Keywords

Community sentiment can differ dramatically based on individuals’ personal characteristics. Thus, many community sentiment studies focus on the relationship between community sentiment and individual differences. For instance, Democrats and Republicans typically differ in their support for various laws such as abortion (e.g., Lindsey, Sigillo, & Miller, 2013). The current chapter provides an example of a study that investigates how individual differences are related to level of support for Safe Haven laws.

Further, as is common in some psychological research (including community sentiment research), students are often used as participants. As such, the chapter will discuss the general body of studies comparing student and nonstudent samples. The general finding of such research (e.g., jury decision-making studies) is that although there can be differences between these two groups depending on the topic being studied, there tend to be only limited differences between student and nonstudent samples. Therefore, in general, student populations are typically adequate proxies for community members. This ultimately could depend on the topic being studied, however, as student status could relate to sentiment on only some topics.

This chapter first provides an in-depth discussion of two common approaches to community sentiment research (and social psychology research more broadly): assessing differences in sentiment based on individual differences and using a student sample. The chapter then offers an example of a study using these methods. Specifically, this study investigated the relationship between students’ individual differences and their support for Safe Haven laws (i.e., laws allowing for the legal abandonment of a child).

Assessing Individual Differences in Sentiment

Community sentiment is rarely, if ever, uniform across a population. As such, researchers have often studied what individual characteristics are associated with individuals’ attitudes. Many journals (e.g., Personality and Individual Differences; Individual Differences Research) focus specifically on research exploring individual differences in a variety of areas within psychology, and other journals publish studies of individual differences on topics related to the journal (e.g., religion). Such studies of individual differences include studies of topics related to families and children, similar to some included in this volume (e.g., abortion; Lindsey et al., 2013; in vitro fertilization; Sigillo, Miller, & Weiser, 2012).

A person’s ideology, beliefs, and values are closely linked to many individual differences and are sometimes the bases for one’s sentiment. For instance, Republicans typically value traditional family structure more than Democrats (Arnold & Weisberg, 1996). This may explain why Republicans tend to oppose nontraditional family situations such as gay relationships more than Democrats (Burnett & Salka, 2009). Similarly, personal experiences unique to people with certain individual characteristics (e.g., gender) can affect one’s sentiment. For instance, men are less supportive of women having autonomy in abortion decisions (Patel & Johns, 2009), possibly because pregnancy affects women and men differently. A small sample of individual differences that are sometimes related to sentiment about issues affecting family and children include religion, gender, political affiliation, and race.

Religion is related to sentiment concerning many topics concerning family and children, including pregnancy, marriage, divorce, and child raising. Pro-life abortion attitudes were positively related to frequency of prayer/church attendance (Adamczyk & Felson, 2008) and orthodox Christian beliefs (Lindsey et al., 2013; Mavor & Gallois, 2008); in contrast, pro-choice attitudes were related to being on a religious “quest” (e.g., an open-ended search for religious meaning conducted with the knowledge that firm answers are not obtainable; Mavor & Gallois, 2008). Similarly, religious characteristics (i.e., fundamentalism, orthodoxy, devotionalism, and extrinsic religiosity) were related to attitudes toward in vitro fertilization use by nontraditional mothers-to-be (e.g., lesbians, single mothers; Sigillo et al., 2012). In addition to these pregnancy issues, religious beliefs (i.e., orthodoxy, literal interpretism, evangelism) and religious motivations (i.e., extrinsic religiosity) were all negatively related to support for gays and gay rights to marry, adopt, and practice sexual behavior (Miller & Chamberlain, 2013). Conservative Protestant beliefs were positively related to support for corporal punishment (Ellison & Bradshaw, 2009) and more restrictive attitudes toward divorce (Kapinus & Flowers, 2008). This small sample illustrates a few of the many relationships between religious characteristics and sentiment.

Gender is also related to sentiment about topics concerning children and families. Compared to men, women were more approving of the use of in vitro fertilization (Lasker & Murray, 2001) and making divorce harder to acquire (Kapinus & Flowers, 2008). Meanwhile, men were more supportive than women of using formula for feeding infants (Chang, Valliant, & Bomba, 2012) and using physical discipline and critical feedback to correct children’s misbehavior (Budd et al., 2012).

Political affiliation is an oft-studied individual difference. Compared to Republicans, Democrats were more supportive of nontraditional mothers-to-be who wanted to use in vitro fertilization and were less supportive of doctors who refused to perform in vitro (Sigillo et al., 2012); Democrats were also more supportive of the right to abortion (Hess & Rueb, 2005) and less supportive of parental notification provisions requiring minors to get permission before obtaining an abortion (Lindsey et al., 2013). More broadly, political attitudes were related to other family issues. For instance, sociopolitical conservatism was positively related to support for corporal punishment (Ellison & Bradshaw, 2009) and negatively related to attitudes toward gays and lesbians (Hicks & Lee, 2006).

Race is also a frequently studied individual difference in studies investigating sentiment toward topics related to family and children. In an early study, African Americans were less approving of in vitro fertilization than Caucasians (Dunn, Ryan, & O’Brien 1988), but more recent research found that race differences varied depending on the identity of the woman (e.g., lesbian, single woman, a woman with early onset alzheimer’s; Sigillo et al., 2012). Race was also related to some attitudes about relationships: African Americans have more negative attitudes toward gays and lesbians than White Americans (Lewis, 2003), and African Americans tend to have more negative attitudes about marriage (see Chap. 10 this volume). As for parenting, African American participants are more supportive of physical discipline than Asians, Hispanics, Caucasians, and mixed ethnicity participants, while Asians were more supportive than Hispanics and Caucasians (Budd et al., 2012).

Considerations When Conducting Studies of Individual Differences in Sentiment

While this is by no means a comprehensive summary of individual differences in sentiment regarding family and children issues, it does illustrate the range of differences and topics that have been studied in community sentiment research. When conducting such research, there are a number of issues that should be considered. First, it is important to study the interactions between multiple individual differences. For instance, southern men were more supportive of corporal punishment than southern women, but no gender differences were found for other regions (Flynn, 1994); thus, gender mattered—but only in one region of the United States.Footnote 1

Other considerations are statistical in nature. For instance, researchers should determine whether two or more individual difference predictor variables are highly correlated; in such cases, multicollinearity will affect the results for those variables (although the predictive power of the full model as a whole is not affected). Because these two variables are redundant, the validity and reliability of results for those variables may be questionable, as results may change substantially with even minor changes in the model or data. Various remedies are available to address multicollinearity issues, although that discussion is beyond the scope of this chapter.

Covariance in individual difference measures is a consideration in some studies that attempt to separate the effects of variables that might vary together. Researchers might also control for certain individual differences in order to see how much variance in attitudes is explained by an individual difference predictor, after controlling for other factors known to relate to the outcome variable (attitude). For instance, Flynn (1994) was interested in whether sentiment regarding corporal punishment varied by region of the United States (e.g., south versus northeast). Regions differ in many ways such as religion and political affiliation—and these differences also predict support for corporal punishment. So, Flynn controlled for sociodemographic differences (e.g., age, education, religion, gender) in order to remove the influence of these variables and isolate region as a predictor. In other examples, Patel and Johns (2009) used religion as a covariate in their study of gender differences in abortion attitudes because religion is also known to relate to abortion attitudes; Ellison and Bradshaw’s (2009) study of corporal punishment revealed that sociopolitical conservatism has an effect independent from religious variables.

A third consideration is the number of measures of an individual difference that are taken. For instance, if a researcher wanted to investigate the relationship between “religion” and sentiment, it might be easiest to simply ask for participants’ religious affiliation. Affiliation is only one of many measures of religiosity, however. Numerous studies have found that affiliation is often not related to sentiment, but religious characteristics (e.g., fundamentalism, devotionalism) are related (Ellison & Bradshaw, 2009; Lindsey et al., 2013; Sigillo et al., 2012). Thus, multiple measures of individual differences can offer a more complete and detailed picture of the relationships of interest.

A final consideration is the number of measures of sentiment taken. Multiple measures are often necessary because sentiment can differ depending on the specific stimuli. For example, African Americans were less supportive of gays in general but more supportive of some gay rights compared to Caucasians (Lewis, 2003). Similarly, participants were more supportive of the use of in vitro fertilization for some types of nontraditional mothers-to-be than others, and various individual differences produced different patterns of support for the multiple categories of women (Sigillo et al., 2012). Chapters 3 and 8 in this volume further discuss the need for multiple measures of sentiment, but will not be discussed here to prevent redundancy.

In sum, the study of individual differences in community sentiment is quite broad and incorporates a wide variety of individual difference measures and topics. While there are a number of considerations researchers should consider, studying individual differences is an important aspect of the study of community sentiment.

Using Convenience Samples of Students to Study Community Sentiment

This chapter illustrates how some community sentiment studies are conducted using a student sample. As discussed in depth in Chap. 3, two popular methods of measuring sentiment include surveys and mock juror studies. Mock juror studies measure sentiment inasmuch as they measure preference for a penalty (e.g., death penalty or a life in prison, length of a sentence); often they try to manipulate this sentiment by manipulating some independent variable. Surveys more directly measure sentiment through close-ended measures (e.g., Likert-type scales) or open-ended-type measures. Surveys do not often manipulate an independent variable, but sometimes they do (see Chaps. 4, 8, and 9 this volume). Both surveys and mock jury studies frequently use student samples, often freshman and sophomores taking social science classes that require participation. The main concern with using student samples is external validity; specifically the concern is whether students properly represent the population as a whole (see Wiener, Krauss, & Lieberman, 2011). As discussed below, this is more critical in some circumstances than others (e.g., because sentiment about some issues is not different between students and nonstudents). This chapter discusses the use of students and then gives an example of this technique.

While the use of students as a convenience sample has been addressed in many areas of psychology (e.g., Barua, 2012; Wiener et al., 2011), this chapter will focus on the debate within the law-psychology realm, as there has been much discourse in this area in recent years, and because the topics included in this book pertain to law or the legal system more broadly. The sentiment of college students toward criminal justice issues is studied much more now than prior to 1990 (Hensley, Miller, Tewksbury, & Koscheski, 2003). However, researchers have begun to study students’ attitudes more in recent years, including attitudes toward topics such as criminal punishment (Farnworth, Longmire, & West, 1998; Lane, 1997; Mackey & Courtright, 2000), juvenile justice policy (Benekos, Merlo, Cook, & Bagley, 2002), policing (Carlan & Byxbe, 2000), death penalty (Payne & Coogle, 1998), electronic monitoring of offenders (Payne & Gainey, 1999), the war on drugs (Farnworth et al., 1998), legal responses toward pregnant drug users (Chaps. 8 and 15, this volume), fear of crime (Dull & Wint, 1997), police use of social media (Spizman & Miller, 2013), laws regulating online teacher-student interactions (Chap. 11, this volume), and restrictions on abortions for minors (Lindsey et al., 2013). This is by no means a comprehensive list, as there are countless other studies.

Many of these studies intentionally sought out a student sample. For example, Lane (1997) measured changes in students’ attitudes before and after they attended a corrections class, Farnworth et al. (1998) compared freshman and seniors, and Mackey and Courtright (2000) compared attitudes of criminal justice majors and other majors. Other studies used students as a convenience sample (e.g., Chaps. 4, 8, 11, this volume) or chose students primarily because they are similar in age to those affected by the issues being studied (e.g., Chap. 11, this volume; Lindsey et al., 2013). Often, student and nonstudent samples vary in many personal characteristics, but this does not lead to any differences in verdicts (e.g., Hosch, Culhane, Tubb, & Granillo, 2011).

In addition to the studies listed above, some mock juror decision-making researchers also use convenience samples of students. Most juror decision-making studies use an experimental design and ask students to issue a verdict, assign the defendant a sentence, and/or award a plaintiff damages. Although many students are jury eligible (and some studies only include jury-eligible students), a student sample is not exactly comparable to a typical sample of jurors. Students and nonstudents differ in many ways, some of which could affect the outcome of studies; for instance, they might have different understandings of the law and legal procedure; different attitudes toward crime, police, and deviance; different biases and stereotypes; different life experiences; and so on.

Bornstein (1999) surveyed the literature from the first 20 years of Law and Human Behavior and determined that only 6 out of the 26 studies he reviewed reported significant differences between students and nonstudents. Nevertheless, there is concern. A special issue of Behavioral Sciences and the Law in 2011 was dedicated to this topic; a brief review of the articles in this issue—and other relevant studies—illustrates the concerns with student samples. The three main concerns associated with using convenience samples of students are that the groups have different characteristics, make different decisions, and use different decision-making processes.

The most basic concern is that university student samples may have different personal characteristics from the community as a whole. Student samples often contain participants that have higher socioeconomic status, are more educated, have better verbal skills, and are less racially diverse (see e.g., Barua, 2012) than the broader community. The samples might differ in many personal characteristics that are related to jury decisions, including: conservatism, authoritarianism, and cognitive capacities (Wiener et al., 2011). This is important because demographic characteristics are often related to sentiment, legal attitudes, and judgments, as discussed in detail above. In addition to different demographics, students and community members might have had different experiences which could affect their judgments or thought processing. For instance, differences between judgments made by students and community members in a hostile sexism case could be partially due to community members’ greater experience with workplace interactions and/or sexism in general (Schwartz & Hunt, 2011). Community members were more favorable toward an overweight victim of medical malpractice than were students, perhaps because community members have had personal experience with the difficulty of maintaining a healthy weight (Reichert, Miller, Bornstein, & Shelton, 2011). Particularly of relevance to the current study are the religious experiences and characteristics of students versus nonstudents. University students are experiencing a time of religious exploration and transition; their evolving development allows them to begin to think of religion in new ways (e.g., McNamara Barry, Nelson, Davarya, & Urry, 2010; Stoppa & Lefkowitz, 2010). Thus, college students’ religiosity and religious experiences might differ from that of nonstudents. If religion is related to sentiment, then sampling only students might affect the generalizability of the study.

In addition to differing in characteristics, student samples might also differ from the general population in the decisions they make. Farnworth et al. (1998) found that college freshman participants were more punitive than seniors. This could be due to education or maturity. This suggests that freshmen (who are commonly used student participants) have different sentiment from seniors; thus, freshman participants might differ even more from the general population than from seniors. Recent studies have revealed that nonstudent samples gave higher punitive damage awards (Fox, Wingrove, & Pfeifer, 2011), were more punitive toward a homicide defendant (Keller & Wiener, 2011), but were less likely to find the defendant doctor liable in a malpractice trial (Reichert et al., 2011).

Students might also differ from the general population in the process they use to form sentiment or make decisions. These processes can involve biases, cognitive processes, and the legal aspects the participant relies on while making a decision. Compared to community samples, students were less likely to exhibit racial bias (Mitchell, Haw, Pfeifer, & Meissner, 2005) and use their biases about rape (Keller & Wiener, 2011) in making juror decisions. Students can be encouraged to overcome their biases through a “bias correction intervention,” but community members resist this intervention (McCabe & Krauss, 2011). Further, students’ verdicts were related to cognitive processing style (i.e., need for cognition and faith in intuition; McCabe, Krauss, & Lieberman, 2010) and amount of cognitive effort (McCabe & Krauss, 2011), but community members’ verdicts were not. Finally, the two groups use expert testimony differently (McCabe & Krauss, 2011) and appropriate damages differently (Fox et al., 2011). Compared to students, nonstudents are more influenced by evidence (Fox et al., 2011) and react much more to culture-based testimony (Schwartz & Hunt, 2011).

In sum, there are many differences between student and nonstudent samples, some of which can affect decisions and processing. The key is to determine when a student sample is likely to be generalizable and when it is not; this is an area that is currently getting a lot of attention in the literature, as just discussed briefly above (see, e.g., Wiener et al., 2011).

Overcoming Limitations of Student Samples

As discussed in Chap. 3 of this volume, there are ways to overcome the limitations of a convenience sample of students. Researchers’ ability to obtain representative samples of the US population (e.g., random digit phone dialing) has improved in recent decades. Most recently, Amazon.com’s MTurk system allows anyone with a computer and the internet to participate in online studies for payment. MTurk produces a sample that is significantly more diverse than other samples (Buhrmester, Kwang, & Gosling, 2011). Also, multiple judgment and decision-making studies have found comparable results using MTurk participants and lab participants (see Mason & Suri, 2012). Although sources of participants such as MTurk produce other limitations (e.g., only participants who are internet and computer savvy can participate), they do address some of those discussed above. A good approach is for researchers to begin a line of research using convenience samples of students and follow-up with samples that are more diverse (see also Wiener et al., 2011). Researchers will then be able to determine when participant identity matters and when it does not; later studies can choose samples accordingly. Such strategies will improve the external validity of research studies.

In order to demonstrate how community sentiment research is sometimes conducted with an eye toward finding individual differences in sentiment within a student sample, this chapter now offers an analysis of sentiment regarding Safe Haven laws.

Introduction to Safe Haven Laws

In 2011, a Tennessee mother was charged with killing her twin sons moments after they were born (CNN, 2011). In 2012, a teen mother from Florida admitted to choking her newborn boy to death and hiding his body in a shoebox because she feared her parents’ reaction (Cavazini, 2012). More recently, in February of 2013, a prosecutor from Ohio educated the public about Safe Haven laws after an Ohio woman received a life sentence for drowning and strangling her newborn son and then hiding his body in a freezer (Feehan, 2013). This most recent example shows the belief held by some (like the Ohio prosecutor above) that tragic past and future deaths might be avoided if more people are aware of Safe Haven laws.

Safe Haven laws are designed to prevent infanticide by offering parents the option to anonymously relinquish parental rights over their children to authorities (e.g., hospitals, fire stations) without penalty (Dreyer, 2002; Hammond, Miller, & Griffin, 2010). These laws, which were enacted in the late 1990s, differ from state to state and may not be what people typically think of as “laws”. For example, some states only allow the parent to legally abandon the child until the child is 3 days old; other states set the time limit at 30 days or have no time limit (Hammond et al., 2010). Individuals may think of a law as some type of restriction or punishment, but Safe Haven laws are not a punishment—they act as a way for individuals who do not want their child to give up their parental rights without fear of punishment or legal consequences. Sanger (2006) argued that the focus of Safe Haven laws is not criminological, but rather they are used to further the politics surrounding the “culture of life.”

Although these laws are well intentioned, there is the potential for negative side effects. For example, a law that allows parents to relinquish parental rights to any child under the age of majority (i.e., the age at which a child becomes adult—typically 18 in the United States, but this age varies from state to state), as Nebraska’s law did when it was instated in 2008, can overburden the state’s child welfare system. Parents could (as they did in Nebraska) start using the Safe Haven laws to “get rid of” their difficult teenagers as opposed to the law’s initial purpose of preventing infanticide. Of the 35 children left at the Nebraska Safe Haven drop-off sites, only 1 was younger than 6 and many were teens with behavioral problems (O’Hanlon, 2013). Once Nebraska lawmakers realized the need for increased behavioral and mental health services for youth and their parents, they passed an overhaul of the state’s child welfare system—it is still too soon, however, to gauge the effectiveness of these changes in meeting the needs of the community (O’Hanlon, 2013).

The controversy surrounding Safe Haven laws has led to the examination of the merits, disadvantages, and support of these laws (e.g., Donnelly, 2010; Hammond et al., 2010; Racine, 2005). In 2007, Rutgers Eagleton Polling Institute conducted a poll of 604 adult (i.e., over 18 years old) New Jersey residents to assess public opinions of Safe Haven laws (Safe Haven Awareness Promotion Task Force, 2007). This poll indicated a high level of community support for these laws, with 80 percent of respondents either strongly approving or approving of multiple versions of the law. The poll also collected respondents’ demographics, including gender, race, age, education, and income. There were no significant differences between groups (e.g., males/females, whites/non-whites) in terms of support for Safe Haven laws, and support for all groups was generally high (varying between 67 and 89 %). The poll did not, however, investigate religion as a possible influence; the current study seeks to fill that gap and further examine the factors that impact individual’s sentiment toward Safe Haven laws.

Examining Individual Differences

There are several aspects of religion and religiosity that can be examined when attempting to study “religion” and its relationship to community sentiment. The current study uses six religious characteristics to further examine some of these relationships. The scales measure participants’ (1) amount of religious fundamentalism, (2) amount of religious evangelism, (3) involvement in organized religion, (4) value placed on religion, and (5) literal interpretation of the Bible.

Religious fundamentalism is defined as the belief that there is one set of religious teachings clearly containing fundamental, essential, and inerrant truths about humanity and deity. Fundamentalists believe that this truth is opposed by evil forces, the truth must be followed today according to the essential and unchanging practices of the past, and that believers of these fundamental teachings have a unique relationship with the deity (Altemeyer & Hunsberger, 1992, p. 118). Many researchers have found that fundamentalism is associated with punitiveness (Grasmick, Davenport, Chamlin, & Bursik, 1992; Grasmick, Cochran, Bursik, & Kimpel 1993; Young, 1992).

Evangelism refers to the desire and attempt to convert other individuals to one’s faith (Young, 1992). In studies that find relationships between evangelism and punitiveness, those high in evangelism tend to be less punitive than their counterparts (Bornstein & Miller, 2009).

Another fairly consistent finding in the literature is that individuals high in biblical literalism (i.e., believe the Bible is the literal word of God) are more punitive than those who do not (e.g., Young, 1992).

The Fetzer Institute (1999) describes the values scale as an assessment of the extent to which a person’s behavior reflects a normative expression of his/her faith or religion as the ultimate value. This is a different concept than just simply valuing religion; it is having religion as the ultimate value. The organizational practice scale is an assessment of the extent to which a person is involved with a formal religious institution. These measures have not been linked to punitiveness and thus are exploratory variables in this research.

In addition to the religious measures, participants also provided information on their amount of legal authoritarianism. Legal authoritarians (i.e., those high in legal authoritarianism) are more likely than nonlegal authoritarians to believe that the rights of the government trump those of the individual (Butler & Moran, 2007).

Overview of Study

The current study measures community sentiment about Safe Haven laws that apply to children of any age, as these may be the most controversial types of Safe Haven laws. In addition, this is the first study, other than the Rutgers poll described above, which investigates relationships between any individual difference characteristics and support for Safe Haven laws. The general research question for this study is: Is there a relationship between support for Safe Haven laws and the participants’ gender, race, political affiliation, level of evangelism, level of fundamentalism, involvement in organized religion, value placed on religion, and literal interpretation of the Bible?

Method

Participants and Procedure

Participants (N = 133) were mostly female (62 %), Democrats (56 %), and White (72 %) and ranged from 18 to 35 years (M = 20.34; Mdn = 20). Participants were recruited via the University of Nevada, Reno’s subject pool; they completed the survey on surveymonkey.com. Participants completed six scales measuring different aspects of religious beliefs and attitudes. For all scales, higher scores mean higher levels of that characteristic. All scales were created by averaging participant responses. Participants indicated their support for a Safe Haven laws. Finally, basic demographic information was collected from all participants (see Table 6.1).

Table 6.1 Summary statistics
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Measures

A variety of measures assessed authoritarianism, multiple religious beliefs, and demographics.

Legal Attitudes Questionnaire: The Revised Legal Attitudes Questionnaire (RLAQ) is a scale that measures an individual’s level of legal authoritarianism (Kravitz, Cutler, & Brock, 1993). The scale included 23 items (e.g., “Defendants in a criminal case should be required to take the witness stand”; α = 0.73). The Likert-style items were rated from 1 (strongly disagree) to 5 (strongly agree).

Evangelism Scale: Evangelism was assessed with Putney and Middleton’s (1961) 6-item measure of fanaticism (a measure of evangelism; Bornstein & Miller, 2009; α = 0.72). Items (e.g., “I have a duty to help those who are confused about religion”) were rated from 1 (strongly disagree) to 5 (strongly agree).

Fundamentalism Scale: Fundamentalism was assessed with Altemeyer and Hunsberger’s (2004) Revised 12-Item Fundamentalism Scale (α = 0.86). The twelve items (e.g., “The basic cause of evil in this world is Satan, who is still constantly and ferociously fighting against God”) were rated on a scale of 1 (strongly disagree) to 5 (strongly agree).

Organizational Practice and Values Scales: Both of these scales are subscales from the Fetzer Institute’s multidimensional measure of religiosity-spirituality (1999). The Fetzer Organizational Practice scale included two questions, for example, “How often do you attend religious services?” (α = 0.79). Items were measured on a 9-point Likert scale ranging from 1 (never) to 9 (several times a week). The original Fetzer value scale included three questions examining how much individuals believe religion is central to their life, such as: “My whole approach to life is based on my religion”; items were rated on a scale of 1 (strongly disagree) to 5 (strongly agree). This three-item scale, however, was unreliable for this sample (α = 0.43). One item that had low correlations with the other two (i.e., the recoded item “Although I believe in my religion, many other things are more important in life”) was dropped from the scale for all analyses. The new two-item scale was acceptably reliable (α = 0.72).

Biblical Interpretism: This measure is a single question (“Do you believe that the Bible is the actual word of God and is to be taken literally, word for word?”) answered with a dichotomous yes/no response (Young, 1992).

Demographics: Gender, race, and political affiliation were all self-reported by participants. Gender was dummy coded so that women = 1 and males = 0; race was dummy coded so that white = 1 and all other races = 0; political affiliation was dummy coded so that Democrat = 1 and Republican = 0. Because prior studies have focused on the differences between these two main political groups (i.e., Republicans and Democrats; e.g., Sigillo et al., 2012), the authors decided to compare only these two political categories. Individuals who self-identified as a different political affiliation were not included in the analyses.

Support for Safe Haven Laws: Participants rated on a 1 (no, absolutely not) to 5 (yes, absolutely) scale their support for the following statement: “Would you support a law that would allow a woman to legally abandon a child in a safe place (e.g., a hospital) no matter what the age of the child?”

Results

Overall support for Safe Haven laws was moderate (M = 2.39; SD = 1.39). An ordinary least squares (OLS) regression examined which individual differences variables significantly predicted participants’ support for Safe Haven laws. Although some scales were correlated (see Table 6.2), the researchers found no multicollinearity. The overall model examining the relationship between the outcome variable (support for Safe Haven laws) and all predictor variables (gender, race, political affiliation, legal attitudes, evangelism, fundamentalism, organizational practice, religious values, and biblical interpretism) was significant (R 2 = 0.12; F(9,132) = 1.93, p = 0.05), indicating that individual differences do, in fact, significantly predict support for Safe Haven laws. Specifically, organizational practice (b = 0.19, p = 0.02) was a significant predictor of support for Safe Haven laws. Three variables were nearing significance: political affiliation (b = −0.54, p = 0.06), fundamentalism (b = 0.48, p = 0.07), and evangelism (b = −0.43, p = 0.07). All other relationships between individual difference predictors and the dependent variable were not significant (see Table 6.3). An examination of the interaction effects of the various predictor variables was not possible in the current study due to the sample size. A power analysis was conducted using G-Power, indicating that the sample (and resulting power) allows for the detection of medium and large effects for the main predictor variables, but the inclusion of the interaction terms and thus any significant findings in that model would be highly suspect.

Table 6.2 Correlations between variables
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Table 6.3 Summary of ordinary least squares regression model examining the relationship between individual differences on level of support for Safe Haven laws
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Discussion

The current study provided preliminary evidence of relationships between individual differences and student community sentiment about Safe Haven laws. Findings indicate that the more individuals attend religious services and participate in other religious meetings, the more they support Safe Haven laws. Prior research (e.g., Gorsuch, 1995) has found that regular attendance is related to behavioral conformity. Therefore, individuals who attend such services might be comfortable with power hierarchies (e.g., comfortable with the pastor—or authority figure—telling them what to do). Likewise, these individuals might also favor parents (as authorities) being able to decide whether to relinquish their parental rights (i.e., favor Safe Haven laws). This finding is also consistent with research finding that the more individuals attend religious services and participate in other religious meetings, the more likely they are to support parental involvement clauses for minors’ abortion (Lindsey et al., 2013). In both instances, this group of individuals favors parents having control over their children.

Although not statistically significant at a p < 0.05 level, the finding that Democrats support Safe Haven laws less than Republicans was nearing significance. This finding is similar to research indicating that Republicans are more supportive of laws requiring parental involvement in minors’ abortion, in that Republicans value the ability to have control over and make decisions about their children’s lives (Lindsey et al., 2013).

Another finding nearing significance indicates that the more fundamental individuals are, the more they support Safe Haven laws. Previous research has found that religion is a strong predictor of attitudes toward parental involvement, with more religious people holding favorable attitudes toward parental involvement (Mahoney, Pargament, Tarakeshwar, & Swank, 2008). Although this meta-analysis had a wide variety of indicators for what it meant to be “religious,” this finding can still be useful in understanding the impact of fundamentalism on support for Safe Haven laws. Individuals high in fundamentalism have a core set of strong and unshakable beliefs; among those beliefs is the view that parents should be involved in their child’s life and make decisions regarding that child. In other words, parents high in fundamentalism endorse the right of the parent to determine the fate of his/her child.

A final finding nearing significance indicates that the more evangelical individuals are, the less they support Safe Haven laws. This information appears to conform to what may be the “typical” evangelical belief system that places value on family and God’s ability to save people according to His will. Thus, because God provided a child (or children), parents should keep the child and bring the child up in the faith in order to spread God’s word and grow the faith. This belief is consistent with not supporting Safe Haven laws. Although these last three results discussed were not statistically significant, it is still important to explore the possible relationships so that future researchers are aware of the potential interplay.

It is worth noting that the overall rate of support for Safe Haven laws in the current study (22 % either supported or strongly supported the law) was quite a bit lower than that observed in the Rutgers study (i.e., 80 % strongly approving or approving of the law). It is possible that this difference is a result of location, sample, or question wording. The Rutgers poll only asked about support for Safe Haven laws for infants 30 days old or younger, whereas the current study asked about Safe Haven laws for a child of any age. Thus, the difference in attitudes measures might account for differences in findings between the studies.

Another possible explanation for different findings could be that the Rutgers poll was conducted in New Jersey, whereas the current study was conducted in Nevada. There may be differences in support of Safe Haven laws based on the region of the United States in which an individual is asked. For example, there are certain states (e.g., Nebraska) where Safe Haven laws are more widely known about and discussed; this would allow individuals greater opportunity to collect information about and determine their opinion of the laws. Regional differences thus might explain differences between the studies.

A final possible explanation for differences between studies is that the current study employed a student sample, whereas the Rutgers poll sampled community members. This could indicate that students are not good proxies for the community, perhaps because there are important differences between the groups that lead to differing sentiment. For example, it is possible that students have less experience with having children and the stresses/responsibilities associated with that than community members; this might decrease their overall support for such laws. These differences highlight the importance of sampling from the population from which researchers want to generalize. Researchers who are interested in being able to confidently generalize findings to community members of a specific location should sample from those community members and not rely on students.

Conclusion

This chapter had two main goals: to illustrate (1) how community sentiment research can be conducted using a student sample and (2) how sentiment is sometimes related to individual differences. As to the chapter’s first goal, the literature review highlighted the importance of identifying whether the research topic is one in which students can be a good proxy for the community. The finding that the current sample is less supportive of Safe Haven laws than the Rutgers sample might indicate that this is one topic in which students are not good proxies for the general community. Only a single study using a single set of attitude measures for both students and community members could fully determine whether this is so.

Whether a student sample is adequate to represent the entire community is largely dependent on the topic at hand. Unfortunately, identification of when students do and do not represent the community is a relatively new endeavor in community sentiment research. Similarly, knowing when a sample from one part of the country can represent the sentiment of the entire country is difficult. Studies can be conducted with this specific goal in mind—if the researcher has the means to garner a broad enough sample. Researchers are wise to use student samples from only one region in the United States in exploratory research, but follow-up with broader samples as resources and new research questions arise.

Sometimes, however, a researcher might intentionally focus only on a particular group—such as college students. Understanding the attitudes of students toward Safe Haven laws might be particularly important to law and policymakers because it is often these younger citizens who have unwanted pregnancies and therefore could benefit from these laws. In such instances, the population of interest is young adults. A sample of college students is arguably a closer proxy to a population of young adults than a population of the community as a whole.

As to the chapter’s second goal, the literature review highlighted only a small number of the many individual differences that have been used in past studies. A handful of individual differences were used in the current study, though only one was significantly related to support (and three more neared significance).

Knowing what individual differences (if any) are related to support for a particular law can be useful to law and policymakers, as it can help identify groups (e.g., fundamentalist Christian groups, females) that do and do not support the law. This is important in helping lawmakers know the sentiment of their entire constituency. As noted above (and in more detail in Chaps. 1 and 19), there are many benefits that arise when laws coincide with community sentiment. Also, knowing which groups favor or disfavor a law can assist lawmakers in campaigning for legal changes. Although this has not been done frequently in the past, this information can help policymakers find a base of supporters who can repeat the message and advocate for changes (e.g., through social media and traditional campaigning strategies).

More broadly, this study demonstrates how some laws might not adequately reflect community sentiment of all subsets of the population. It is difficult to “please everyone” with the creation and implementation of laws because community sentiment can vary by many different factors including individual differences and group membership (including student status). This also demonstrates the difficulty in measuring sentiment because researchers and policymakers have to take into account many different characteristics in order to get a full picture of community sentiment.

In sum, community sentiment is complex. Measuring individual differences thought to be related to the topic at hand can help researchers and lawmakers/policymakers better understand community sentiment. Yet, knowing which individual differences to measure can be tricky—though researchers are aided by past sentiment research on similar topics. Further, knowing when a student sample is an adequate representative of the community as a whole can be difficult. Through much research, community sentiment researchers can gain a broader understanding of which differences to study—and what sample to use in doing so.