analyses. To measure binge drinking, subjects were asked, “How many times, if at all, over the past two weeks have you drunk at least 5 drinks [4 for females] in the same night?” with the response set ranging from “Never” to “Five or more times.” As with marijuana use, the distribution of the responses was highly skewed (more than 82% of subjects responded “Never” or “Once or twice”), and thus we created a dichotomous measure reflecting whether or not the youth has drunk at least five (or four) drinks in one night in the past 2 weeks (0 = “No,” 1 = “Yes”). There is also theoretical justification for coding the outcomes as dichotomies, as prior research suggests religiosity has a stronger association with the prevalence as opposed to the frequency of substance use (e.g., Ulmer et al. 2012). Table 1. Descriptive Statistics Variable Mean SD Min. Max. N Dependent variables  Marijuana use (1 = yes) 0.30 0 1 2,443  Binge drinking (1 = yes) 0.47 0 1 2,445 Independent variables  Religiositya 1.31 0.76 0 3.41 2,285  Moral communitya 1.15 0.67 0 3.25 2,409 Control variables  Gender (1 = female) 0.51 0 1 2,458  Age 20.02 1.44 18 23 2,458 Race/ethnicity  Black 0.16 0 1 2,443  White 0.69 0 1 2,443  Hispanic 0.10 0 1 2,443  Other 0.05 0 1 2,443 No. of friends who drink or use drugs 1.68 1.69 0 5 2,440 Variable Mean SD Min. Max. N Dependent variables  Marijuana use (1 = yes) 0.30 0 1 2,443  Binge drinking (1 = yes) 0.47 0 1 2,445 Independent variables  Religiositya 1.31 0.76 0 3.41 2,285  Moral communitya 1.15 0.67 0 3.25 2,409 Control variables  Gender (1 = female) 0.51 0 1 2,458  Age 20.02 1.44 18 23 2,458 Race/ethnicity  Black 0.16 0 1 2,443  White 0.69 0 1 2,443  Hispanic 0.10 0 1 2,443  Other 0.05 0 1 2,443 No. of friends who drink or use drugs 1.68 1.69 0 5 2,440 aAfter construction, a constant equal to the scale’s minimum value was added to each case so the new minimum value would equal zero. View Large Table 1. Descriptive Statistics Variable Mean SD Min. Max. N Dependent variables  Marijuana use (1 = yes) 0.30 0 1 2,443  Binge drinking (1 = yes) 0.47 0 1 2,445 Independent variables  Religiositya 1.31 0.76 0 3.41 2,285  Moral communitya 1.15 0.67 0 3.25 2,409 Control variables  Gender (1 = female) 0.51 0 1 2,458  Age 20.02 1.44 18 23 2,458 Race/ethnicity  Black 0.16 0 1 2,443  White 0.69 0 1 2,443  Hispanic 0.10 0 1 2,443  Other 0.05 0 1 2,443 No. of friends who drink or use drugs 1.68 1.69 0 5 2,440 Variable Mean SD Min. Max. N Dependent variables  Marijuana use (1 = yes) 0.30 0 1 2,443  Binge drinking (1 = yes) 0.47 0 1 2,445 Independent variables  Religiositya 1.31 0.76 0 3.41 2,285  Moral communitya 1.15 0.67 0 3.25 2,409 Control variables  Gender (1 = female) 0.51 0 1 2,458  Age 20.02 1.44 18 23 2,458 Race/ethnicity  Black 0.16 0 1 2,443  White 0.69 0 1 2,443  Hispanic 0.10 0 1 2,443  Other 0.05 0 1 2,443 No. of friends who drink or use drugs 1.68 1.69 0 5 2,440 aAfter construction, a constant equal to the scale’s minimum value was added to each case so the new minimum value would equal zero. View Large Thus, we will be examining the association between religiosity and the odds of engaging in marijuana use and binge drinking, and whether or not the extent of a subject’s integration into a moral community moderates these associations. Although the age range of the current sample includes both late-adolescence and young adulthood, it is reasonable to consider these two groups similar to each other in terms of the outcomes being examined. As Bachman et al. (1997) note, although there are changes in life circumstances such as employment, marriage, family, and legal status over time, and thus some degree of change in substance use between adolescence and young adulthood, there is also quite a bit of within-individual stability in the prevalence of substance use between these stages. Thus, despite the potential weakening of the influence of primary networks on religiosity and drug use post-adolescence (e.g., Hoffmann 2014), actual rates of substance use appear to remain relatively stable. Nonetheless, as mentioned below, age will be used as a control in all multivariate models. Independent Variables We operationalized religiosity with the combination of six items from Wave 3. The first item, measuring how often the subject attends religious services, had a response set ranging from “Never” to “Once a week or more.” A second item measured the subject’s frequency of attendance at organized religious groups such as Bible study, prayer group, or another religious group, and had a response set ranging from “Never” to “More than once a week.” The next two items, measuring how often the subject reads the sacred scriptures of his or her religious tradition when alone and how often the subject prays alone, each had a response set ranging from “Never” to “Once a day or more.” A fifth item, measuring how distant or close the subject feels to God most of the time, had a response set ranging from “Very distant” to “Very close.” The final item, measuring how important religious faith is in shaping how the subject lives his or her daily life, had a response set ranging from “Not important at all” to “Extremely important.” We standardized the six items so they would all be on the same metric, and then calculated the mean of the items for each subject to arrive at an overall measure of religiosity (α = .858). We operationalized moral community with the combination of four items from Wave 3. The items include how many of the subject’s friends are religious, how many friends have beliefs about religion that are similar to the subject’s, how many friends are in a religious group to which the subject belongs, and how many friends does the subject talk with about religious belief and experience. Subjects were asked to think about up to five of their closest friends before this series of questions, therefore the response set for each of these items ranges from “0” to “5.” As with the religiosity scale, we standardized the five items and then calculated the mean of the items for each subject to arrive at an overall measure of moral community (α = .684). This measure reflects an individual’s integration into what we are calling a micro moral community, and provides a new way of both conceptualizing and operationalizing the concept of moral community. It adheres to network analysts’ emphasis on primary social networks (Fisher 1982; Wellman 1996), and reflects Stark et al.’s (1982:15) comment that, “ . . . [W]e experience membership in a moral community as an aspect of our immediate social setting.” To facilitate the examination of whether or not the effect of religiosity is moderated by the level of an individual’s integration into a moral community, we created a second measure of moral community by recoding the original scale into a three category variable. The categories reflect whether the subject was in the lowest 25%, middle 50%, or upper 25% of the moral community distribution, and are thus indicative of each subject’s relative integration into a moral community. Table 2 contains the mean and standard deviation for each of the items that comprise the moral community scale across the three categories. Table 2. Descriptive Statistics for Individual Moral Community Items across Scores on the Moral Community Scale Item Lowest 25% on Moral Community Scale (n = 618) Middle 50% on Moral Community Scale (n = 1,186) Highest 25% on Moral Community Scale (n = 605) Mean (SD) Mean (SD) Mean (SD) Number of religious friends 0.82 (0.90) 1.89 (1.34) 4.13 (1.08) Number of friends with similar beliefs to subject 1.06 (0.91) 2.52 (1.30) 4.08 (0.95) Number of friends in religious group with subject 0.04 (0.19) 0.34 (0.66) 2.03 (1.74) Number of friends subject talks with about religion 0.58 (0.82) 2.00 (1.56) 3.39 (1.59) Item Lowest 25% on Moral Community Scale (n = 618) Middle 50% on Moral Community Scale (n = 1,186) Highest 25% on Moral Community Scale (n = 605) Mean (SD) Mean (SD) Mean (SD) Number of religious friends 0.82 (0.90) 1.89 (1.34) 4.13 (1.08) Number of friends with similar beliefs to subject 1.06 (0.91) 2.52 (1.30) 4.08 (0.95) Number of friends in religious group with subject 0.04 (0.19) 0.34 (0.66) 2.03 (1.74) Number of friends subject talks with about religion 0.58 (0.82) 2.00 (1.56) 3.39 (1.59) View Large Table 2. Descriptive Statistics for Individual Moral Community Items across Scores on the Moral Community Scale Item Lowest 25% on Moral Community Scale (n = 618) Middle 50% on Moral Community Scale (n = 1,186) Highest 25% on Moral Community Scale (n = 605) Mean (SD) Mean (SD) Mean (SD) Number of religious friends 0.82 (0.90) 1.89 (1.34) 4.13 (1.08) Number of friends with similar beliefs to subject 1.06 (0.91) 2.52 (1.30) 4.08 (0.95) Number of friends in religious group with subject 0.04 (0.19) 0.34 (0.66) 2.03 (1.74) Number of friends subject talks with about religion 0.58 (0.82) 2.00 (1.56) 3.39 (1.59) Item Lowest 25% on Moral Community Scale (n = 618) Middle 50% on Moral Community Scale (n = 1,186) Highest 25% on Moral Community Scale (n = 605) Mean (SD) Mean (SD) Mean (SD) Number of religious friends 0.82 (0.90) 1.89 (1.34) 4.13 (1.08) Number of friends with similar beliefs to subject 1.06 (0.91) 2.52 (1.30) 4.08 (0.95) Number of friends in religious group with subject 0.04 (0.19) 0.34 (0.66) 2.03 (1.74) Number of friends subject talks with about religion 0.58 (0.82) 2.00 (1.56) 3.39 (1.59) View Large Comparing the association between religiosity and binge drinking and marijuana use across subjects in each of the three categories will allow us to test the moral community hypothesis, and provide the first test of this hypothesis with a measure of micro moral community. The hypothesis would predict that the association will be strongest for those in the upper 25% of the moral community distribution, and weakest for those in the lowest 25%. Recognizing that there is conceptual overlap between many of the items included in the religiosity and moral community scales, and as a further check on our decisions of which items to include in which scale, we conducted exploratory factor analysis with all ten items that comprised both scales (detailed results available upon request). Only two factors were extracted, and the makeup of these factors matched our two scales. Specifically, all six religiosity items loaded on the same factor, with loadings ranging from .523 to .859, and all four moral community items loaded on the same factor, with loadings ranging from .568 to 882. As another check on our decisions, we also ran two separate exploratory factor analyses—one for only the six religiosity items and a second for only the four moral community items. In both analyses only one factor was extracted, and the loadings ranged from .613 to .840 on the single factor extracted in the analysis of the religiosity items, and from .645 to .770 on the single factor extracted in the analysis of the moral community items (detailed results available upon request). Several control variables were used in each of the multivariate models. Specifically, the demographic variables of gender (0 = Male; 1 = Female), age (at Wave 3), and race and ethnicity were controlled. Race and ethnicity were measured with a series of dichotomous variables for Black, White, Hispanic, and Other (the variable for White was entered into each model). The number of friends who use drugs or drink heavily (maximum = 5) was also controlled based upon the subjects’ self-report in Wave 3. Plan of Analysis The analysis that follows consists of two sets of multivariate logistic regressions predicting binge drinking and marijuana use, respectively, and all procedures were weighted with the appropriate sampling weight. In order to establish associations between each of the outcomes and religiosity and moral community, each set of regressions began with the estimation of a model including religiosity, moral community, and all of the controls for the full sample. Following this, three separate models were run for each outcome, including religiosity and all controls, for subjects in the lowest 25% on the moral community measure, those in the middle 50%, and those in the upper 25%. A z-score for comparing coefficients across equations (Paternoster et al. 1998) was used to test for statistically significant differences in the religiosity coefficients across the three models. Constructing the trichotomous moral community variable and comparing the impact of religiosity across the three groups, as opposed to using a multiplicative interaction term between religiosity and the continuous version of moral community, allows us to test for differences across discrete levels of moral community, such as high, medium, and low. This reflects the expectation that the difference between someone who is high and someone who is low on moral community is more meaningful than differences along a more continuous scale such as, for example, between someone who has a 2.2 and someone who has a 2.4 on the moral community scale, which a multiplicative interaction term would be testing for. As Paternoster et al. (1998:860) note, this is a common approach for which “ . . . there has been considerable consensus in the criminological literature with respect to the appropriateness of this coefficient-comparison strategy in examining what is essentially an interactive effect . . .” As stated earlier, it is expected that the strength of the association between religiosity and each of the outcomes will increase as integration into a moral community increases. Thus, the religiosity coefficient is expected to be strongest in the model for those in the upper 25% of moral community, and weakest for those in the lowest 25%. RESULTS Table 3 contains the results for the set of multivariate logistic regressions predicting binge drinking. The results for model 1 reveal a statistically significant association between binge drinking and religiosity, controlling for each of the factors discussed in the previous section. Specifically, since odds ratios of less than one indicate a decrease in the odds of the outcome occurring, higher religiosity is associated with lower odds of having engaged in binge drinking in the past 2 weeks. The moral community measure was not statistically significant in model 1. Table 3. Odds Ratios from Multivariate Logistic Regression Models Predicting Binge Drinkinga Variables Model 1: full sample Model 2: Lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.393*** 0.848 0.443*** 0.224*** Moral community 0.991 — — — Nb 2,229 558 1,075 596 Model chi-square 208.09*** (df = 6) 30.25*** (df = 5) 155.48*** (df = 5) 75.07*** (df = 5) Variables Model 1: full sample Model 2: Lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.393*** 0.848 0.443*** 0.224*** Moral community 0.991 — — — Nb 2,229 558 1,075 596 Model chi-square 208.09*** (df = 6) 30.25*** (df = 5) 155.48*** (df = 5) 75.07*** (df = 5) aAll models control for the following variables: gender, age, race/ethnicity, and number of friends who use drugs or drink heavily. Detailed results for all controls are available upon request. bThe sample size differs from 2,458 in the full sample model, and from 25% or 50% of the full sample in subsequent models due to listwise deletion of missing cases. *p < .05; **p < .01; ***p < .001. View Large Table 3. Odds Ratios from Multivariate Logistic Regression Models Predicting Binge Drinkinga Variables Model 1: full sample Model 2: Lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.393*** 0.848 0.443*** 0.224*** Moral community 0.991 — — — Nb 2,229 558 1,075 596 Model chi-square 208.09*** (df = 6) 30.25*** (df = 5) 155.48*** (df = 5) 75.07*** (df = 5) Variables Model 1: full sample Model 2: Lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.393*** 0.848 0.443*** 0.224*** Moral community 0.991 — — — Nb 2,229 558 1,075 596 Model chi-square 208.09*** (df = 6) 30.25*** (df = 5) 155.48*** (df = 5) 75.07*** (df = 5) aAll models control for the following variables: gender, age, race/ethnicity, and number of friends who use drugs or drink heavily. Detailed results for all controls are available upon request. bThe sample size differs from 2,458 in the full sample model, and from 25% or 50% of the full sample in subsequent models due to listwise deletion of missing cases. *p < .05; **p < .01; ***p < .001. View Large Models 2 through 4 in table 3 contain results for the subsamples in the lowest 25%, middle 50%, and upper 25% on moral community, respectively. The coefficient for religiosity is statistically significant for those in the upper 25% and middle 50% of moral community, with higher levels of religiosity associated with lower odds of binge drinking for both subsamples, and is not statistically significant for those in the lowest 25%. With an odds ratio approximately 50–75% smaller compared to the other two subsamples, and since odds ratios further from one indicate potentially stronger associations, the association appears to be strongest for the subsample in the upper 25% on moral community. The z-score used to test for differences in the religiosity coefficients across models (table 4) reveals that the coefficient for the upper 25% subsample differs significantly from the coefficient for both the middle 50% and lowest 25% subsamples. Thus, the association between religiosity and binge drinking is indeed strongest for the upper 25% subsample. The difference between the coefficients for the middle 50% and lowest 25% subsamples was also statistically significant, indicating a stronger association between religiosity and binge drinking for those in the middle 50% compared to the lowest 25%. Table 4. Z-scores Comparing Religiosity Coefficient across Subsamples Reflecting Different Levels of Moral Community; Binge Drinking Models Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 3.20** 5.09*** 2.48* Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 3.20** 5.09*** 2.48* *p < .05; **p < .01; ***p < .001. View Large Table 4. Z-scores Comparing Religiosity Coefficient across Subsamples Reflecting Different Levels of Moral Community; Binge Drinking Models Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 3.20** 5.09*** 2.48* Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 3.20** 5.09*** 2.48* *p < .05; **p < .01; ***p < .001. View Large Figure 1 graphically represents the different magnitudes of association between religiosity and binge drinking across the levels of moral community. Specifically, the bars represent the estimated percent reduction in odds of binge drinking associated with a one unit increase in religiosity for each moral community subsample. The estimated percent reduction in odds was calculated by subtracting 1 from the odds ratio for the religiosity variable in the relevant model (Long 1997). FIGURE 1. View largeDownload slide Estimated percent reduction in odds of binge drinking with each one-unit increase in religiosity, by moral community subsample. FIGURE 1. View largeDownload slide Estimated percent reduction in odds of binge drinking with each one-unit increase in religiosity, by moral community subsample. Table 5 contains the results for the set of multivariate logistic regressions predicting marijuana use. The results for model 1 reveal a statistically significant association between marijuana use and religiosity, and no significant association between marijuana use and moral community, controlling for each of the factors discussed in the previous section. Specifically, higher religiosity is associated with lower odds of using marijuana. Table 5. Odds Ratios from Multivariate Logistic Regression Models Predicting Marijuana Usea Variables Model 1: full sample Model 2: lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.383*** 0.808 0.380*** 0.231*** Moral Community 1.144 — — — Nb 2,226 556 1,075 595 Model Chi-Square 167.22*** (df = 6) 35.21*** (df = 5) 77.05*** (df = 5) 82.31*** (df = 5) Variables Model 1: full sample Model 2: lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.383*** 0.808 0.380*** 0.231*** Moral Community 1.144 — — — Nb 2,226 556 1,075 595 Model Chi-Square 167.22*** (df = 6) 35.21*** (df = 5) 77.05*** (df = 5) 82.31*** (df = 5) aAll models control for the following variables: gender, age, race/ethnicity, and number of friends who use drugs or drink heavily. Detailed results for all controls are available upon request. bThe sample size differs from 2,458 in the full sample model, and from 25% or 50% of the full sample in subsequent models due to listwise deletion of missing cases. *p < .05; **p < .01; ***p < .001. View Large Table 5. Odds Ratios from Multivariate Logistic Regression Models Predicting Marijuana Usea Variables Model 1: full sample Model 2: lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.383*** 0.808 0.380*** 0.231*** Moral Community 1.144 — — — Nb 2,226 556 1,075 595 Model Chi-Square 167.22*** (df = 6) 35.21*** (df = 5) 77.05*** (df = 5) 82.31*** (df = 5) Variables Model 1: full sample Model 2: lowest 25% on moral community measure Model 3: middle 50% on moral community measure Model 4: upper 25% on moral community measure Religiosity 0.383*** 0.808 0.380*** 0.231*** Moral Community 1.144 — — — Nb 2,226 556 1,075 595 Model Chi-Square 167.22*** (df = 6) 35.21*** (df = 5) 77.05*** (df = 5) 82.31*** (df = 5) aAll models control for the following variables: gender, age, race/ethnicity, and number of friends who use drugs or drink heavily. Detailed results for all controls are available upon request. bThe sample size differs from 2,458 in the full sample model, and from 25% or 50% of the full sample in subsequent models due to listwise deletion of missing cases. *p < .05; **p < .01; ***p < .001. View Large Models 2 through 4 in table 5 reveal that the religiosity coefficient is statistically significant for those in the upper 25% and middle 50% of moral community, with higher levels of religiosity associated with lower odds of marijuana use for both subsamples, and is not statistically significant for those in the lowest 25% of moral community. With the smallest odds ratio (approximately 40–70% smaller), the association appears to be strongest for those in the upper 25% on moral community compared to the middle 50% and lowest 25%. The z-scores (table 6) indicate that the coefficient for the upper 25% subsample does differ significantly from the coefficients for both the middle 50% and lowest 25% subsamples, indicating that the association between religiosity and marijuana is significantly stronger for the upper 25% compared to the other two subsamples. Further, the difference between the middle 50% and lowest 25% subsamples is also statistically significant, indicating the association between religiosity and marijuana use is stronger for the middle 50% versus the lowest 25% subsample. Table 6. Z-scores Comparing Religiosity Coefficient across Subsamples Reflecting Different Levels of Moral Community; Marijuana Use Models Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 2.26* 5.05*** 3.01** Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 2.26* 5.05*** 3.01** *p < .05; **p < .01; ***p < .001. View Large Table 6. Z-scores Comparing Religiosity Coefficient across Subsamples Reflecting Different Levels of Moral Community; Marijuana Use Models Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 2.26* 5.05*** 3.01** Variable Upper 25% vs. middle 50% on moral community measure Upper 25% vs. lowest 25% on moral community measure Middle 50% vs. lowest 25% on moral community measure Z-score 2.26* 5.05*** 3.01** *p < .05; **p < .01; ***p < .001. View Large Figure 2 graphically represents the different magnitudes of association between religiosity and marijuana across the levels of moral community. Specifically, the bars represent the estimated percent reduction in odds of marijuana use associated with a one unit increase in religiosity for each moral community subsample. FIGURE 2. View largeDownload slide Estimated percent reduction in odds of marijuana use with each one-unit increase in religiosity, by moral community subsample. FIGURE 2. View largeDownload slide Estimated percent reduction in odds of marijuana use with each one-unit increase in religiosity, by moral community subsample. It is important to note that we ran multicollinearity diagnostics for all of the models predicting binge drinking and marijuana use, and there are no problems with multicollinearity as all variance inflation factors were under 1.9. DISCUSSION AND CONCLUSIONS The moral community hypothesis predicts that adherence to religious norms against deviance will be stronger when religious individuals participate in a moral community, or a social setting in which religious norms are accepted and reinforced through interactions (Stark 1996; Stark et al. 1982). Conceptualizing moral communities as religious regions, counties, or institutions does not allow researchers to account for the integration of religious individuals into small groups or micro moral communities. Religious individuals residing in irreligious communities may participate in micro moral communities or those living in highly religious communities may fail to meaningfully interact. We posit that integration into a micro moral community increases the protective effect of religiosity on deviant behavior. Social routines within these small groups may be the primary mechanisms for transferring and reinforcing religious norms prohibiting deviant behavior. Relying on data from Wave 3 of the NSYR, which contains rich measures of personal networks and religiosity, our measure of micro moral community accounts for both the number of religious friends a person has within a limited, egocentric network and the nature of interactions between these individuals. This approach maintains that social integration into a community is relational and can be captured by measuring social ties within limited and extended networks. Religious individuals participating in micro moral communities routinely interact with a small number of co-religionists who discuss matters of faith. Consistent with previous research (e.g., Kelly et al. 2015; Michalak et al. 2007; Ulmer et al. 2012; Willis et al. 2003), we find that religiosity has a significant inverse relationship with the likelihood of engaging in both binge drinking and marijuana use. We also find that greater integration into a micro moral community strengthens the inverse association between religiosity and both binge drinking and marijuana use. Thus, we find support for the moral community hypothesis for both outcomes. By using egocentric measures to account for social integration into micro moral communities, this study provides a new test of the moral community hypothesis. Since micro moral communities are positioned within variably religious broader communities, future research can build on this test with a closer examination of the interaction between these two “types” of communities. Integration into a micro moral community may be directly related to the existence of a broader moral community. In highly religious institutions, counties, or regions, co-religionists have more opportunity to closely interact and are more likely to participate in a micro moral community. These small groups of co-religionists reinforce religious norms and enhance the protective effect of religiosity against deviant behavior. The prominence of micro moral communities within highly religious areas may explain the general effect of moral communities at various levels of aggregation. However, micro moral communities are not just limited to highly religious areas, as co-religionists are likely to interact even in relatively irreligious areas (e.g., Fischer 1982). Some religious individuals may even strengthen their faith when immersed in more religiously heterogeneous settings (e.g., Hammond and Hunter 1984; Olsen 2008; Smith and Emerson 1998). In relatively irreligious institutions, counties, or regions, interactions between religious individuals are limited by opportunity, but micro moral communities in these settings may provide a stronger boost to the protective effect of religiosity than those in more religious areas. Contrasting norms between small religious groups and the surrounding social milieu may intensify social processes that maintain and reinforce religious norms. The integration of small religious groups/small group participants into the broader social fabric merits further attention. The relationship between broader moral communities and micro moral communities is more complex than accounting for the absence or existence of religious adherence. Even basic denominational or theological differences complicate efforts to understand small group integration into the broader social milieu. For example, studies on the relationship between binge drinking and denominational affiliation find substantial variations across denominations (e.g. Michalak et al. 2007). Some broad moral communities may be dominated by denominations or theological orientations that are more accepting of binge drinking while others may be more diverse or generally unaccepting of such activities. Micro moral communities may also vary by denominational or theological make-up, which influences small group norms about drinking. Social networks comprised primarily of Catholics may adhere to different norms than those involving Evangelical Protestants, Mainline Protestants, Mormons, or Muslims. Yet, personal networks for all such faiths variably exist within regions, counties, or institutions. Conceptualizing moral communities at both the broad community level and the micro level requires a more thorough examination of how these two levels of measuring community interrelate to influence norms of deviant behavior. There are a few limitations to the current study which should be noted, and which could be addressed with future research. First, although chosen for the theoretical reasons addressed earlier, the cross-sectional nature of the analysis prevents us from drawing causal inferences about the impact of religiosity on deviant behavior, and the moderating effect of moral community. Second, based on available data, the current outcomes do not include measures of serious crime, and instead are limited to two measures of substance use. Although these are certainly important, it would be useful to extend future analyses to include more serious forms of crime including index crimes and hard drug use. Third, the data does not test the effect of micro moral communities beyond emerging adulthood, and, given that the influence of group socialization on both religious norms and substance abuse may weaken with age (e.g., Hoffman 2014), our findings may be limited to a narrow age range. In addition to addressing these limitations, future research can also pursue two additional issues. First, it would be useful to examine denomination-specific models of the interaction between religiosity and moral community. Second, mapping the development of individual religiosity and integration into a moral community over time using techniques such as growth-curve modeling or group-based trajectory modeling (e.g., Nagin 2005) would be particularly informative. Researchers can then examine how the identified trajectories of both concepts inter-relate, and how developmental patterns of religiosity and moral community integration interact, if at all, in protecting against involvement in deviant behavior. 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Published: Oct 1, 2018

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