The Pennsylvania State University. The Graduate School. College of the Liberal Arts UNDERSTANDING HOW CONGREGATIONS PROMOTE COMMUNITY

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1 The Pennsylvania State University The Graduate School College of the Liberal Arts UNDERSTANDING HOW CONGREGATIONS PROMOTE COMMUNITY INVOLVEMENT AND PROSOCIAL BEHAVIOR A Dissertation in Sociology by Jennifer M. McClure 2015 Jennifer M. McClure Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2015

2 The dissertation of Jennifer M. McClure was reviewed and approved* by the following: Roger Finke Distinguished Professor of Sociology and Religious Studies Dissertation Advisor Chair of Committee Diane Felmlee Professor of Sociology David Johnson Professor of Sociology, Demography, and Human Development and Family Studies Eric Plutzer Professor of Political Science and Sociology Melissa A. Hardy Distinguished Professor of Sociology and Demography Director, Graduate Program in Sociology *Signatures are on file in the Graduate School

3 iii ABSTRACT This dissertation concerns how religious congregations can promote community involvement and prosocial behavior among their attenders. Whereas many studies have sought to understand differences in community involvement and prosocial behavior between religious people and non-religious people, this dissertation focuses on attenders of religious congregations. This focus contributes to the literature on religion, community involvement, and prosocial behavior in a number of ways. First, it helps to explain why some attenders become involved in the community and engage in prosocial behavior, while other attenders do not. Additionally, it investigates how congregational context and activities relate with community involvement and prosocial behavior. This dissertation examines involvement in congregationally sponsored community service activities. Using a sample of attenders and congregations from the 2008/2009 U.S. Congregational Life Survey (USCLS), this dissertation seeks to understand how congregations can encourage their attenders to become involved in these community service activities and whether they can use these activities to encourage attenders to engage in private prosocial behavior, that is, in prosocial behavior that is not through organizations or activities. I begin this dissertation by introducing its main concepts and by providing an outline of its contributions to the literature (Chapter 1). Next, I discuss the 2008/2009 USCLS and the statistical methods used in this dissertation, including multilevel modeling and multiple imputation (Chapter 2). Following these introductory chapters, I present three empirical chapters. Since congregationally sponsored community service activities are rarely examined in other studies, in the first empirical chapter (Chapter 3), I explore involvement in these activities and describe how involvement in these activities varies among religious traditions. Results from this chapter indicate that Mainline Protestants and Black Protestants are the most likely to be involved in these activities. After exploring involvement in congregationally sponsored community service activities, I turn to understanding why some attenders are involved in these activities while other attenders are not (Chapter 4). The findings suggest that congregational friendships, values/emphases, and context matter for understanding whether attenders are involved in these activities. In the last empirical chapter (Chapter 5), I consider how involvement in congregationally sponsored community service activities relates with private prosocial behavior. Attenders who are involved in these activities are more likely to provide social support, to give charitably, and to be civically engaged. Finally, a concluding chapter (Chapter 6) discusses how congregations can promote community involvement through these activities and use these activities to stimulate private prosocial behavior among attenders.

4 iv TABLE OF CONTENTS List of Figures... vi List of Tables... vii Acknowledgements... viii Chapter 1 - Introduction... 1 Outline of the Dissertation... 5 Chapter 2 - Data and Methods U.S. Congregational Life Survey Multilevel Modeling Missing Data and Multiple Imputation Introducing the Attenders and Congregations Chapter 3 - Religious Tradition and Involvement in Congregational Activities That Focus on the Community Introduction Literature Review Mainline Protestantism Evangelical Protestantism Black Protestantism Roman Catholicism Hypotheses Data and Methods Data and Sample Variables Analytical Strategy Results Discussion Conclusion Chapter 4 - Congregations and the Community: Examining Whether Attenders Participate in Congregationally Sponsored Community Service Activities Introduction Literature Review Social Networks Values... 66

5 v Theology Cross-level Interactions Data and Methods Data and Sample Variables Analytical Strategy Results Discussion Conclusion Chapter 5 - Promoting Prosocial Behavior: Examining the Roles of Religious Beliefs and Values, Religious Social Networks, and Involvement in Community Organizations and Activities Introduction Literature Review Religious Beliefs and Values Religious Social Networks Community Organizations and Activities Data and Methods Data and Sample Variables Analytical Strategy Results Discussion and Conclusion Chapter 6 - Conclusion Limitations Future Directions Bibliography

6 vi LIST OF FIGURES Figure 1-1. Conceptual Diagram for Chapter Figure 1-2. Conceptual Diagram for Chapter Figure 1-3. Conceptual Diagram for Chapter Figure 3-1. Involvement in Congregationally Sponsored Activities that Focus on the Community, by Religious Tradition Figure 3-2. Predicted Probabilities for Involvement in Different Combinations of Congregationally Sponsored Activities that Focus on the Community Figure 4-1. Cross-level Interaction between the Proportion of Attenders in Each Congregation Who Value Their Congregation s Community Emphasis and Whether Attenders Value Their Congregation s Community Emphasis Figure 4-2. Cross-level Interaction between Congregational Theological Liberalism and Attender Social Embeddedness Figure 4-3. Cross-level Interaction between Congregational Theological Liberalism and Whether Attenders Value Their Congregation s Community Emphasis... 95

7 vii LIST OF TABLES Table 2-1. Examining the Sample Table 2-2. Examining the Analytical Sample Table 2-3. Descriptive Statistics for the Attenders and Congregations Table 3-1. Descriptive Statistics for Religious Tradition and Involvement in Congregationally Sponsored Activities that Focus on the Community Table 3-2. Multilevel Logistic Regressions Predicting Involvement in Two Congregationally Sponsored Activities that Focus on the Community Table 3-3. Multilevel Multinomial Regression Predicting Involvement in Different Combinations of Congregationally Sponsored Activities that Focus on the Community Table 4-1. Descriptive Statistics for Social Embeddedness, Values, Theology, and Involvement in Congregationally Sponsored Community Service Activities Table 4-2. Correlations among the Independent Variables Table 4-3. Multilevel Logistic Regressions Predicting Involvement in Congregationally Sponsored Community Service Activities Table 5-1. Descriptive Statistics for Private Prosocial Behavior, Beliefs and Values, Social Networks, and Involvement Community Organizations and Activities Table 5-2. Correlations among the Independent Variables Table 5-3. Multilevel Logistic Regressions Predicting the Provision of Social Support Table 5-4. Multilevel Logistic Regressions Predicting Charitable Giving and Civic Engagement Table 5-5. Summarizing the Results

8 viii ACKNOWLEDGEMENTS It is very likely that I would not be where I am today if I had not met my undergraduate mentor, David Caddell. He inspired me with his enthusiasm for sociology, his ability to mentor students, and his integration of faith and the intellectual life. When I told him that I wanted to study sociology of religion in graduate school, he encouraged me to go to Penn State and to meet Roger Finke. David has encouraged and supported me throughout my undergraduate and graduate studies, and he has motivated me to be a professor in Christian higher education. Roger Finke, my advisor at Penn state, has been a wonderful mentor and friend. His advice on scholarship, writing, and teaching has been invaluable, and working with him at the Association of Religion Data Archives ( has been a delight. I have also had the opportunity to work with Roger at a local community garden. Getting to know Roger has helped me to see how committed he is not only to his work but also to his family, faith, and community. I could not have asked for a better dissertation committee. My committee members, David Johnson, Diane Felmlee, and Eric Plutzer, have been very helpful, engaged, and responsive throughout this project. Barry Lee has also been a very supportive mentor, even though I have not taken a class with him or asked him to serve on my committee. I have enjoyed working with a number of graduate students in the ARDA office, including Jaime Harris, Steve Merino, the late Ben McKune, Rob Martin, Nathaniel Porter, Ben Gurrentz, Christine Bucior, Jose Luis Debora, Andrea Liza Ruiz, and Dane Mataic. Our office has always been a good place to talk about religion and to encourage and support each other. I have also been able to work with a number of other people through the ARDA. Gail Ulmer has been a wonderful supervisor and friend, and I have also enjoyed working with Chris Bader, Neil Luft, David Briggs, Kevin Dougherty, Ruth Christiansen, Dale Jones, Laura Lance, Joseph Baker, Chris Scheitle, Andrew Whitehead, and Jennifer McKinney. I could not have made it through graduate school without support from a number of other friends. Many thanks to Erica Dollhopf, Cat Tucker, Alanna Kmicikewycz, Steve and Sonya Woods, Terri Finke, Matt and Jill Finke, Father John and Matushka Linda Reeves, Deacon Alex Cadman, Deacon David and Matushka Brenda Smith, Joseph Nakpil, Phil Graybill, and all of the wonderful people at Holy Trinity Orthodox Church. Most of all, I would like to thank my parents, Don and Pam McClure, and my sister and brother-in-law, Megan and Patrick Kamler. I have cherished their love, support, and encouragement throughout this process.

9 ix Jesus answered and said, So which of these three do you think was neighbor to him who fell among the thieves? And the lawyer said, He who showed mercy on him. Then Jesus said to him, Go and do likewise. Luke 10:36-37

10 x My parents, Don and Pam McClure, have demonstrated how religion and congregational participation can promote community involvement and prosocial behavior throughout my life. This dissertation is dedicated to them.

11 1 Chapter 1 Introduction Research on community involvement and prosocial behavior underscores the importance of religion. Religious people are more likely than non-religious people to help others, to volunteer, and to be involved in the community (Wilson & Musick 1997; Putnam 2000; Beyerlein and Hipp 2006; Driskell, Lyon, and Embry 2008; Loveland, Jones-Stater, and Park 2008; Putnam and Campbell 2010:451). Scholars argue that religion has such a strong influence on community involvement and prosocial behavior, acts that are defined [ ] as generally beneficial to other people (Penner et al. 2005:366), because it embeds people in strong, moral social networks and instills prosocial values (Wuthnow 1991; Wilson 2000; Beyerlein and Hipp 2006; Putnam and Campbell 2010; Einolf 2011; Merino 2013). Religion is indispensable for understanding why people are involved in the community and engage in prosocial behavior. Despite a near consensus on the differences between the religious and the nonreligious in community involvement and prosocial behavior (Wilson & Janoski 1995; Putnam 2000; Beyerlein and Hipp 2006; Driskell et al. 2008; Putnam and Campbell 2010), there have been few studies that examine why some religious people become involved in the community and engage in prosocial behavior while other religious people do not. Past studies that investigate such differences have found that involvement in church activities, religious identity, and religious social networks are associated with community involvement and volunteering (Park and Smith 2000; Schwadel 2005, 2012).

12 2 What these studies fail to do, however, is look more closely at the activities of congregations and their members. Because it focuses on explaining differences between religious and non-religious people, the majority of research fails to examine how congregational context shapes attenders community involvement and prosocial behavior. Most studies that seek to understand why some attenders volunteer in the community while other attenders do not focus on individual-level characteristics, like congregational involvement, religious tradition, private devotional activities, and involvement in religious social networks (Park and Smith 2000; Lam 2002; Beyerlein and Hipp 2006; Driskell et al. 2008; Loveland et al. 2008; Putnam and Campbell 2010; Merino 2013). Evidence indicates, however, that congregational context also matters for predicting community involvement as congregational theology and social networks shape attenders involvement in community organizations (Schwadel 2005). Neglecting to examine how congregational context influences community involvement and prosocial behavior is an unfortunate outcome of the focus on the differences between religious and non-religious people in the literature. The focus on differentiating between the religious and non-religious also neglects the examination of a key explanation for how religious social capital influences community involvement involvement in congregational activities. Through these activities, attenders can learn civic skills, like planning, organizing meetings, and handling conflict, which they can use in voluntary organizations in the community (Verba, Schlozman, and Brady 1995; Ammerman 1997; Park and Smith 2000; Putnam 2000; Beyerlein and Hipp 2006; Driskell et al. 2008; Loveland et al. 2008). Most studies that examine participation in congregational activities use general measures that ask

13 3 about involvement in church activities (e.g., Park and Smith 2000:277). When analyzing these questions, scholars do not know if attenders participated in social service activities, a choir or worship team, the children s or youth ministry, or a congregational committee. Having more specific questions is important for determining whether some types of congregational activities are more likely to encourage community involvement than others. The lack of studies that distinguish between different types of congregational activities weakens the sociological understanding of how participation in congregational activities relates to community involvement and prosocial behavior. Although past research focuses on explaining differences in community involvement and prosocial behavior between the religious and non-religious, this dissertation argues that it is also important to understand why some attenders of religious congregations are involved in the community and in helping others while other attenders are not. This focus on attenders also allows this dissertation to incorporate congregational context and congregational activities into the literature on community involvement and prosocial behavior. This dissertation emphasizes the role of congregationally sponsored community service activities, through which congregations provide opportunities for attenders to serve in the broader community. These activities are prosocial, and they include, but are not limited to, serving meals at a soup kitchen or homeless shelter, providing cash assistance for impoverished families, offering after-school programs for neighborhood children, and advocating for a living wage for low-income workers (Cnaan 2002). These activities are important to investigate for three reasons. First, attenders are more likely to volunteer through these congregationally sponsored activities than they do through

14 4 organizations that are not related to their congregation (Park and Smith 2000:279). Second, through these activities, congregations can equip attenders to serve in the community (Park and Smith 2000:284). Third, congregations can use these activities to direct attenders prosocial energies toward specific needs, causes, and activities (Wuthnow 1991:127; Ammerman 1997:366). In this dissertation, I conduct three empirical studies. Since congregationally sponsored community service activities are rarely examined in the literature on religion and community involvement (see Schwadel 2012 for a notable exception), the first study (Chapter 3) explores involvement in these activities and describes how involvement varies among different religious traditions. The second study (Chapter 4) seeks to explain why some attenders are involved in these activities while other attenders are not, using religious social networks, values, theology, and congregational context as predictors. In the third study (Chapter 5), I investigate whether attenders who are involved in congregationally sponsored community service activities are more likely to engage in private prosocial behavior, that is, in prosocial behavior that is not through organizations or activities. Through these studies, I seek to understand how congregations can promote community involvement and prosocial behavior through these specific activities and how involvement in congregationally sponsored community service activities influences attenders private behavior. Outlines of each of the subsequent chapters are presented below.

15 5 Outline of the Dissertation In Chapter 2, I discuss the data and statistical methods that are used in this dissertation. This dissertation utilizes data from the 2008/2009 U.S. Congregational Life Survey (USCLS). This survey was conducted by the Presbyterian Church (U.S.A.) Research Services and funded by the Lilly Endowment and the Louisville Institute. The USCLS contains data on congregations, their leaders, and their attenders; this dissertation, however, only uses data from the congregational and attender levels. Data were collected through self-completion questionnaires. The sample of attenders includes adults who have attended their congregation for a year or more. The hypotheses presented in this dissertation are examined using multilevel models, and the results are estimated using HLM 6.0. I also use multiple imputation to address missing data. In beginning to examine congregationally sponsored community service activities, Chapter 3 explores attender involvement in these activities. While these activities are the main focus of this dissertation, I also examine involvement in congregationally sponsored evangelism activities in this chapter in order to provide a more holistic description of how attenders of religious congregations engage in the broader community. Because religious groups have differing social and theological emphases that influence whether attenders are involved in the community, I describe how involvement in these activities varies among different religious traditions (see Figure 1-1). Describing these differences is an important first step to understanding involvement in these activities.

16 6 While Chapter 3 focuses on exploring and describing involvement in congregationally sponsored community service activities, Chapter 4 seeks to explain why some attenders are involved in these activities while other attenders are not. Scholars have suggested that religious social networks, values, and theology are important mechanisms through which religion influences community involvement. In Chapter 4, I use these three mechanisms to predict whether attenders are involved in congregationally sponsored community service activities (see Figure 1-2). The measures that I use for religious social networks and values have a strong congregational emphasis that measures in many other studies lack, and I also aggregate attender data on social networks, values, and theology to the congregational level in order to examine individual attender vs. congregational context effects. In this chapter, I investigate how congregations can encourage attenders to serve in the community through the activities that they offer. Chapter 5 concerns how involvement in congregationally sponsored community service activities and involvement in community organizations can encourage attenders to engage in private prosocial behavior, that is, in prosocial behavior that is not through organizations or activities. The analyses also account for two other common explanations for why religion relates with prosocial behavior: (1) religious beliefs and values; (2) religious social networks (see Figure 1-3). This chapter contributes to the literature on religion and prosocial behavior in three ways. First, it analyzes why some attenders engage in private prosocial behavior while other attenders do not. Second, it emphasizes the role of community organizations and activities more strongly than other studies on religion and prosocial behavior. Third, it utilizes a unique predictor that concerns how

17 7 congregations can promote private prosocial behavior involvement in congregationally sponsored community service activities. In Chapter 6, I conclude this dissertation by integrating the findings concerning involvement in congregationally sponsored community service activities from the three empirical chapters. First, I describe how involvement in these activities varies among different religious traditions. Second, I consider how congregations can promote involvement in these activities through their social networks, values, and theology. Third, I examine how congregations may be able to encourage attenders to engage in private prosocial behavior through involvement in these activities. After reviewing these findings, I discuss the three ways in which these findings contribute to the literature on religion, community involvement, and prosocial behavior. They provide an explanation for why some attenders participate in the community and engage in prosocial behavior while others do not. Additionally, these findings address how congregational context shapes whether attenders are involved in the community and how involvement in specific types of congregational activities relates with whether attenders engage in private prosocial behavior. Through these contributions, this dissertation helps scholars to understand how congregations can promote community involvement and prosocial behavior among their attenders. In concluding this chapter, I also address the limitations of this dissertation and future directions for research.

18 Figure 1-1. Conceptual Diagram for Chapter 3 8

19 Figure 1-2. Conceptual Diagram for Chapter 4 9

20 Figure 1-3. Conceptual Diagram for Chapter 5 10

21 11 Chapter 2 Data and Methods This dissertation uses data from the U.S. Congregational Life Survey (USCLS) to test its hypotheses (Research Services, Presbyterian Church (U.S.A.) 2008/2009a). Because attenders are clustered within congregations in the data, it utilizes multilevel models as this clustering biases results from standard statistical models. This dissertation employs multiple imputation to address missing data. This chapter also briefly introduces the attenders and congregations examined in this dissertation. U.S. Congregational Life Survey This dissertation uses the 2008/2009 data from the U.S. Congregational Life Survey (USCLS), the largest survey of American worshipers ever conducted (Woolever and Bruce 2010:121). This survey includes data on congregations, their leaders, and their attenders, and this dissertation utilizes its data on congregations and attenders. The USCLS has unique strengths which make it ideal to use for this dissertation. Because of its focus on congregations, it does not contain data on nonattenders, and it allows one to examine differences between attenders. The USCLS was also designed so that attenders are clustered within congregations, and this design allows researchers to examine how congregational context influences attenders. Finally, this survey contains many measures of congregational involvement, including measures of

22 12 involvement in congregationally sponsored community service activities. Such specific measures are usually not included in general surveys of American adults. The USCLS was conducted by the Presbyterian Church (U.S.A.) Research Services in 2001 and in 2008/2009, and both waves of data are archived through the Association of Religion Data Archives ( The Lilly Endowment and the Louisville Institute provided funding for the USCLS. The 2008/2009 data were collected in the fall of 2008 and the spring of 2009, and they include 64,674 cases of attenders and 251 cases of congregations (Woolever and Bruce 2010). Harris Interactive conducted the sampling for the 2008/2009 USCLS through a hypernetwork strategy, using congregations as the primary sampling unit. Individuals in the 2008 General Social Survey who reported that they attended worship at least once in the prior year were asked to name the place where they worshiped (Woolever and Bruce 2010:121). Since these individuals are a random sample, the congregations they named are also a random sample of congregations. The existence of these congregations was verified, and then they were invited to participate in the 2008/2009 USCLS (Woolever and Bruce 2010:121). The sampling frame for the 2008/2009 USCLS includes both the congregations that participated in the 2001 USCLS and the congregations recruited by Harris Interactive, and it is generalizable to all U.S. congregations. About a quarter (26.3%) of the congregations which participated in the 2001 USCLS participated in the 2008/2009 USCLS, and eleven percent of the new congregations sampled by Harris Interactive also participated (Woolever and Bruce 2010:122). Data were collected through self-administered questionnaires. Data on congregations were collected through congregational profiles that asked about

23 13 congregational facilities, staff, programs and worship services (Woolever and Bruce 2010:121). A clergy member or lay leader completed this congregational profile. All attenders of age 15 and older were also invited to complete questionnaires during services over a weekend of each congregation s choice. Because congregations only collected data over one weekend, regular attenders were more likely to participate in the study than less frequent attenders (Woolever and Bruce 2010). The sample of attenders utilized in this dissertation was developed starting with those cases that had both attender and congregational data. This criterion was important because some congregations submitted congregational data but did not survey their attenders, while other congregations surveyed their attenders but did not submit congregational data. There were 63,371 attenders from 250 congregations that met this criterion. Two selection filters were used to focus the sample. The sample excludes attenders who were less than 18 years old and those who attended their congregation for less than a year. These selection filters were used to restrict the sample to adult attenders and, since respondents were asked to indicate whether they engaged in a variety of prosocial behaviors in the past year, to increase the likelihood that the congregation that influenced the attender was the congregation that he or she was currently attending. Due to the selection filters, the sample was reduced further to 53,473 attenders from 250 congregations. The selection filters introduced a number of biases into the sample, and Table 2-1 examines these biases. The sample became, on average, older. A number of proportions also increased, including the proportion of attenders who were involved in congregationally sponsored community service activities, the proportion of attenders

24 14 involved in other congregational activities, the proportion of attenders involved in congregational small groups, and the proportion of attenders who gave charitably. Compared to nationally representative surveys, the sample is highly religiously active. In the 2008 General Social Survey (National Opinion Research Center 2008), about a quarter of respondents attended religious services once a week or more, while 80% of the attenders in the sample attended services that frequently. This sample is beneficial because it allows scholars to begin to understand why some attenders participate in congregationally sponsored community service activities and engage in private prosocial behavior while other attenders do not. The overall high level of some religiosity measures, like attendance, however, is a weakness and results in lower variation in these variables. Analyses of the USCLS must adjust for a number of survey design characteristics. The first characteristic concerns weighting. The U.S. Congregational Life Survey contains two sets of congregational-level weights. The first set can be used for estimating the percentage of congregations with a certain characteristic, and the second set can be used to estimate the percentage of attenders who belong to a congregation with a certain characteristic. The attributes used to create the weights include congregation size, region of the United States, and denominational family. Both sets of weights can only be applied to congregation-level data; there are no attender-level weights (Research Services, Presbyterian Church (U.S.A.) 2001). The second survey design characteristic pertains to clustering. Since congregations are the primary sampling unit and attenders are clustered within congregations, results need to adjust for clustering as it can bias standard errors and hypothesis testing (Groves et al. 2009: ). The analyses presented in this

25 15 dissertation adjust for these survey design characteristics. Congregational descriptive statistics are weighted using the first set of weights, and attender descriptive statistics adjust for clustering. Multilevel models are used to adjust for clustering within the data, and the congregational-level data in these models are also weighted using the first set of weights. Multilevel Modeling Because attenders are nested within congregations in the USCLS, there is likely to be dependence within the data. In other words, attenders of a congregation are likely to have characteristics that are the same as or similar to the characteristics of fellow attenders of the same congregation; similarly, attenders of a congregation are likely to have residuals that are similar to those of their fellow attenders. Normal statistical models assume that cases are sampled independently and that errors are uncorrelated (Allison 1999: ; Fox 2008:101), and the dependence within clustered data breaks these assumptions. This dependence among cases can bias statistical estimation, especially regarding standard errors and hypothesis testing (Raudenbush and Bryk 2002; Groves et al. 2009). When analyzing clustered data, it is important to use multilevel models that account for the dependence within the data. This dissertation uses two types of multilevel models. In the first type, both individual-level and contextual, or group-level, variables predict an individual-level outcome. The basic equation for this model is presented below. The format that I use to

26 16 write the equation splits the equation into an individual-level component and two contextual-level components. Y ij = β 0j + β 1j X ij + r ij β 0j = γ 00 + γ 01 W j + u 0j β 1j = γ 10 On the individual level, Y ij is the outcome, β 0j is the constant or intercept that varies among groups, X ij is an individual-level predictor, β 1j is the regression coefficient for X ij, and r ij is the error term that accounts for the individual differences in the outcome within a group. On the group level, γ 00 is the average constant across the groups, W j is a contextual-level predictor, γ 01 is the regression coefficient for W j, u 0j is the error term that accounts for differences in the outcome between groups, and γ 10 is the average regression coefficient for X ij across the groups. By splitting the error into these two terms (u j and r ij ), multilevel analyses can adjust for the dependence within the data (Raudenbush and Bryk 2002). The second type of model includes an error term that allows regression coefficients to vary among groups. This feature allows scholars to examine cross-level interactions, or whether an individual-level predictor has a regression coefficient that varies based on a group characteristic (Raudenbush and Bryk 2002). The basic equation for a cross-level interaction is presented below. Y ij = β 0j + β 1j X ij + r ij β 0j = γ 00 + γ 01 W j + u 0j β 1j = γ 10 + γ 11 W j + u 1j

27 17 The additional notation includes γ 11 as the regression coefficient for the interaction between W j and X ij and u 1j as the error term that allows β 1j to vary across groups. In this model, γ 01 becomes the average regression coefficient for X ij across the groups when W j equals zero. In examining the hypotheses in each chapter, I use multilevel models to adjust for the dependence within the data, and I use HLM 6.0 to estimate these models. In the analyses presented, all of the variables are grand-mean centered. For each variable, this centering method subtracts the mean for the entire sample from each attender s value. 1 Grand-mean centering allows scholars to examine individual and contextual relationships, and it also helps to make the constant in regression models more meaningful. With grand-mean centered data, the constant is the expected outcome when all predictors are set to their means (Raudenbush and Bryk 2002). This dissertation reports unit-specific results, which can be interpreted as the expected change in the outcomes associated with a one-unit increase in the relevant predictor, holding constant other predictors and all random effects [or varying regression coefficients] in the model (Raudenbush and Bryk 2002:334). Missing Data and Multiple Imputation The 2008/2009 USCLS also contains missing data, or instances where respondents did not answer certain survey questions. Many statistical softwares use 1 This method is different from group-mean centering, which centers each individual s response on the mean within his or her group. While group-mean centering allows scholars to examine effects within and between social contexts, grand-mean centering is needed for this study since it allows scholars to examine individual and contextual effects (Raudenbush and Bryk 2002).

28 18 casewise deletion, where cases that have missing data on a predictor and/or outcome are excluded from the analysis (Allison 2002), as a default method for dealing with missing data. Some scholars, however, argue that more modern methods, like multiple imputation, yield results that are at least as good as the results that are produced when using casewise deletion (Johnson and Young 2011:942). Multiple imputation uses regression-based models to estimate potential values for cases with missing data, and a critical part of its effectiveness is its ability to model uncertainty in these estimates. Multiple imputation models uncertainty in three different ways. First, it adds a randomly generated residual to each regression coefficient that is used to produce the estimate. Second, it adds a randomly generated residual to each estimate. Third, it produces multiple datasets with these estimates, and the variation of estimates among the datasets allows scholars to model the uncertainty of the estimated values (Allison 2002:28-29). There are two methods for performing multiple imputation. The first method relies on the multivariate normal model and assumes that all variables are continuous and have normal distributions. The second method uses chained equations and predicts missing data for each variable based on its distribution. So, rather than assuming that each variable has a normal distribution, chained equations can predict values for binary, ordinal, categorical, and continuous variables (Johnson and Young 2011: ). This dissertation uses multiple imputation to address missing data. 2 In the 2008/2009 USLCS, many variables having missing data, and there is a substantial amount of missing data for attender income and theology and for congregation size (see Table 2-1). HLM 6.0 can analyze multiply imputed data as long as imputed datasets are 2 Correlational, supplemental, and sensitivity analyses use casewise deletion.

29 19 generated previously in another statistical program, like Stata. For multiple imputation in two-level analyses, HLM uses one group-level dataset and multiple imputed individuallevel datasets (Raudenbush et al. 2004:179). HLM 6.0 also requires that there be full group-level data, so the analytical sample was limited to attenders whose congregations had complete data (van Buuren 2011:173). Using Stata 13.1, I imputed ten datasets of data, the maximum number of datasets that HLM 6.0 can analyze, using chained equations. As Table 2-2 indicates, the requirement to have complete group-level data biases some of the data concerning congregational religious tradition. Before restricting congregational data to only those cases with complete data, 13% of congregations were Catholic, 26% were Evangelical Protestant, 48% were Mainline Protestant, five percent were Black Protestant, and eight percent were from other traditions. After applying this restriction, however, these percentages changed. Now, nine percent of congregations are Catholic, 26% are Evangelical Protestant, 50% are Mainline Protestant, six percent are Black Protestant, and eight percent are from other traditions. With this restriction, the percentages of congregations that are Mainline Protestant and Black Protestant have increased, and the percentage of congregations that are Catholic has decreased. Multiple imputation is beneficial, though, because it allows many cases to be retained in the analysis. Through using multiple imputation, this dissertation can analyze data from 46,514 attenders and 227 congregations; if this dissertation were to use casewise deletion, it would only be able to analyze data from 31,153 attenders and 227 congregations.

30 20 Introducing the Attenders and Congregations Before proceeding to the subsequent chapters, I present basic descriptive statistics for the attenders and congregations that are examined in this dissertation in Table 2-3. Beginning with the attenders, the average attender is in his or her mid-50s, and 61% of the attenders are female. The average attender has gone to trade school or earned an Associate s degree, and he or she has an annual household income between $50,000 and $74,999 before taxes. In terms of race, three percent of the attenders are Asian, six percent are African American, eight percent are Hispanic, 79% are non-hispanic white, and three percent are from other races. The average attender also attends worship services about once a week. Turning to congregations, 50% of the congregations are Mainline Protestant, 26% are Evangelical Protestant, six percent are Black Protestant, nine percent are Catholic, and eight percent are from other traditions. The average congregation has an average weekly attendance of about 200 and offers four to five different social service activities. Other key variables are discussed in the subsequent empirical chapters.

31 21 Table 2-1. Examining the Sample Cases with both attender-level and congregation-level data Cases that have attended a congregation for one or more years and that are adults Attender-level Variables N Missing Mean N Missing Mean Age 60, % , % Female 58, % , % 0.61 Education 60, % , % 3.21 Income 54, % , % 4.18 Asian 60, % , % 0.03 African American 60, % , % 0.05 Hispanic 60, % , % 0.08 White, non-hispanic 60, % , % 0.80 Other Race 60, % , % 0.03 Attendance 62, % , % 2.86 Involvement in Congregation Sponsored Evangelism Activities 63, % , % 0.18 Involvement in Congregation Sponsored Community Service Activities 63, % , % 0.20 Social Embeddedness 61, % , % 2.70 Valuing Congregation's Community Emphasis 63, % , % 0.21 Theological Liberalism 49, % , % Involvement in Congregational Activities 63, % , % 0.44 Involvement in Congregational Small Groups 63, % , % 0.50 Giving a Loan 63, % , % 0.26 Caring for the Sick 63, % , % 0.21 Helping Someone Find a Job 63, % , % 0.22 Charitable Giving 63, % , % 0.73 Working to Solve Community Problems 63, % , % 0.19 Contacting Elected Officials 63, % , % 0.18 Involvement in Social Service Organizations 63, % , % 0.31 Involvement in Advocacy Organizations 63, % , % 0.06 Total 63,371 53,473

32 22 Cases with both attender-level and congregation-level data Cases that have attended a congregation for one or more years and that are adults Congregation-level Variables N Missing Mean N Missing Mean Catholic % % 0.13 Evangelical Protestant % % 0.26 Mainline Protestant % % 0.48 Black Protestant % % 0.05 Other Traditions % % 0.08 Average Social Embeddedness % % 2.89 Proportion Valuing Congregation's Community Emphasis % % 0.18 Average Theological Liberalism % % Congregation Size (LN) % % 4.73 Number of Social Service Activities % % 4.70 Weight % % 1.00 Total Source: U.S. Congregational Life Survey, 2008/2009 Note: All means for congregational variables are weighted, except for the mean for the weight variable.

33 23 Table 2-2. Examining the Analytical Sample Cases that have attended a congregation for one or more years and that are adults Cases with Full Congregational Data Attender-level Variables N Missing Mean N Missing Mean Age 51, % , % Female 49, % , % 0.61 Education 51, % , % 3.22 Income 47, % , % 4.16 Asian 51, % , % 0.03 African American 51, % , % 0.06 Hispanic 51, % , % 0.08 White, non-hispanic 51, % , % 0.79 Other Race 51, % , % 0.03 Attendance 53, % , % 2.86 Involvement in Congregation Sponsored Evangelism Activities 53, % , % 0.18 Involvement in Congregation Sponsored Community Service Activities 53, % , % 0.20 Social Embeddedness 52, % , % 2.70 Valuing Congregation's Community Emphasis 53, % , % 0.21 Theological Liberalism 42, % , % Involvement in Congregational Activities 53, % , % 0.44 Involvement in Congregational Small Groups 53, % , % 0.50 Giving a Loan 53, % , % 0.26 Caring for the Sick 53, % , % 0.21 Helping Someone Find a Job 53, % , % 0.22 Charitable Giving 53, % , % 0.72 Working to Solve Community Problems 53, % , % 0.20 Contacting Elected Officials 53, % , % 0.18 Involvement in Social Service Organizations 53, % , % 0.31 Involvement in Advocacy Organizations 53, % , % 0.06 Total 53,473 46,514

34 24 Cases that have attended a congregation for one or more years and that are adults Cases with Full Congregational Data Congregation-level Variables N Missing Mean N Missing Mean Catholic % % 0.09 Evangelical Protestant % % 0.26 Mainline Protestant % % 0.50 Black Protestant % % 0.06 Other Traditions % % 0.08 Average Social Embeddedness % % 2.89 Proportion Valuing Congregation's Community Emphasis % % 0.18 Average Theological Liberalism % % Congregation Size (LN) % % 4.73 Number of Social Service Activities % % 4.67 Weight % % 1.00 Total Source: U.S. Congregational Life Survey, 2008/2009 Note: All means for congregational variables are weighted, except for the mean for the weight variable.

35 25 Table 2-3. Descriptive Statistics for the Attenders and Congregations N Description Mean Std. Dev. Attender-level Variables Age 44,358 In years (18-100) Female 45,262 Female= Education 44,731 1=Less than High School diploma to 5=Graduate degree Income 40,818 1=Less than $10, to 8=$150,000 or more Asian 44,588 1=Asian African American 44,588 1=African American Hispanic 44,588 1=Hispanic White, non-hispanic 44,588 1=White, non-hispanic Other Race 44,588 1=Other race Attendance 46,327 1=Once a month or less to 4=More than once a week Congregation-level Variables Mainline Protestant 227 1=Mainline Protestant Evangelical Protestant 227 1=Evangelical Protestant Black Protestant 227 1=Black Protestant Catholic 227 1=Catholic Other Traditions 227 1=Other Traditions Congregation Size Average Weekly Attendance (15-10,000) Number of Social Service Activities 227 Number of activities (0-20) Source: U.S. Congregational Life Survey, 2008/2009

36 26 Chapter 3 Religious Tradition and Involvement in Congregational Activities That Focus on the Community 3 As the first empirical study in this dissertation, this chapter explores involvement in congregationally sponsored community service activities, which is the main focus of this dissertation. It also incorporates involvement in congregationally sponsored evangelism activities in order to more holistically depict how congregations engage in the broader community. This chapter describes how involvement in these activities varies among different religious traditions. Introduction Research on community involvement underscores the importance of religion. Despite a near consensus that religious people are more likely to be involved in the community than non-religious people (Putnam 2000; Beyerlein and Hipp 2006; Driskell et al. 2008; Putnam and Campbell 2010), there is little research concerning why some religious people are involved in the community while other religious people are not (see Park and Smith 2000 and Schwadel 2005 for notable exceptions). This chapter addresses that question by exploring whether some attenders of religious congregations are more 3 A version of this chapter is currently published in the Interdisciplinary Journal of Research on Religion (

37 27 likely than other attenders to engage in a unique form of community involvement-- participating in congregational activities that focus on the community. These congregationally sponsored activities can take two different forms. Some of these activities concern community service, like serving meals at a soup kitchen or homeless shelter, providing cash assistance for impoverished families, offering afterschool programs for neighborhood children, and advocating for a living wage for lowincome workers (Cnaan 2002). On the other hand, these activities can also focus on evangelism, that is, building relationships and even serving in the community in order to encourage people to join a religious group (Wilson and Janoski 1995; Dunn 2012). Since these two types of community involvement community service and evangelism are commonly contrasted in the literature on religion and community involvement (Kanagy 1992; Wilson and Janoski 1995; Schwadel 2005; Beyerlein and Hipp 2006), this chapter examines attenders involvement in each of these types of congregationally sponsored activities. Furthermore, it also describes how involvement in each of these activities varies among different religious traditions. This chapter contributes to the literature on religion and community involvement in a number of ways. First, whereas other studies have focused on describing how religious people are more likely than non-religious people to be involved in the community (Putnam 2000; Beyerlein and Hipp 2006; Driskell et al. 2008; Putnam and Campbell 2010), this chapter investigates why some attenders are involved in the community while other attenders are not. There are additional contributions that are the result of using this narrowed sample. By limiting its attention to attenders, this chapter can examine involvement in specific congregational activities that focus on the

38 28 community, which is not normally measured in surveys of the general American population. It can also explore how congregational context in this case, each congregation s religious group or tradition relates with involvement in these activities because all of the attenders are connected to a religious congregation. Finally, it can address whether the relationship between religious tradition and involvement in congregationally sponsored community service activities is different than the relationship between religious tradition and involvement in other community organizations. Literature Review The main approach that scholars use to study different religious groups is categorizing them into religious traditions, based on which denominations and groups have similar histories and theologies. The largest religious traditions in America are Mainline Protestants, Evangelical Protestants, Black Protestants, and Catholics (Steensland et al. 2000). Affiliation with these groups is related to religious beliefs and behaviors, political and social attitudes (Steensland et al. 2000), and even involvement in community organizations (Beyerlein and Hipp 2006; Loveland et al. 2008). Mainline Protestants have the most memberships in charitable organizations (Beyerlein and Hipp 2006:107), and Mainline Protestants, Black Protestants, and Catholics have more memberships in voluntary, community-based organizations than Evangelical Protestants do (Beyerlein and Hipp 2006:107; Loveland et al. 2008:14). These studies argue that Evangelicals are less likely to be involved in community organizations because they are more concerned with evangelism (Beyerlein and Hipp 2006:100; Loveland et al. 2008:7).

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