Examining the Relationship between Religiosity and Delay-of-Gratification:

Differentiating between Organizational and Personal Religiosity

A thesis presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Adam Carlitz

December 2018

© 2018 Adam Carlitz. All Rights Reserved. 2

This thesis titled

Examining the Relationship between Religiosity and Delay-of-Gratification:

Differentiating between Organizational and Personal Religiosity

by

ADAM CARLITZ

has been approved for

the Department of Psychology

and the College of Arts and Sciences by

Kimberly Rios and Ronaldo Vigo

Associate Professors of Psychology

Joseph Shields

Interim Dean, College of Arts and Sciences 3

ABSTRACT

CARLITZ, ADAM., M.S., December 2018, Psychology

Examining the Relationship between Religiosity and Delay-of-Gratification:

Differentiating between Organizational and Personal Religiosity

Directors of Thesis: Kimberly Rios and Ronaldo Vigo

Religiosity is positively related to self-regulation, though more research is needed to understand the nature of this relationship. For example, relatively few studies have examined the link between religiosity and delay-of-gratification (i.e., resisting immediate temptation in favor of some objectively larger, delayed reward). Most of the limited research on this topic has conceptualized and operationalized religiosity as though it were a unidimensional construct. We review literature that, instead, suggests religiosity be treated as a two-dimensional construct – consisting of organizational (i.e., religious practice/community) and personal religiosity (i.e., religious belief) dimensions. Personal religiosity elements are more strongly associated with asceticism than are organizational religiosity elements. Therefore, we hypothesized that personal religiosity would lead to greater delay-of-gratification than would organizational religiosity. Furthermore, we hypothesized that cognitive construal level, rational-experiential processing, and/or deontological thinking would mediate this effect. Consistent with the former hypothesis, experimental results indicated that activating personal, but not organizational, religiosity concepts increased delay-of-gratification. We did not find support for the latter hypothesis. We discuss the implications of these findings and future research directions. 4

DEDICATION

For Mom, Dad, Jared, Phil, and Lexi.

5

ACKNOWLEDGMENTS

I could not have done this without Drs. Kimberly Rios and Ronaldo Vigo’s help and support. You both make me a better scientist, researcher, and person each day.

Thank you both so much! I would also like to thank Dr. Nicholas Allan for his help with data analysis.

6

TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication ...... 4 Acknowledgments...... 5 List of Tables ...... 8 List of Figures ...... 9 Introduction ...... 10 Literature Review...... 12 Self-Regulation, Delay-of-Gratification, and Real-World Outcomes ...... 12 Measuring Delay-of-Gratification via Delay Discounting ...... 13 The Relationship between Religiosity and Delay-of-Gratification ...... 15 Religiosity as a Two-Dimensional Construct ...... 19 The Effects of Organizational vs. Personal Religiosity on Delay-of-Gratification .... 25 Cognitive Processing Systems as Possible Mediators ...... 27 Cognitive processing systems and delay-of-gratification...... 27 Cognitive construal level and two-dimensional religiosity...... 29 Deontological Thinking as a Possible Mediator ...... 31 Hypotheses ...... 35 Methods...... 37 Participants and Design ...... 37 Procedure ...... 37 Materials (see Appendix for all study materials) ...... 38 Experimental manipulation questionnaire...... 38 Delay discounting...... 39 Construal level...... 39 Deontological thinking...... 40 Rational vs. experiential thinking...... 40 Organizational and personal religiosity...... 41 Data Preparation ...... 41 Results ...... 43 Participants ...... 43 7

Main Analyses ...... 44 Hypotheses 1a, 1b, and 1c...... 44 Hypotheses 2a, 2b, and 2c...... 46 Exploratory Analyses ...... 47 Discussion ...... 55 Main Analyses ...... 55 Exploratory Analyses ...... 58 Limitations and Future Directions ...... 59 Conclusion ...... 63 References ...... 65 Appendix ...... 83 Experimental Manipulation ...... 83 Monetary-Choice Questionnaire (MCQ-27) ...... 84 Behavior Identification Form (BIF) ...... 85 Abbreviated Morality Founded on Divine Authority (A-MFDA) ...... 88 Rational-Experiential Inventory ...... 89 Religious Practice ...... 91 Demographics Questionnaire ...... 94

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LIST OF TABLES

Page

Table 1 Demographics ...... 44 Table 2 Means and Standard Deviations for Variables ...... 45 Table 3 Zero-Order Correlations for Measurement Variables ...... 47 Table 4 Results of Mediation Analysis ...... 47 Table 5 Religiosity Items for Exploratory Analyses ...... 49 Table 6 Zero-Order Inter-item Correlations for Religiosity Items ...... 51 Table 7 Exploratory Analyses: Nested Model Comparisons ...... 51 Table 8 Standardized Path Estimates for Three-Factor Model ...... 54

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LIST OF FIGURES

Page

Figure 1. Three-factor model of organizational and personal religiosity on DD ...... 52

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INTRODUCTION

People who are more religious are generally better at regulating their behavior compared to those who are relatively less religious (Fishbach, Friedman, & Kruglanski,

2003; Carter, McCullough, Kim-Spoon, Corrales, & Blake, 2012). However, some studies of this relationship have found mixed effects (e.g., Benjamin, Choi, & Fisher,

2016; Kim-Spoon, McCullough, Bickel, Farley, & Longo, 2015; Morgan et al., 2016).

One reason for these mixed findings may be that most of previous research has operationalized religion as a single, unidimensional construct (Boswell & Boswell-Ford,

2009; Gorsuch, 1984; Graham & Haidt, 2010). For example, many researchers have treated belief in God and religious attendance as interchangeable and assume the two have similar effects (e.g., DeWall et al., 2014; Morgan et al., 2016; Rounding, Lee,

Jacobson, & Ji, 2012). Given the multifaceted nature of religion, we conceptualize religion as a combination of two related but distinct dimensions: organizational religiosity (practice/community) and personal religiosity (faith/belief). Organizational and personal religiosity may make separate, or perhaps even opposite, predictions regarding various behaviors – including those related to self-regulation.

A small number of studies have examined different categories of religiosity (e.g., priming religion as opposed to God; Preston & Ritter, 2013). However, few studies have examined how organizational and personal religiosity dimensions and their sub-elements compare with one another and influence self-regulation. Even fewer have examined delay-of-gratification – a specific form of self-regulation. To address these gaps in the literature, we conducted an experiment to investigate whether and to what extent 11 organizational and personal religiosity uniquely influence delay-of-gratification. We hypothesized that personal religiosity would have a greater influence on delay-of- gratification than would organizational religiosity, and that differences in cognitive construal level (high-level vs. low-level thinking), rational-experiential processing, and/or deontological thinking would mediate this effect.

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LITERATURE REVIEW

Self-Regulation, Delay-of-Gratification, and Real-World Outcomes

For the purposes of this study, we adopt Baumeister and Vohs’ (2004) definition of self-regulation: the process by which “a person exerts control over his or her own responses so as to pursue goals and live up to standards” (p. 500).1 Self-regulation is

important to study given its positive relationship with various real-world outcomes. For

example, those with greater self-regulation generally demonstrate higher levels of

professional and academic achievement (McCullough & Carter, 2013), show lower levels

of substance abuse, and engage in fewer risky behaviors (McCullough & Willoughby,

2009).

Delay-of-gratification is a form of self-regulation that specifically refers to the

tendency or ability to resist immediate temptation in favor of an objectively greater,

future reward. Thus, delay-of-gratification predicts many of the same outcomes as does

self-regulation more generally. For example, delay-of-gratification is associated with less

cigarette smoking (Chabris, Laibson, Morris, Schuldt, & Taubinsky, 2008; Kirby &

Petry, 2004; Ohmura, Takahashi, & Kitamura, 2005), lower body mass index (BMI;

Charbis et al., 2008), more exercise frequency (Charbis et al., 2008), less alcoholism

(Bickel, Miller, Yi, Kowal, Lindquist, & Pitcock, 2007; Bjork, Hommer, Grant, &

Danube, 2004; Mitchell, Fields, D’esposito, & Boettiger, 2005; Petry, 2001), less illicit

1 Many experts consider self-regulation and self-control to be distinct concepts (e.g., Kopp, 1982, McCullough & Willoughby, 2009; Shanker & Barker, 2016). However, the present study is not particularly concerned with discussing or examining these differences. For simplicity, both terms will henceforth be referred to as “self-regulation.” 13

drug use (Hamilton & Potenza, 2012; Kirby, Petry, & Bickel, 1999; MacKillop, Amlung,

Few, Ray, Sweet, & Munafò, 2011), and less depression (Charbis et al., 2008). The

effects of delay-of-gratification expand beyond health-related behaviors; delay-of-

gratification is also associated with better long-term career outcomes and economic

decision-making (Angeletos, Laibson, Repetto, Tobacman, & Weinberg, 2001; Laibson,

1997; Logue & Anderson, 2001; Schoenfelder & Hantula, 2003).

Measuring Delay-of-Gratification via Delay Discounting

Many studies have operationalized delay-of-gratification as reduced delay

discounting (DD), also widely known as temporal discounting. Specifically, greater DD indicates less delay-of-gratification. DD describes the phenomenon whereby increases in temporal distance are associated with decreases in the subjective value of rewards

(Ainslie, 1975; Green, Fry, & Myerson, 1994; Green, Myerson, Lichtman, Rosen, & Fry,

1996). In other words, human beings prefer immediate to delayed gratification.

Consider, for example, a choice between receiving $100 now and $100 in two months.

Most people prefer the former option. This implies that the same objective amount of money ($100) is somehow subjectively worth less in two months than if it were made available immediately (for a more exhaustive review of DD and intertemporal choice, see

Green & Myerson, 2004; Klapproth, 2008; Rachlin, 2006). Therefore, again, those high in DD are low in delay-of-gratification.

Traditional DD assessments follow a similar format as in the example above; participants complete several trials that involve choosing between an objectively smaller more immediate reward and a larger more delayed reward. Many consider DD 14

assessments to be both valid and reliable methods of measuring delay-of-gratification

(e.g., Frederick, Loewenstein, & O’Donoghue, 2002; Hardisty & Weber, 2009; Johnson

& Bickel, 2002; Kirby, 2009; Kirby & Finch, 2010; Kirby & Petry, 2004; McCullough &

Boker, 2007; Mischel, Shoda, & Rodriguez, 1989; Mischel et al., 2011). After several

trials, researchers can analyze participants’ response patterns to estimate their discount

rates using a discount function (Green et al., 1996; Kirby & Maraković, 1996; Kirby,

Petry, & Bickel, 1999). Specifically, one’s discount rate is a measure of the degree to

which he or she devalues rewards over time.

In 1937, Samuelson used an exponential discount function to explain discounting

behavior. Exponential functions carry with them the assumption that decision-makers are

rational and consistent across time (Frederick, Loewenstein, & O’Donoghue, 2002;

Rachlin, 2006). However, human beings are often neither rational nor consistent. For

example, consider the phenomenon of dynamic inconsistency: people discount rewards

more for the same time difference (e.g., Δ2 months) when rewards are relatively proximal

(e.g., now vs. 2 months) rather than distal (e.g., 10 months vs. 12 months; Loewenstein &

Prelec, 1992; Rachlin, 2006; Thaler, 1981). In other words, people are present-biased. In

1987, Mazur developed a hyperbolic discount function that accounts for dynamic

inconsistency. His function provides a better fit to DD data than the previously accepted

exponential function (Green & Myerson, 1996; Kirby & Maraković, 1996; Madden,

Bickel, & Jacobs, 1999; Mazur & Biondi, 2009). In Mazur’s model (depicted below), V

represents the discounted value after a certain delay D. A is the amount of reinforcement.

Finally, k is a free parameter representing the magnitude of discounting (i.e., the discount 15

rate); larger k values indicate greater discounting and, therefore, lower delay-of-

gratification.

= 1 + 𝐴𝐴 𝑉𝑉 𝑘𝑘 ∗𝐷𝐷 Mazur’s (1987) Hyperbolic Equation

As a concrete example, if given a choice between $100 (V) now and $120 (A) in

two months (D), someone with a discount rate k = .1 would be indifferent between these

choices. Put differently, if someone with a discount rate k = .1 is offered $100 now, he or

she would require more than $120 in order to delay gratification between now and two

months from now. The temporal distance between now and two months from now causes

a devaluation of the objective $120 such that it is subjectively worth $100. Thus, the

decision-maker prefers to forgo almost as much as $20 to avoid delaying gratification over this particular two-month period. Importantly, knowing an individual’s discount rate allows for predictions about choice behavior over time.

The Relationship between Religiosity and Delay-of-Gratification

As mentioned previously, many studies have found that religiosity, in general, is

positively related to self-regulation. Furthermore, religiosity and self-regulation appear

to be related to many of the same outcomes, such as better physical and psychological

wellbeing (McCullough & Carter, 2013; McCullough & Willoughby, 2009). However,

almost all the research concerning religiosity and self-regulation outcomes (e.g., delay-

of-gratification) has been correlational (Graham & Haidt, 2010; McCullough &

Willoughby, 2009). In this section, we review the few studies that have, instead, used

experimental methods to investigate the direction of this relationship. Interestingly, 16 many of these studies report findings that conflict with one another; some suggest a positive relationship while others report null findings or even a negative relationship between religiosity and delay-of-gratification.

In 2012, Rounding and colleagues conducted a series of studies meant to address the question of whether religion enhances self-regulation. In each study, the researchers borrowed from Shariff and Norenzayan’s (2007) scrambled sentence task, which is able to implicitly activate God-related concepts (also see Shariff, Willard, Andersen, &

Norenzayan, 2016). During this task, researchers present participants with 10 sentences, each containing five words. For each sentence, participants must discard one word and reorder the remaining four words such that they form a coherent sentence. However, for participants in the experimental condition, half of the sentences include a word related to religion or God (e.g., divine, sacred, God). By contrast, those in the control condition only receive sentences with neutral words (e.g., sky, retrace, train).

Rounding and colleagues specifically addressed delay-of-gratification in their second study. Participants were randomly assigned to one of two conditions: religious- prime or neutral-prime. After completing the scrambled sentence task, participants were told the study was over and they could return either any time the next day to receive $5 compensation or any time one week later for $6 compensation. Rounding et al. reported a significant effect of condition. In particular, 60.7% of participants in the religious- prime condition chose to wait compared to only 34.4% in the neutral-prime condition.

The researchers concluded that implicitly priming religious concepts, as opposed to neutral concepts, better enabled participants to delay gratification. However, readers 17

should interpret these results with caution. For instance, perhaps participants were not

better able to delay gratification, but more motivated to do so for self-concept and/or reputational reasons. In other words, perhaps participants who chose to delay gratification wanted to prove to themselves that they could regulate their behavior or signal positive characteristics about themselves (e.g., trustworthiness, self-regulation, and patience) to others (i.e., the researchers) (Harrison & McKay, 2013).

Secondly, Harrison and McKay (2013) suggested that priming religiosity concepts may have actually decreased rather than increased delay-of-gratification – especially in

light of the small differences between $5 and $6 as well as between now and one week.

Assuming that having a good reputation is rewarding in the present, then Rounding et

al.’s decision scenario may have actually involved choosing either social rewards now

(i.e., reputation-signaling and bolstered self-efficacy) plus $6 in a week or no social

rewards now plus $5 in a day. If this was the case, the proximal reward would have been

the option that involved the $6 in one week because it also included immediate social

rewards.

In addition to the above alternative explanations, the religious composition of

Rounding et al.’s sample makes their results difficult to interpret. Specifically, Catholics

were the largest group, and Catholics are more likely to prefer immediate to delayed

gratification than are Protestants (Paglieri, Borghi, Colzato, Hommel, & Scorolli, 2013).

Rounding and colleagues thus may have obtained different results if their samples were

predominantly Protestant rather than Catholic. Such differences between religious 18

groups may suggest that religion, at least with respect to research on delay of

gratification, should not be treated as a unidimensional construct.

Morgan and colleagues (2016) conducted another study that examined the

relationship between religiosity, in general, and delay-of-gratification. The researchers

examined the effects of priming religion concepts on delay-of-gratification for older

adults with and without Parkinson’s disease. They additionally investigated the

relationship between chronic religiosity and delay-of-gratification. The researchers used

a computerized version of the Monetary Choice Questionnaire (MCQ-21; Kirby &

Maraković, 1996), that they borrowed from Paglieri et al. (2013), to measure delay

discounting; and they formed religious and neutral prime words based on pilot data. In

developing religious prime words, Morgan et al. (2016) did not distinguish between

different aspects of religiosity. They also did not do so in their measure of religiosity,

which consisted of two items about overall religiosity and spirituality: “To what extent do

you consider yourself a religious [spiritual] person?”

Morgan and colleagues reported that, overall, religious primes did not affect discounting. Specifically, there was no effect of religious primes for those with or without Parkinson’s disease. This result did not replicate Rounding et al.’s (2012) findings (discussed above). Notably, however, Morgan and colleagues found that religiosity, as an individual-differences variable, was positively related to discounting: as religiosity increased, delay-of-gratification decreased. To explain their findings, they cited two studies (Green, Myerson, Ostaszewski, 1999; Read & Read, 2004) that found age – which correlates positively with religiosity – was positively related to discounting. 19

Yet the majority of studies that examined these variables reported a negative, linear relationship between age and discounting. In other words, as people get older, they are usually better able to delay gratification. This inconsistency in findings calls Morgan et al.’s explanation into question (e.g., Green et al., 1996; Löckenhoff, O’Donoghue, &

Dunning, 2011; Reimers, Maylor, Stewart, & Chater, 2009; Scheres et al., 2006;

Steinberg et al., 2009).2 Thus, Morgan et al.’s results add to the current disagreement

regarding the relationship between religiosity and delay-of-gratification. Perhaps these

mixed effects are, in part, due to the manner in which researchers operationalize and

conceptualize religiosity.

Religiosity as a Two-Dimensional Construct

Traditionally, most researchers have operationalized religiosity as a single,

unidimensional construct (Boswell & Boswell-Ford, 2009; Gorsuch, 1984; Graham &

Haidt, 2010), despite that several other scholars have advocated for a multidimensional

structure (e.g., Atchley, 2002; Mindel & Vaughan, 1978; Zinnbauer & Pargament, 2005).

In 1902, William James wrote in his book The Varieties of Religious Experience, “We are

struck by one great partition which divides the religious field. On the one side of it lies

institutional, on the other personal religion” (1985). Several studies (such as those

discussed in the previous section) have treated disparate elements of religiosity (e.g.,

belief in God and church attendance) as interchangeable measures that yield similar

2 Morgan et al. (2016) incorrectly cited Green, Myerson, and Ostaszewski (1999) as having found that older individuals demonstrated stronger preferences for immediate gratification. In fact, Green, Myerson, and Ostaszewski reported the exact opposite finding: increases in age were related to greater delay-of- gratification. 20

effects. However, a great deal of evidence suggests treating religion as a two-

dimensional construct: with principal categories like organizational and personal

religiosity. Additionally, such dimensions may have distinct, or even opposite,

influences upon various behaviors (Preston & Ritter, 2013), including those related to

delay-of-gratification (Kim-Spoon et al., 2015). Therefore, researchers who do not

account for these distinct dimensions may end up with null, specious, and/or

contradictory results. In this section, we review theoretical and empirical literature

concerning various two-dimensional religiosity structures and outline elements subsumed

within the two superordinate categories. We then explain how the two dimensions relate

to one another.

There are several accounts and theories, each with their own subtle variations, that

essentially characterize the same two-dimensional structure of religiosity. Graham and

Haidt (2010), for example, distinguished between religious practice and belief (for a similar distinction, see Marshall, 2002). Religious practice, they explained, involves social interactions such as group ritual, collective worship, and community engagement.

The authors posited that religious practice reduces the salience of one’s self- representation such that he or she becomes increasingly selfless, and religious practice enhances one’s religious beliefs. In contrast, religious belief is more individualistic and involves personal prayer. Though Graham and Haidt acknowledged that religious beliefs have some beneficial properties, such as combating psychological threat (i.e., an anxious mental and physiological response characterized by a fear of potential harm), they argued that religious practices are responsible for most of the benefits associated with religion. 21

As such, Graham and Haidt criticized past research for focusing primarily on beliefs

while largely neglecting practices.3

Additionally, Cohen, Siegel, and Rozin (2003) made the distinction between religious practice and belief (they referred to “faith” and “belief” interchangeably throughout their article). To investigate whether religious groups value practice and belief differently, they conducted a series of studies in which they administered self-

report surveys to Jews and Protestants. They asked participants to determine the extent to

which each item indicated a person was religious (i.e., a good member of his or her

religion). Religious practice items included the following: attending religious services

regularly, reading religious texts of your religion regularly, not having sex outside of

marriage, observing religious requirements to give charity, and raising your children with

a religious background. In contrast, religious belief items included the following: belief

in God, belief that religion can answer more fundamental questions than science, belief in

an afterlife, belief in a soul, and belief that the events described in the religious texts of

your religion are literally true. Their results indicated that Jews valued practice more than they did beliefs, whereas Protestants considered beliefs to be more important than practices. Moreover, Jews rated beliefs as less important than did Protestants.

Again primarily juxtaposing Protestantism with Judaism, Cohen, Hall, Koenig, and Meador (2005) differentiated between social and individual religiosity (for a similar

3 Contrary to Graham and Haidt’s (2010) criticism, most of the work that has investigated religiosity and self-regulation has focused primarily upon religious practices (Chitwood, Weiss, & Leukefeld, 2008; McCullough & Willoughby, 2009). However, it may be the case that the majority of studies examining religion in domains other than self-regulation have mainly focused upon religious beliefs rather than practices. 22

distinction between collectivistic and individualistic religiosity, see Hommel et al., 2011).

These dimensions are analogous to those previously discussed; social religiosity is

similar to religious practice/experience and community, whereas individual religiosity

corresponds to personal beliefs and values. The authors noted that Jewish membership is

determined by descent (i.e., ancestry), whereas Protestant membership is based on assent

(i.e., endorsing a set of beliefs and values). This may explain why Jews and members of

other such descent religions (e.g., Hindus) often place greater importance on community

relationships and social practices such as (group) rituals and traditions. In comparison,

Protestants and members of most assent religions strongly emphasize personal beliefs,

values, and desires. To further illustrate these differences, Cohen and colleagues

discussed prosociality. Judaism encourages altruism but is relatively less concerned with

internal states. Hence, Jews and members of other socially-oriented religions focus more

on behavioral outcomes and community interactions than they do intrinsic motivations.

In many belief-based religions such as Protestantism, however, a person who is driven to

help others by selfish desires or feelings of guilt (rather than empathy, compassion,

and/or sympathy) would likely not be considered truly altruistic – he or she would only appear altruistic (Cohen at al., 2005). Thus, Protestants value individual internal beliefs and desires more than they do behavioral outcomes.

Expanding upon Cohen and colleagues’ prosociality example, Preston and Ritter

(2013) examined how different religion concepts facilitate altruism. They highlighted

inconsistencies in the literature regarding the effects of priming religion concepts on

prosocial behavior, and thus, examined how Religion and God concepts uniquely 23 contribute to prosocial behavior. Preston and Ritter posited that religion, rather than God, is closely related to religious practice, social responsibility, and ingroup cooperation.

Therefore, religion concepts should promote concern for one’s ingroup. By contrast, God concepts are strongly associated with personal belief, moral authority, and divine punishment. The researchers mentioned that God’s ability to punish seems to inspire impression management and public self-consciousness – that is, concern for how one appears to outgroup members (Gervais & Norenzayan, 2012a). In accordance with their hypotheses, Preston and Ritter demonstrated that participants primed with Religion [God] concepts were more charitable to ingroup [outgroup] members. Thus, religion and God concepts can exert opposite influences upon certain behaviors – such as those related to prosociality.

Importantly, these two dimensions (i.e., organizational and personal; practice and belief; social and personal; collectivistic and individualistic) are not necessarily opposites of one another. They are significantly related yet distinct enough to warrant examining them as separate entities (Boswell & Boswell-Ford, 2009; Kim-Spoon et al., 2015;

Worthington et al., 2003). Therefore, it would be inappropriate to define them as completely unique constructs. Even so, related dimensions can still engender opposite or contradictory influences upon certain mental processes and, in turn, behavioral outcomes.

As one example of how personal and organizational religiosity are related to one another, Boswell and Boswell-Ford (2009) conducted a confirmatory factor analysis to examine the link between what they referred to as “public” and “private” religiosity and spirituality. 221 participants filled out a Likert-type questionnaire in the lab and again 24

two years later. Public religiosity items involved attending services and participating in

activities. The researchers measured private religiosity using items related to personal

prayer, faith in God, and finding comfort in religion. Spirituality, however, contained

more abstract items (e.g., “spiritually touched by beauty of creation” and “feel deep inner

peace/harmony”). Results indicated the model provided a good fit to the data. The

researchers found strong factor loadings for public (.69 to .89) and private (.81 to .96) religiosity items. The loadings for spirituality (.65 to .74) were slightly less robust, presumably because the items (and, perhaps, the construct itself) were quite abstract.

Additionally, as predicted, Boswell and Boswell-Ford found that private, but not public,

religiosity predicted spirituality. More importantly, they found a significant moderate

correlation between public and private religiosity (r = .60). Kim-Spoon et al. (2015)

found a similar correlation between personal and organizational religiosity (r = .57).

Ritter and Preston (2013) provide additional evidence that organizational and personal

religiosity are related but categorically distinct. They asked participants to group

different religion words (e.g., faith, God, Bible, sermon, church, belief, faith, Heaven).

Across two studies, Ritter and Preston found that participants reliably categorized

organizational and personal religiosity words separately.

Additionally, Worthington and colleagues (2003) conducted a confirmatory factor

analysis upon their Religious Commitment Inventory (RCI-10), which many have used to

measure religiosity in myriad studies – including two correlational studies examining

delay-of-gratification (Carter et al., 2012; DeWall et al., 2014). Personal, private,

individualistic, and belief-based items (e.g., “My religious beliefs lie behind my whole 25 approach to life” and “It is important to me to spend periods of time in private religious thought and reflection”) loaded strongly on one factor. Organizational, public, collectivistic/social, and practice-based items (e.g., “I enjoy working in the activities of my religious organization” and “I enjoy spending time with others of my religious affiliation”) loaded on a separate factor. Again, personal and private religiosity factors

(or as Worthington and colleagues put it, intrapersonal and interpersonal religious commitment scales) were significantly related to one another (r = .72). Importantly, when comparing the one and two-factor models across two separate samples (one sample being predominantly Protestant and the other being religiously diverse), the two-factor model provided a better fit to the data. Despite these results, researchers very frequently sum items on the RCI-10 to generate a single composite score. Unfortunately, measuring and analyzing these separate factors as one omnibus construct is likely to result in a loss of information and specificity (Zinnbauer & Pargament, 2005). Taken together, these factor analysis studies provide strong evidence in support of a two-dimensional religiosity construct.

The Effects of Organizational vs. Personal Religiosity on Delay-of-Gratification

To our knowledge, only one study has examined the unique effects of organizational and personal religiosity dimensions on delay-of-gratification. Kim-Spoon and colleagues (2015) conducted a longitudinal study investigating whether delay-of- gratification mediated the relationship between religiosity and substance use in adolescents (aged 10 to 13 at the study’s onset). Organizational and personal religiosity were measured at time one. The researchers measured delay-of-gratification at time two 26

(approximately 2.4 years later) using an updated version of the Monetary Choice

Questionnaire (MCQ-27; Kirby, Petry, & Bickel, 1999). Kim-Spoon et al. measured

substance abuse at time two by averaging items regarding the frequency with which

participants reported using alcohol, cigarettes, and marijuana. There were no differences

due to demographic variables such as gender, ethnicity, and family income. After

controlling for age, results indicated no direct effect of organizational nor personal

religiosity upon substance use. However, there was an indirect effect of personal, but not

organizational, religiosity on substance abuse through delay-of-gratification.

Specifically, as personal religiosity increased, participants discounted rewards less (i.e.,

exhibited more delay-of-gratification).

Kim-Spoon and colleagues suggested that their results – specifically, the

influence of personal but not organizational religiosity on delay-of-gratification – might be due to shared variance between organizational and personal religiosity. Specifically, the zero-order correlation between organizational religiosity and delay-of-gratification

was nonsignificant (r = -.06); however, personal religiosity and delay-of-gratification

were significantly positively correlated with one another (r = .24). In their main

analyses, the researchers examined the impact of each religiosity dimension after

controlling for the other dimension’s influence. Therefore, if organizational religiosity is

even somewhat subsumed beneath a superordinate “personal religiosity” category, its

unique impact may have been obscured in Kim-Spoon et al.’s findings. In the following

sections, we discuss how certain factors might mediate the relationship between the two

religiosity dimensions and delay-of-gratification. 27

Cognitive Processing Systems as Possible Mediators

In this section, we begin by reviewing literature concerning how two cognitive processing systems (construal level and rational-experiential) might affect delay-of- gratification. Afterward, we discuss ways in which both organizational and personal religiosity dimensions might influence these cognitive processing systems.

Cognitive processing systems and delay-of-gratification. Our ability to mentally represent or imagine future events and outcomes allows us to make decisions now about prospective situations (Schacter & Addis, 2007; Wilson & Gilbert, 2005). For example, in choosing between $100 now and $120 in two months, one must rely on his or her ability to imagine what it might be like to receive this larger amount in the future. Trope and Liberman’s (2010) construal level theory (CLT) explains that increasing [decreasing] psychological distance (i.e., one’s subjective judgment of how far away an object is relative to him or herself in the present) encourages more high-level [low-level] mental representations and cognitive processing.4 Put differently, as objects become more psychologically distal, people tend to think of them in broader terms: forming increasingly abstract, global, general, decontextualized, and superordinate mental representations of said objects. By contrast, relatively proximal objects promote more concrete, local, detailed, contextualized, and subordinate mental representations (Trope &

Liberman, 2003). For example, Liberman and Förster (2009) demonstrated that

4 Trope and Liberman (2010) noted that psychological distance can refer to temporal, social, physical, or even hypothetical domains – though, for the purposes of the present study, we focus on the former two domains.

28

participants who were asked to imagine and write about their life tomorrow focused on

local details more than did those asked to imagine and write about their life in one year

from now. Additionally, the researchers found similar results when they manipulated

social distance (see also, Kim, Schnall, & White, 2013).

Very much related to CLT, Epstein’s (2003) cognitive-experiential self-theory

(CEST) also distinguishes between two information processing systems. The experiential

system processes information in concrete terms, whereas the rational system is better at

making sense of abstract ideas. Epstein’s experiential system is analogous to what

Kahneman referred to as System 1 (2015) in that both are affectively-oriented, intuitive,

preconscious, automatic, rapid, effortless, and minimally demanding of cognitive

resources. By contrast, the rational system resembles System 2; it is characterized by

non-affective, analytical, conscious, intentional, slow, effortful, and cognitively

demanding information processing. In essence, the experiential system primarily

processes affective information. The rational system, however, is non-affective and

instead focuses on logical and analytical processing.

Research has demonstrated that both low-level/high-level cognitive construal and experiential/rational thinking are related to delay-of-gratification (Frederick, 2005; Fujita

& Han, 2009; Malkoc, Zauberman, & Bettman, 2010; Trope & Liberman, 2000). Fujita,

Trope, Liberman, and Levin-Sagi (2006) manipulated construal level and asked participants how much they would be willing to pay for certain objects (e.g., a $100 restaurant gift card and a DVD player) now and in six months. Participants in the high- level condition discounted the value of these objects less than did those in the low-level 29

condition. Similarly, Dunn and Ashton-James (2008) manipulated experiential and

rational processing before administering Fujita et al.’s (2006) DD task. Results revealed

that participants in the rational condition reported significantly less discounting than did

those in the experiential condition. Thus, both high-level and rational thinking increase

delay-of-gratification relative to low-level and experiential thinking.

Cognitive construal level and two-dimensional religiosity. Because cognitive

construal is related to delay-of-gratification, it may mediate the relationship between

religiosity and delay-of-gratification. However, the nature of this mediation is unclear because there are competing explanations regarding how cognitive construal is related to both religiosity dimensions. For example, one could argue that organizational religiosity motivates lower-level cognitive processing more than does personal religiosity.

Specifically, religious practice involves no psychological distance; the activities one performs while practicing his or her religion occur in the present. Mental representations

of current experiences depend upon present sensory and perceptual experiences – thus,

they are more vividly and concretely represented in the mind relative to mental

representations of the future. By contrast, religious beliefs that involve supernatural

beings and other such counterintuitive concepts are likely more abstract by nature.

Furthermore, as discussed previously, religious belief might be a superordinate category

that encompasses religious practice. Therefore, given that low-level cognitive construal

predicts less delay-of-gratification, organizational [personal] religiosity may be negatively [positively] related to delay-of-gratification. 30

Alternatively, it may be that organizational, relative to personal, religiosity encourages more global or high-level cognitive construal. One possible reason is that the former involves interacting with more people than does the latter. Furthermore, religious practices often involve ritual behaviors that may have a vague, ambiguous, or unknown relationship to religious beliefs (Barrett, 2000; Marshall, 2002). Thus, participation in such activities may promote abstract, high-level cognitive processing. However, religious beliefs may conjure incredibly detailed and specific mental representations for those who subscribe to them (Norenzayan, Gervais, & Trzesniewski, 2012; Luhrmann,

Nusbaum, & Thisted, 2010; Monroe & Jankowski, 2016). To illustrate, a highly religious person may have a very vivid and detailed representation of, and relationship with, abstract concepts and figures such as God and Heaven. In such cases, religious belief might activate low-level cognitive processing (see Colzato et al., 2010). Recall that low- level cognitive construal predicts less delay-of-gratification. Therefore, according to these explanations (rather than those from the paragraph above), organizational

[personal] religiosity may be positively [negatively] related to delay-of-gratification.

Thus, it is unclear how cognitive construal is related to both religiosity dimensions and, in turn, delay-of-gratification. We believe it is more likely that organizational [personal] religiosity is negatively [positively] related to construal level. However, the competing explanations we present between these two paragraphs underscore the importance of examining how these two religiosity dimensions influence high-level versus low-level cognitive construal and, consequently, delay-of-gratification. 31

Few have directly examined how CEST relates to religiosity. However, Epstein

(1993) theorized that religion, in general, is a manifestation of the experiential system; specifically, he posited that concrete and symbolic thinking are representative of organizational religiosity. He further claimed that religion is better at communicating with the experiential, compared to the rational, system (Epstein, 1994). The small amount of research that has tested the relationship between religiosity and rational- experiential processing has either failed to distinguish between organizational and personal religiosity (e.g., Gervais & Norenzayan, 2012; Ritter, Preston, & Hernandez,

2014; Shenhav, Rand, & Greene, 2011) or has produced mixed results (e.g., Finley, Tang,

& Schmeichel, 2015; Pennycook, Cheyne, Koehler, & Fugelsang, 2013; Yonker, Edman,

Cresswell, & Barrett, 2016).

Deontological Thinking as a Possible Mediator

Another possible explanation for how personal and organizational religiosity differently affect delay-of-gratification is that these religiosity dimensions focus on different types of values. In their meta-analysis, Saroglou, Delpierre, and Dernelle

(2004) found that religious individuals (across several denominations) valued responsibility, conformity, and self-discipline. Furthermore, personal religiosity was negatively related to hedonism (i.e., sensation-seeking, indulgence, and pleasure) and self-direction (i.e., independence and freedom). Other studies have shown that religious people often consider belief in God necessary for being moral (Simpson, Piazza, & Rios,

2016) – suggesting that personal religiosity, in particular, may be tied to a motivation to

“do the right thing” (i.e., to act according to the moral values cited above). 32

One’s values and what he or she considers morally correct may impact delay-of- gratification. For instance, Piazza and Landy (2013) studied the relationship between religiosity and utilitarian versus deontological thinking. When thinking in utilitarian terms, individuals judge outcomes according to how much value they produce. However, the researchers noted that religious individuals are more resistant to this type of thinking than their non-religious counterparts, and this might be because they strictly evaluate information and goals according to God’s directives. In other words, religious individuals do not question divine authority, but instead faithfully and dogmatically comply with what they believe is God’s will (Nielsen, 1990; Sinnott-Armstrong, 2009). Because religious people feel they are obligated to behave according to God’s directives, they likely experience less goal conflict. Emmons, Cheung, and Tehrani (1998) found that those with a greater proportion of religious goals demonstrated less goal conflict than did those with fewer religious goals. To illustrate, rather than ponder the consequences of some morally ambiguous action (e.g., whether to murder someone who will eventually commit several murders), religious individuals need not experience such conflict; they simply act in accordance with what they believe are God’s wishes (i.e., “You shall not murder.” Exodus 20:1, New International Version). Indeed, religious beliefs, such as belief in God, are good predictors of deontological thinking (i.e., strictly following overall governing rules; Goodwin & Darley, 2008) as well as non-utilitarian thinking

(i.e., not considering the consequences when evaluating information, decisions, and other such criteria; Piazza & Landy, 2013). 33

Importantly, Piazza and Landy’s findings suggest that religious individuals are

more concerned with acting according to their moral values than with the utility

associated with said values’ consequences or outcomes. In terms of delay-of-

gratification, religious individuals may not take into account many of the important

decision factors such as tradeoffs between rewards and consequences. Instead, they

faithfully act according to the values stipulated within their religion (e.g., avoiding

temptation, practicing patience, valuing the future, suppressing hedonistic desires). Thus,

by not considering certain decision factors, religious individuals likely experience less goal conflict than their non-religious counterparts. However, Piazza and Landy assessed religiosity in general and did not distinguish between organizational and personal religiosity dimensions.

Research by Hommel et al. (2011) provides some insight into how organizational and personal religiosity may relate to deontological thinking. In their studies, they found

that Protestants were more likely – and Catholics were less likely – than atheists to regulate their behavior according to their values and goals. In particular, Protestants were better (and Catholics were worse) than atheists at employing top-down self-regulation.

However, neither Protestants nor Catholics differed from atheists regarding bottom-up self-regulation. The former type of self-regulation involves proactively regulating one’s behavior according to his or her attitudes, beliefs, and/or goals; the latter form of self- regulation involves directly inhibiting impulses that conflict with goal attainment. Given that, as described earlier, Protestants tend to be relatively belief-focused and Catholics tend to be relatively practice-focused, these findings suggest that personal religiosity may 34

increase delay-of-gratification more than would organizational religiosity. This is because the sanctified beliefs associated with personal religiosity seem to promote deontological thinking (e.g., acting based on what is stipulated by one’s religion – which often includes being patient and avoiding temptation) and reduce goal conflict by strengthening top-down self-regulation directives.

35

HYPOTHESES

Throughout our review of the literature, we discussed a great deal of research that

suggests organizational and personal religiosity dimensions are distinct yet related

constructs. Furthermore, these religiosity dimensions may sometimes lead to different

behavioral outcomes, such as those related to delay-of-gratification. Particularly,

personal religiosity is more closely associated with religious beliefs and moral values

than is organizational religiosity. Therefore, the former may more strongly encourage asceticism. Although the extant literature concerning the link between religiosity and delay-of-gratification is limited, what little research does exist seems to suggest that

personal, but not necessarily organizational, religiosity is positively related to delay-of-

gratification. By examining cognitive processing systems and deontological thinking

(which are related to delay-of-gratification and goal conflict, respectively), we may learn

more about the relationship between these two religiosity dimensions and delay-of-

gratification.

• Hypothesis 1a: Those in the personal religiosity condition will exhibit higher

delay-of-gratification (less DD) than will those in the control condition

• Hypothesis 1b: Those in the organizational religiosity condition will exhibit

lower delay-of-gratification compared to those in the control condition

• Hypothesis 1c: Those in the personal religiosity condition will exhibit higher

delay-of-gratification compared to those in the organizational religiosity

condition 36

• Hypothesis 2a: Cognitive construal level will mediate the relationship

between experimental condition (i.e., organizational religiosity, personal

religiosity, and control) and delay-of-gratification

• Hypothesis 2b: Rational-experiential processing will mediate the relationship

between experimental condition and delay-of-gratification

• Hypothesis 2c: Deontological thinking will mediate the relationship between

experimental condition and delay-of-gratification 37

METHODS

Participants and Design

We recruited undergraduate students from Ohio University using Sona Systems’

online psychology experiment system to participate in a between-subjects experiment.

The criteria for inclusion were as follows: be at least 18 years of age, have no difficulty

speaking or understanding English, and have no mental or cognitive disabilities. Those

who enrolled in the study received partial course credit as compensation for their

participation.

Procedure

Upon arriving to the lab, those who consented to participate in the study were randomly assigned to one of three conditions: organizational religiosity, personal religiosity, and control. According to their experimental condition, participants received a questionnaire meant to arouse personal or organizational religiosity concepts. Those in the control condition received no such religiosity questionnaire. Then, all participants completed a DD task to measure delay-of-gratification. After the DD task, participants completed a series of questionnaires meant to assess the following constructs: deontological thinking, construal level processing, rational-experiential thinking. We counterbalanced the order of these measures. Then, participants completed additional questionnaires concerning religiosity and demographics. Finally, we debriefed, compensated, and thanked participants. 38

Materials (see Appendix for all study materials)

Experimental manipulation questionnaire. We created two separate experimental

questionnaires, each containing five self-report items, to arouse thoughts related to either

organizational or personal religiosity. To generate organizational religiosity items, we

drew from Ritter and Preston’s (2013) research on religious prime words. Ritter and

Preston asked participants to categorize various religion words however they liked.

Across two studies, multidimensional scaling analyses demonstrated that participants

reliably categorized words in accordance with organizational and personal religiosity

dimensions. Thus, when generating questionnaire items meant to arouse organizational

religiosity concepts, we selected prime words that strongly mapped on to the

organizational religiosity dimension (e.g., clergy, sermon, ritual). Sample items include,

“How often do you attend church or church-related activities?” and “Do you think

religious scriptures (such as the Bible) should be interpreted literally?”

For the personal religiosity experimental questionnaire, we drew from Shariff and

Norenzayan’s (2007) scrambled sentence task (described above), which has been successful in activating personal religiosity concepts. Furthermore, the words that Shariff and Norenzayan chose for their scrambled sentence task (e.g., faith, God, sacred) correspond to Ritter and Preston’s categorization results. Using these words, we generated items meant to arouse personal religiosity concepts. Sample items include,

“Do you have faith that God exists?” and “Do you consider your religious beliefs to be sacred?” 39

Delay discounting. We operationalized delay-of-gratification as DD. To measure

DD, we employed the Monetary Choice Questionnaire (MCQ-27; Kirby, Petry, & Bickel,

1999). The MCQ-27 is a self-report measure containing 27 items. Like those DD tasks

described above, for each item, participants indicated whether they prefer some

objectively smaller reward now or larger reward later (e.g., seven days from now). The

measure includes three items with small, medium, and large magnitudes for nine different

k values (e.g., small: $34 now vs. $35 in 186 days, medium: $54 now vs. $55 in 117 days,

large: $78 now vs. $80 in 162 days; all three items correspond to k = 0.000158128). The

MCQ-27 is a valid and reliable measure of DD – exhibiting high construct validity (Kirby

& Finch, 2010; Kirby & Petry, 2004; Kirby, Petry, & Bickel, 1999; Duckworth &

Seligman, 2005) as well as strong test-retest reliability (Amlung & MacKillop, 2011;

Kirby, 2009).

Construal level. Several researchers have used the Behavioral Identification

Form (BIF; Vallacher & Wegner, 1989) to measure construal level (e.g., Fujita et al.,

2006; Liberman & Trope, 1998). During this task, participants read 25 short behavior phrases such as, “Locking the door.” After reading a behavior phrase, participants selected one of two choices to describe said behavior phrase. For this example, the choices are, “Putting a key in a lock,” and “Securing the house.” For each behavior phrase, one choice represents low-level construal while the other represents high-level construal. Higher scores indicate higher construal level. The BIF has strong test-retest reliability (two weeks; r = .91) and internal consistency (Cronbach’s α = .84); 40

furthermore, the BIF is significantly, though modestly, related to action planning and

self-regulation (Vallacher & Wegner, 1989).

Deontological thinking. Piazza and Landy (2013) developed the Morality

Founded on Divine Authority (MFDA) scale, which measures the extent to which

individuals think belief in God is necessary to be a moral person. The MFDA consists of

20 self-report items such as, “What is morally good and right is what God says is good

and right” and “Everything we need to know about living a moral life God has revealed

to us.” To reduce the risk of participant fatigue, we used Simpson, Piazza, and Rios’

(2016) abbreviated MFDA scale. Participants used a 9-point scale (where 1 means

“strongly disagree” and 9 means “strongly agree”) to indicate the extent to which they

agree with each statement. The shortened MFDA consists of five items and demonstrated

strong internal reliability (Cronbach’s α = .92; factor loadings between .626 - .929) and

goodness of fit (X2 (5, N = 293) = 7.68, p > .05).

Rational vs. experiential thinking. We included a measure of rational- experiential thinking to explore whether it mediates the relationship between experimental condition and DD. To measure rational-experiential thinking, we used

Pacini and Epstein’s (1999) Rational-Experiential Inventory (REI-40). This measure consists of 40 self-report items and contains two main scales: rational and experiential.

The following are sample items from the rational scale, “I am not good at figuring out complicated problems” (reverse-coded) and “I enjoy intellectual challenges.” Sample items for the experiential scale include, “I like to rely on my intuitive impressions” and “I think it is foolish to make important decisions based on feelings” (reverse-coded). Using 41 a five-point scale (where 1 means “definitely not true of myself” and 5 means “definitely true of myself.”), participants indicated the extent to which each item corresponds to themselves. Internal consistency for the rational and experiential scales are strong

(Cronbach’s α = .90 and .87, respectively). Furthermore, the REI-40 demonstrates high construct validity as it predicts decision-making behaviors such as heuristic processing and the impact of reward magnitude on response accuracy.

Organizational and personal religiosity. We included 14 self-report items to measure organizational and personal religiosity, and subsequently, examine the correlation between these dimensions and DD. To measure organizational religiosity, we generated items related to religious practice and community: “How often do you engage in religious prayer?” and “How close do you feel to others in your religious community?”

To measure personal religiosity, we developed items such as, “How much do you believe in God?” and “How often do you engage in personal religious prayer (i.e., praying in private without others present)?” Participants answered each item using various Likert- type and text-entry response formats – depending on the nature of the question.

Data Preparation

With the exception of our religiosity items (which we subjected to an exploratory structural equation model that provided religiosity factor scores), we computed arithmetic means of the items within each predictor measure to create composite scores. Finally, for our continuous dependent variable, delay-of-gratification, we calculated the geometric mean of MCQ-27 discount values using Kaplan, Lemley, Reed, and Jarmolowicz’ (2016) automated scoring system. Specifically, we used the program to calculate k discount 42

values for each participant. The geometric mean of k values was positively skewed (as is common with discounting measures). Therefore, we conducted a log transformation of these skewed k values in order to approximate a normal (Gaussian) distribution (for more information on MCQ-21 and MCQ-27 scoring, see Kaplan et al., 2016; Kirby, 2009;

Kirby, Petry, & Bickel, 1999; Kirby & Maraković, 1996).

43

RESULTS

Participants

172 undergraduates consented to participate in our experiment. Of these

participants, we excluded eight from the following statistical analyses for failing at least

one of three attention check items (e.g., “If you're paying attention, select ‘definitely true

of myself’”). Additionally, we excluded another eight participants for either selecting all

proximal or distal choice options on the MCQ-27. Although we acknowledge that these

participants may have strictly preferred the smaller/sooner or larger/later reward options

for all 27 items, we believe it is much more likely that they were indifferent toward

completing the task (and, perhaps, the entire study) honestly and accurately. Furthermore,

we conducted outlier analyses for MCQ-27 responses and excluded two participants

whose Cook’s D scores were at least .05 (i.e., more than four standard deviations from

the mean). Finally, the efficacy of a prime depends on its ability to activate stored

conceptual information associated with said prime. However, God and religion primes

likely activate different conceptual information for those who do and do not believe in

God. This may explain why such primes are ineffective for those who do not believe in

God (Dijksterhuis, Preston, Wegner, & Aarts, 2008; Shariff et al., 2016). Therefore, we

excluded 53 participants who did not report a belief in God. In sum, a total of 65 unique

participants were excluded from all of the following analyses – leaving a total of 107 participants (for descriptive statistics, see Table 1). 44

Table 1 Descriptive Statistics for Demographics Variables (N = 107) Variable M SD Age (18-24) 19.19 1.05 N % Gender Male 38 35.5 Female 69 64.5 Race White/European American 86 80.4 Black/African American 10 9.3 Asian American or Pacific Islander 4 3.7 Other race 7 6.6 Religion Christian-Catholic 51 47.7 Christian-Protestant 20 18.7 Christian-Other 26 24.3 Other religious affiliation 10 9.3

Main Analyses

Hypotheses 1a, 1b, and 1c. We hypothesized that those in the personal religiosity condition would exhibit greater delay-of-gratification than would those in control condition (hypothesis 1a). We further hypothesized that those in the organizational religiosity condition would exhibit lower delay-of-gratification compared to those in the control condition (hypothesis 1b). Finally, we hypothesized that those in the personal religiosity condition would exhibit greater delay-of-gratification compared to those in the organizational condition (hypothesis 1c). To examine these hypotheses, we conducted a one-way analysis of variance (ANOVA) using IBM SPSS Statistics version 25.

Specifically, we entered experimental condition as the independent variable and the log- transformed geometric mean of discount rates (DD) as the dependent variable. Results 45

indicated a main effect of experimental condition on DD, F(2, 104) = 3.12, p < .05, R2 =

.057. To understand the nature of this main effect, we conducted all possible pairwise

comparisons. In accordance with hypothesis 1a, individuals in the personal religiosity

condition exhibited less DD than did those in the control condition, t(106) = -2.07, p <

.05, = .040. However, contrary to hypothesis 1b, DD did not differ among individuals 2 𝑝𝑝 in the𝜂𝜂 organizational religiosity and control conditions, t(106) = 0.25, p > .05, = .001. 2 𝑝𝑝 For hypothesis 1c, individuals in the personal religiosity condition exhibited le𝜂𝜂ss DD than

did those in the organizational religiosity condition, t(106) = -2.25, p < .05, = .046 2 𝑝𝑝 (for means and standard deviations of study variables, see Table 2). These results𝜂𝜂 suggest

that activating personal, but not necessarily organizational, religiosity concepts influences

delay-of-gratification behavior (among those who believe in God). Specifically, priming

individuals with personal religiosity concepts increased the extent to which they delayed

gratification (in favor of objectively larger rewards).

Table 2 Means and Standard Deviations for Measurement Variables (N = 107) Personal Cond. Organizational Control Cond. Total Sample Variables (N = 35) Cond. (N = 34) (N = 38) (N=107)

M SD M SD M SD M SD α Construal 13.86 5.05 14.85 6.18 15.26 5.09 14.67 5.43 .82 Level Experiential 3.52 0.39 3.56 0.40 3.48 0.41 3.52 0.40 .75 Processing Rational 3.57 0.46 3.61 0.65 3.70 0.53 3.63 0.55 .86 Processing Deontological 5.16 2.00 5.50 1.91 5.25 1.72 5.30 1.87 .86 Thinking Delay -2.05 0.54 -1.76 0.54 -1.79 0.52 -1.87 0.54 .87 Discounting 46

Hypotheses 2a, 2b, and 2c. To understand the mechanism(s) underlying the

relationship between religiosity and delay-of-gratification, we examined whether

cognitive construal, rational-experiential processing, or deontological thinking mediated

the relationship between experimental condition and DD (for correlations among study

variables, see Table 3). Using the PROCESS macro version 2.16.2 (Hayes, 2016) for

SPSS, we conducted a mediation analysis in which we entered experimental condition as

a multicategorical independent variable (Hayes & Preacher, 2014). We dummy coded experimental condition such that the control condition was the reference group. First, the omnibus model, containing experimental condition and all potential mediators, was significant, F(6, 100) = 2.67, p < .05, R2 = .14. Furthermore, rational processing was negatively related to DD, β = -0.25, t(106) = -2.63, p < .01, = .07. However, 2 𝑝𝑝 experimental condition was not related to rational processing,𝜂𝜂 p > .05. None of the other

mediators predicted DD, all ps > .05. Even so, we examined the indirect effect of each mediator independently using a bootstrap approach, with 5000 samples, for bias- corrected confidence intervals. All analyses yielded confidence intervals that contained zero and, thus, were nonsignificant, ps > .05 (see Table 4). Taken together, these results do not support hypotheses 2a, 2b, or 2c. Cognitive construal level, rational-experiential processing, and deontological thinking do not seem to explain the relationship between religiosity and delay-of-gratification.

47

Table 3 Zero-Order Correlations for Study Variables (N = 107) 1 2 3 4 5 6 7 1 Personal Cond.a ----- 2 Organizational Cond.a -.48** ----- 3 Construal Level -.11 .02 ----- 4 Experiential Processing .01 .08 -.10 ----- 5 Rational Processing -.07 -.03 -.08 .23* ----- 6 Deontological Thinking -.05 .07 .14 .03 .00 ----- 7 Delay Discounting -.24* .13 -.07 -.10 -.24* -.03 ----- a Dummy coded such that the control condition is the reference group *p < .05 **p < .01

Table 4 Relative Indirect Effects of Potential Mediators for the Relationship between Omnibus Experimental Condition and Delay Discounting (N=107) Potential Mediators B SE CI [LL – UL] Construal Level .0001 .0004 -.0002 – .0018 Experiential Processing .0001 .0007 -.0004 – .0035 Rational Processing .0022 .0071 -.0051 – .0092 Deontological Thinking .0008 .0036 -.0015 – .0180 Note: we entered experimental condition into the model as multicategorical independent variable. All pairwise comparisons of experimental condition on delay discounting through the mediators were nonsignificant. *p < .05

Exploratory Analyses

We were interested in exploring the latent factor structure of our religiosity items as well as the relationship between the emergent factors and DD. Recall that we found support for hypothesis 1a (i.e., personal religiosity increases delay-of-gratification). We wanted to test whether our religiosity items could provide additional, correlational support for this relationship. Thus, we used Mplus version 8.0 statistical modeling 48 software (Muthén & Muthén, 2018) to conduct exploratory structural equation model

(ESEM) analyses.

ESEM is a relatively new statistical technique that incorporates elements of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Specifically, the main advantages to ESEM are that it enables one to conduct CFA-like model comparisons and simultaneously allows EFA-like cross-loading. This latter advantage is especially important; CFAs fix all cross-loadings to zero which may forsake variability, bias estimates, and oversimplify models. However, because ESEM allows indicators to simultaneously load onto all factors, it corrects for these weaknesses and, in doing so, increases model validity. As such, many studies support ESEM’s superiority over both

EFA and CFA (e.g., Asparouhov & Muthén, 2009; Howard, Gagné, Morin, & Forest,

2016; Tóth-Király, Bõthe, Rigó, & Orosz, 2017).

We entered 11 religiosity items from our questionnaire (for ESEM items, see

Table 5) into the models. We did not include items 1, 3, and 6 from our religiosity questionnaire in these analyses because they were redundant, categorical rather than quantitative, and contained missing data, respectively (for items 1, 3, and 6, see the religiosity questionnaire in the Appendix). We allowed all 11 items to load freely across factors. Additionally, we specified paths between factors and DD. We compared one, two, three, and four-factor model solutions. There was no missing data for these analyses. Additionally, because we anticipated that factors would be highly correlated with one another, we subjected factors to oblique geomin rotation. To adjust for the presence of skewed and/or kurtotic data, we used maximum likelihood estimation with 49 robust standard errors (MLR). To examine and compare model fit, we used Satorra-

Bentler chi-square test, which complements MLR estimation when there is no missing data.

Table 5 Organizational and Personal Religiosity Items for Exploratory Analyses # Item 1 How often do you engage in group religious prayer? 2 How often do you attend religious services? 3 How often do you take part in religious activities? 4 How often do you engage in personal religious prayer? 5 How much do you believe in God? 6 How religious are you? 7 To what extent do you feel a sense of belonging in your religious community? 8 To what extent do you and those in your religious community have the same values? 9 To what extent do people in your religious community offer you social support? 10 To what extent do you and those in your religious community have the same goals? 11 How close do you feel to people in your religious community?

Satorra-Bentler chi-square tests indicated that the one and two-factor models did

not provide a good fit to the data ps < .001. By contrast, the three and four-factor models

2 2 provided a good fit to the data, respectively, χ SB (33, N = 107) = 40.13, p > .05; χ SB (24,

N = 107) = 21.11, p > .05. However, both the three and four-factor models yielded the same warning. The latent variable covariance matrices were not positive definite.5 This

5 A covariance matrix that is not positive definite is problematic because the parameter estimation procedure will involve inverting the matrix and dividing by a determinant whose value is zero – resulting in a mathematically undefined result. 50 was because the item, “How close do you feel to people in your religious community?”, correlated with one of the factors at a value greater than one, which likely means the item was highly correlated with several of the other items in the model (for inter-item correlations, see Table 6). We, therefore, decided to remove it and rerun the analysis.

Doing so resolved the issue for the three-factor model. The Satorra-Bentler chi-square

2 test, again, indicated that this model provided a good fit to the data, χ SB (25, N = 107) =

2 22.93, p > .05. Furthermore, removing this item significantly improved model fit, Δχ SB

(8, N = 107) = 23.64, p < .05. However, after omitting the problematic item, the four- factor model failed to converge: the chi-square test statistic was negative. As a result, we restricted further analyses to the three-factor model. We examined modification indices, which suggested we correlate the residual errors of items, “How religious are you?” and

“To what extent do you feel a sense of belonging in your religious community?”. This correlation was marginally significant, r = -.61, p < .10. However, after making this final

2 adjustment, the three-factor model provided a good fit to the data, χ SB (24, N = 107) =

11.50, p > .05. Additional fit indices were consistent with this result, RMSEA = .00, CFI

= 1.0, SRMR = .02 (for all nested model comparisons, see Table 7).6 Thus, we accepted this three-factor model as the best-fitting model (see Figure 1). Overall, the model explained 10.3% of the total variance in DD.

6 Hu and Bentler’s (1999) suggest the following cutoff values for additional model fit indices: the Comparative Fit Index (CFI) value greater than or equal to .95, the root mean squared error of approximation (RMSEA) value less than or equal to .06, and the standardized root mean square residual (SRMR) value less than or equal to .06. 51

Table 6 Zero-Order Inter-item Correlations for Organizational and Personal Religiosity Items (N = 107) Items 1 2 3 4 5 6 7 8 9 10 11 1 ----- 2 .72 ----- 3 .67 .66 ----- 4 .56 .54 .55 ----- 5 .47 .45 .43 .54 ----- 6 .60 .62 .58 .63 .56 ----- 7 .57 .56 .55 .51 .51 .56 ----- 8 .46 .43 .44 .35 .42 .56 .63 ----- 9 .46 .48 .46 .40 .29 .57 .57 .46 ----- 10 .57 .56 .55 .44 .43 .63 .62 .56 .56 ----- 11 .55 .60 .64 .47 .33 .60 .72 .59 .70 .68 ----- Note: item numbers correspond to those reported in Table 5. All ps < .01

Table 7 Exploratory Structural Equation Model of Organizational and Personal Religiosity Items on Delay Discounting: Nested Model Comparisons (N = 107) 2 2 Model χ SB df RMSEA CFI SRMR Δχ SB One-Factor 124.94 54 .11 .90 .06 ------Two-Factor 71.00 43 .09 .94 .04 31.97* Three-Factora 40.13 33 .07 .97 .03 24.52* Three-Factorb 22.93 25 .07 .99 .02 24.15* Three-Factorbc 11.51 24 .00 1.00 .02 29.56* Four-Factora 21.11 24 .03 1.00 .02 ------Note: df = degrees of freedom, RMSEA = Root Mean Square Error of Approximation, CFI = Comparative Fit Index, SRMR = Standardized Root Mean Square Residual. We accepted the three-factorbc model as the best-fitting model. a Model did not have positive definite covariance matrix b Model did not include item 11 c Model with correlated residual errors for items 6 and 7 *p < .01

52

Figure 1. Best-fitting three-factor exploratory structural equation model of organizational and personal religiosity items on delay discounting (DD). Note: item numbers correspond to those reported in Table 5. Bold paths are significant at p < .05; *p < .10

The factor structure for the three-factor model is largely consistent with that which we theorized throughout this paper (for standardized path estimates, see Table 8).

Although we suggested that religiosity be conceptualized as a two-dimensional construct, our results suggest a three-factor solution in which organizational religiosity is 53

partitioned into practice and community subdimensions. Specifically, it seems that factor

one contains organizational practice items, factor two consists of personal religiosity

items, and factor three includes organizational community items. As expected, these

factors are highly correlated with one another: factor one and factor two, r = .73, p <

.001; factor one and factor three, r = .71, p < .001; factor two and factor three, r = .52, p <

.05. Thus, as we discussed earlier in this paper, these dimensions are highly related to

one another as they are part of the same overall construct. However, while highly

correlated with one another, these factors are distinct enough that they relate differently

to delay-of-gratification. Factors one and three were not significantly related to DD, ps >

.05. By contrast, the relationship between factor two and DD was marginally significant,

β = -.46, Z = -1.74, p < .10. Taken together, these results provide support for our theory

regarding the dimensional structure of religiosity. In particular, our religiosity items

loaded onto factors that resemble organizational practice, organizational community, and

personal religiosity. Furthermore, the path from factor two (i.e., personal religiosity) to

DD was marginally significant – thereby providing further support for hypothesis 1a (i.e., personal religiosity increases delay-of-gratification).

54

Table 8 Standardized Path Estimates and Standard Errors for Best-Fitting Three-Factor Exploratory Structural Equation Model of Organizational and Personal Religiosity Items on Delay Discounting (N = 107) Variables Factor 1 Factor 2 Factor 3 1 .87* (.14) .00 (.08) -.03 (.11) 2 .84* (.11) .00 (.09) .01 (.05) 3 .72* (.19) .06 (.14) .04 (.14) 4 .16 (.36) .69* (.35) .00 (.01) 5 -.02 (.06) .55* (.21) .23 (.26) 6 .03 (.22) .44 (.27) .52* (.20) 7 -.01 (.07) .23 (.30) .72* (.20) 8 -.01 (.18) .04 (.30) .71* (.22) 9 .20 (.25) -.05 (.30) .58* (.23) 10 .29 (.22) .01 (.03) .53* (.20) 11 ------

Delay Discounting .24 (.31) -.46† (.26) .05 (.22) Note: item 11 was not included in this model to achieve a positive definite covariance matrix

55

DISCUSSION

Main Analyses

The results of the present study suggest that organizational and personal

religiosity dimensions are distinct – at least in that they relate to delay-of-gratification

differently. Through our experiment, we found preliminary evidence that activating

personal religiosity concepts increases one’s ability or tendency to delay gratification (for

individuals who believe in God). By contrast, activating organizational religiosity

concepts does not influence delay-of-gratification (again, for those who believe in God).

Here, we find support for our assertion that religiosity not be treated as a unidimensional

construct. Therefore, it seems likely that personal, rather than organizational, religiosity

elements are responsible for the (previously established) positive relationship between

religiosity and delay-of-gratification. It is not necessarily one’s religious practices,

experiences, or community relationships that influence his or her delay-of-gratification.

Instead, it seems that one’s religious beliefs, values, and personal relationship with God

impact his or her ability or tendency to delay gratification.

From these results, it seems unlikely that simply increasing organizational

religiosity will lead to improved delay-of-gratification. This is not to say that increasing organizational religiosity will not lead to improved delay-of-gratification but that any

such improvements are likely the result of organizational religiosity’s close relationship

with personal religiosity. Recall that our best-fitting structural equation model yielded religiosity factors highly correlated with one another – as expected and in accordance with previous research. Therefore, if increasing religious attendance, group prayer, or the 56

frequency of religious activities leads to improved delay-of-gratification, it is likely

because these behaviors also strengthen one’s religious beliefs, values, and personal

relationship with God.

Although our study showed no relationship between organizational religiosity and

delay-of-gratification, there may still be situations in which this relationship exists.

Individuals’ religious community might provide external support and/or facilitate

reputation concerns that positively influence self-regulation behaviors such as delay-of- gratification. Indeed, a great deal of research supports the positive impact of interpersonal relationships and social support on self-regulation and goal pursuit (e.g.,

Bandura, 1991; Fitzsimons & Finkel, 2010). For example, one’s religious community

may operate like most support groups (e.g., alcoholics anonymous, gamblers anonymous,

and anger management). Such groups are successful, in part, because they provide social

support (Anderson, Winett, Wajcik, 2007; Kaskutas, Bond, & Humphreys, 2002). Group

members highlight potential problems and help one another to understand, combat,

and/or prevent said problems. Similarly, religious community members may make each

other aware of situations in which they may be compelled to behave impulsively or

impatiently. For example, one’s fellow Christians may admonish premarital sex and

discuss ways to delay that gratification – thereby avoiding sinful behavior – in favor of

some larger reward later (e.g., God’s approval and entrance into Heaven). With regard to

reputation concerns, people may worry that if they (continually) fail to delay

gratification, they may do damage to their reputation within their community. Therefore,

to avoid being ostracized within or excluded from their community, they act in 57

accordance with their community’s shared values and goals (Sosis, 2003, 2005). For

religious individuals, this may mean delaying gratification: consequently, demonstrating

shared values such as patience and asceticism.

However, again, our preliminary results do not support these theories. It is worth

noting that, while not statistically significant, those in the organizational religiosity

condition actually tended to delay gratification less than did those in the control condition

(whereas those in the personal religiosity condition delayed gratification more than did

those in both the organizational religiosity and control conditions). Thus, it is possible

that organizational religiosity negatively affects delay-of-gratification – though, again, we did not find statistical support for this theory in the present study. Similarly, we did not test the effects of reputation concerns directly, yet it seems that religious individuals are more concerned with God’s judgement than they are with that of their fellow religious community members.

The present study also examined potential mediators that might explain the nature of the relationship between religiosity and delay-of-gratification. Specifically, we examined whether construal level, rational-experiential processing, or deontological thinking mediated the relationship between experimental condition and DD. We found that none of the potential mediators explain the relationship between experimental condition and DD. In sum, we still do not have an empirical explanation for why personal, rather than organizational, religiosity concepts influence delay-of-gratification.

However, due to study limitations (which we discuss below), we hesitate to draw

definitive conclusions regarding the null effects of these mediators. We believe it is 58

necessary to conduct further tests before confidently concluding that they do not, at least

partially, explain why organizational and personal religiosity dimensions impact delay- of-gratification differently. Additionally, however, it may be prudent to consider alternative mediators. We wonder if individuals’ beliefs about what characteristics and/or behaviors God values may be responsible for the relationship between personal religiosity and delay-of-gratification. For example, perhaps individuals differ in the extent to which they believe God values patience, self-discipline, and asceticism. It is possible that differences in organizational and personal religiosity relate to different beliefs about what God considers to be the morally correct behavior. Instead or in addition, researchers may want to examine time perception. A limited amount of previous research has found a link between religiosity and time perception (e.g., Öner-

Özkan, 2007; Zimbardo & Boyd, 1999). For example, Carter and colleagues (2012) found that time perception partially mediated the relationship between religiosity and delay-of-gratification. However, this research was correlational and did not distinguish between different types of religiosity. Perhaps organizational and personal religiosity relate to time perception differently. These alternative mediators may help explain the difference between these religiosity dimensions as they relate to delay-of-gratification.

Exploratory Analyses

Our exploratory analyses provide additional, correlational support for our

experimental findings. The best-fitting structural equation model yielded three

discernable factors: organizational practice, organizational community, and personal

religiosity. Of these factors, neither organizational practice nor organizational 59 community was related to DD. However, the personal religiosity factor was marginally related to DD. Therefore, it seems that personal, rather than organizational (practice and community), religiosity is related to delay-of-gratification (again, among those who believe in God). These exploratory findings not only bolster those of our experiment but also have important theoretical and methodological implications for the study of religion.

Because the three-factor model provided the best fit to the data (and the one and two- factor models did not provide a good fit to the data), we have additional evidence against conceptualizing and operationalizing religiosity as a unidimensional construct. Instead, our findings suggest researchers conceptualize and measure religiosity as a multidimensional construct consisting mainly of organizational and personal dimensions.

Importantly, these findings advocate dividing the organizational religiosity dimension into two subdimensions: practice and community.

Limitations and Future Directions

It is important to note that this study has a number of limitations, which should inform future directions. Participant sign-ups in the spring semester were uncharacteristically low compared to those from previous years. This, together with the fact that we excluded many participants who did not believe in God, reduced our sample size and negatively affected our statistical power.7 We conducted a post-hoc power analysis for the ANOVA test of experimental condition on DD (hypotheses 1a, 1b, and

1c) – setting alpha equal to .05. Results indicated an observed statistical power of .59.

7 Statistical power is the probability of rejecting the null hypothesis given that the alternative hypothesis is true. In other words, it is the probability of detecting a significant effect given that one truly exists. As statistical power increases, the probability of committing a type II error (false positive) decreases. 60

For our exploratory analyses, we conducted post-hoc power analysis of the best-fitting model using the test of not close fit. Again, we set alpha equal to .05 and used .05 and

.01 as our null and alternative RMSEA hypotheses, respectively. Results indicated an observed statistical power of .18. Both of these power values are below the conventional threshold of .80 (Cohen, 1988). Because our analyses were underpowered, we may not have been able to detect certain true effects. For instance, and as mentioned above, it may be the case that organizational religiosity negatively influences delay-of- gratification. Although our results trended in this direction, our analyses did not yield statistical significance. As such, future studies should make a point to recruit more participants (who believe in God) – thereby increasing statistical power.

Another important issue to consider is the validity and efficacy of our experimental manipulation. We presented participants with one of two questionnaires meant to prime organizational or personal religiosity concepts. The questions therein contained words that previous research found were associated with these religiosity concepts. For example, Ritter and Preston (2013) found that the word “sermon” was most closely associated with organizational, rather than personal, religiosity. The opposite was true for the word “faith.” Even though we based our experimental manipulation on these previously established findings, we cannot be sure that our manipulation indeed activated organizational religiosity concepts. Furthermore, while we found that those in the organizational and personal religiosity conditions differed from one another in DD, those in the organizational religiosity condition did not discount more or less than did those in the control condition. Thus, our experimental manipulation may 61 not have effectively activated organizational religiosity concepts in the minds of our participants. Put differently, the questions meant to prime participants with organizational religiosity concepts may not have been strong enough such that we could detect meaningful differences between our experimental conditions. By contrast, it may be that priming personal religiosity concepts is simply more effective than is priming organizational religiosity concepts. Nevertheless, in the future, we should make a concerted effort to validate our experimental manipulation. Maybe, in a pilot study, we could conduct a manipulation check. For example, we could ask participants to complete an organizational and personal religiosity questionnaire immediately after the experimental manipulation. In doing so, we could attribute any differences in organizational and personal religiosity to the experimental manipulation – thus providing some evidence of the manipulation’s validity.

Additionally, future studies should consider alternative experimental manipulation methods both for the sake of replicating the present study’s findings with different methodology and perhaps more effectively differentiating between these religiosity dimensions. As one example, in the future, researchers might try asking participants to reflect on and write about their organizational or personal religiosity experiences.

Furthermore, our exploratory results suggest that the organizational religiosity dimension should be portioned into two subdimensions. As such, future studies should take this into account and design experimental manipulations that are sensitive to the distinction between religious practice and community. Perhaps making these adjustments will lead 62

to improvements both in terms of validity and efficacy compared to the present study’s

experimental manipulation method.

Similarly, if our experimental manipulation was limited in its strength or efficacy,

this might explain why we were unable to detect significant mediation effects. Our organizational and personal religiosity primes may not have activated these concepts long enough to influence performance on our mediation measures. Simply put, our priming effects may have worn off before we were able to measure construal level, rational- experiential processing, and deontological thinking. Thus, as mentioned above, it may be premature to conclude that these constructs do not explain, at least in part, the process by which organizational and personal religiosity influence delay-of-gratification. We recommend future studies, in addition to validating and strengthening experimental manipulations, consider alternative ways to measure these possible mediators. For instance, it may be better to use shorter measures of these constructs – thus, minimizing the time between experimental manipulation and mediator measurement. Doing so may prevent the effects of some experimental manipulation from wearing off before researchers are able to collect process data.

Finally, though not necessarily a limitation, future research on the topic of religiosity and delay-of-gratification would likely benefit from measuring DD differently.

The present study used a self-report measure and examined hypothetical monetary rewards. While previous research suggests there is no difference between measuring DD with hypothetical and real rewards (Johnson & Bickel, 2002; Madden, Begotka, Raiff,

Kastern, 2003), future studies should still consider employing behavioral, rather than self- 63

report, measures. Doing so would likely increase the construct, external, and ecological

validity of these findings. Similarly, future studies should attempt to examine different

forms of DD. We focused on discounting monetary rewards; however, various other

forms of DD exist. In particular, individuals are capable of subjectively discounting the

value of non-monetary rewards such as those involving time (Okada & Hoch, 2004),

experiences (Loewenstein, 1987; Shu & Gneezy, 2010), social interactions (Albrecht,

Abeler, Weber, & Falk, 2014; Izuma, Saita, & Sadato, 2008), and tangible objects (Estle,

Green, Myerson, & Holt, 2007; Jeffrey & Adomdza, 2010). Furthermore, these studies

have shown that individuals tend to discount these various types of rewards differently.

This may be because different reward types vary on a number of important dimensions

including but not limited to scarcity and abstraction. To illustrate, individuals may (in

some cases) consider time rewards (e.g., a short break from work) to be more abundant

and abstract compared to tangible rewards (e.g., a state-of-the-art television) or those involving experiences (e.g., attending a concert). To the extent that individuals perceive that organizational and personal religiosity elements differ in their scarcity and abstraction, said elements may lead to different outcomes according to the type of reward in question.

Conclusion

The relatively small amount of extant literature concerning religiosity and self-

regulation suggests that religiosity, in general, is positively related to delay-of-

gratification. However, until the present study, researchers had not determined the causal

direction of this relationship. Moreover, few studies have examined how different 64

elements of religiosity relate to delay-of-gratification. We developed the present study to

fill these gaps in the literature. Here, we provide preliminary evidence supporting the

assertion that religiosity not be treated as a unidimensional construct, but instead, one

consisting primarily of organizational and personal dimensions. Exploratory analyses

advocate dividing the former dimension into community and practice subdimensions.

Perhaps most importantly, the present study provides experimental evidence that

personal, but not organizational, religiosity is responsible for increasing delay-of-

gratification (among those who believe in God). Therefore, when considering ways to

improve delay-of-gratification, individuals may want to focus their efforts on

strengthening their personal religiosity. In sum, it is not religious practice, community,

or experience but instead, religious belief, faith, and values that impact delay-of- gratification – at least for those who believe in God.

65

REFERENCES

Abrams, D., & Hogg, M. A. (1988). Comments on the motivational status of self‐esteem

in social identity and intergroup discrimination. European Journal of Social

Psychology, 18(4), 317-334.

Ainslie, G. (1975). Specious reward: a behavioral theory of impulsiveness and impulse

control. Psychological bulletin, 82(4), 463-496.

Albrecht, K., Abeler, J., Weber, B., & Falk, A. (2014). The brain correlates of the effects

of monetary and verbal rewards on intrinsic motivation. Frontiers in

neuroscience, 8, 303.

Alessi, S. M., & Petry, N. M. (2003). Pathological gambling severity is associated with

impulsivity in a delay discounting procedure. Behavioural Processes, 64(3), 345-

354.

Amlung, M., & MacKillop, J. (2011). Delayed reward discounting and alcohol misuse:

the roles of response consistency and reward magnitude. Journal of experimental

psychopathology, 2(3), 418-431.

Anderson, E. S., Winett, R. A., & Wojcik, J. R. (2007). Self-regulation, self-efficacy,

outcome expectations, and social support: social cognitive theory and nutrition

behavior. Annals of behavioral medicine, 34(3), 304-312.

Angeletos, G. M., Laibson, D., Repetto, A., Tobacman, J., & Weinberg, S. (2001). The

hyperbolic consumption model: Calibration, simulation, and empirical

evaluation. The Journal of Economic Perspectives, 15(3), 47-68. 66

Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation

modeling. Structural equation modeling: a multidisciplinary journal, 16(3), 397-

438.

Atchley, R. C. (2002). The subjective importance of being religious and its effect on

health and morale. Journal of Aging studies, 11, 131–152.

Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational behavior

and human decision processes, 50(2), 248-287.

Barrett, J. L. (2000). Exploring the natural foundations of religion. Trends in cognitive

sciences, 4(1), 29-34.

Baumeister, R. F., & Vohs, K. D. (2004). Self-regulation. In C. Peterson & M. E. P.

Seligman (Eds.), Character strengths and virtues: A handbook and classification

(pp. 499–516). Washington/New York: American Psychological

Association/Oxford Press.

Benjamin, Daniel J., James J. Choi, and Geoffrey Fisher (2016). “Religious identity and

economic behavior.” Review of Economics and Statistics, 98(4), 617–637.

Bickel, W. K., Miller, M. L., Yi, R., Kowal, B. P., Lindquist, D. M., & Pitcock, J. A.

(2007). Behavioral and neuroeconomics of drug addiction: competing neural

systems and temporal discounting processes. Drug and alcohol dependence, 90,

S85-S91.

Bjork, J. M., Hommer, D. W., Grant, S. J., & Danube, C. (2004). Impulsivity in abstinent

alcohol-dependent patients: relation to control subjects and type 1–/type 2–like

traits. Alcohol, 34(2), 133-150. 67

Boswell, G. E., & Boswell-Ford, K. C. (2010). Testing a SEM model of two religious

concepts and experiential spirituality. Journal of religion and health, 49(2), 200-

211.

Carter, E. C., McCullough, M. E., Kim-Spoon, J., Corrales, C., & Blake, A. (2012).

Religious people discount the future less. Evolution and Human Behavior, 33(3),

224-231.

Chabris, C. F., Laibson, D., Morris, C. L., Schuldt, J. P., & Taubinsky, D. (2008).

Individual laboratory-measured discount rates predict field behavior. Journal of

risk and uncertainty, 37(2-3), 237-269.

Chitwood, D. D., Weiss, M. L., & Leukefeld, C. G. (2008). A systematic review of recent

literature on religiosity and substance use. Journal of Drug Issues, 38(3), 653-

688.

Cohen, J. (1988). Statistical power for the social sciences. Hillsdale, NJ: Laurence

Erlbaum and Associates, 98-101.

Cohen, A. B., Hall, D. E., Koenig, H. G., & Meador, K. G. (2005). Social versus

individual motivation: Implications for normative definitions of religious

orientation. Personality and Social Psychology Review, 9(1), 48-61.

Cohen, A. B., Siegel, J. I., & Rozin, P. (2003). Faith versus practice: Different bases for

religiosity judgments by Jews and Protestants. European Journal of Social

Psychology, 33(2), 287-295. 68

Colzato, L. S., van Beest, I., van den Wildenberg, W. P., Scorolli, C., Dorchin, S.,

Meiran, N., ... & Hommel, B. (2010). God: Do I have your

attention?. Cognition, 117(1), 87-94.

DeWall, C. N., Pond Jr, R. S., Carter, E. C., McCullough, M. E., Lambert, N. M.,

Fincham, F. D., & Nezlek, J. B. (2014). Explaining the relationship between

religiousness and substance use: Self-control matters. Journal of personality and

social psychology, 107(2), 339-351.

Dijksterhuis, A., Preston, J., Wegner, D. M., & Aarts, H. (2008). Effects of subliminal

priming of self and God on self-attribution of authorship for events. Journal of

experimental social psychology, 44(1), 2-9.

Duckworth, A. L., & Seligman, M. E. (2005). Self-discipline outdoes IQ in predicting

academic performance of adolescents. Psychological science, 16(12), 939-944.

Dunn, E. W., & Ashton-James, C. (2008). On emotional innumeracy: Predicted and

actual affective responses to grand-scale tragedies. Journal of Experimental

Social Psychology, 44(3), 692-698.

Emmons, R. A., Cheung, C., & Tehrani, K. (1998). Assessing spirituality through

personal goals: Implications for research on religion and subjective well-

being. Social indicators research, 45(1), 391-422.

Epstein, S. (1993). Implications of cognitive-experiential self-theory for personality and

developmental psychology. In D. C. Funder, R. D. Parke, C. Tomlinson-Keasey,

& K. Widaman (Eds.), APA science Vols. Studying lives through time:

Personality and development (pp. 399-438). 69

Epstein, S. (1994). Integration of the cognitive and the psychodynamic

unconscious. American psychologist, 49(8), 709-723.

Epstein, S., & Weiner, I. (2003). Cognitive-Experiential Self-Theory of Personality. In T.

Millon & M. Lerner (Eds.), Handbook of Psychology: Personality and Social

Psychology (Vol. 5, pp. 159-184). Hoboken: John Wiley & Sons, Inc.

Estle, S. J., Green, L., Myerson, J., & Holt, D. D. (2007). Discounting of monetary and

directly consumable rewards. Psychological science, 18(1), 58-63.

Finley, A. J., Tang, D., & Schmeichel, B. J. (2015). Revisiting the relationship between

individual differences in analytic thinking and religious belief: Evidence that

measurement order moderates their inverse correlation. PloS one, 10(9).

Fishbach, A., Friedman, R. S., & Kruglanski, A. W. (2003). Leading us not into

temptation: Momentary allurements elicit overriding goal activation. Journal of

personality and social psychology, 84(2), 296-309.

Fitzsimons, G. M., & Finkel, E. J. (2010). Interpersonal influences on self-

regulation. Current Directions in Psychological Science, 19(2), 101-105.

Frederick, S. (2005). Cognitive reflection and decision making. The Journal of Economic

Perspectives, 19(4), 25-42.

Frederick, S., Loewenstein, G., & O'Donoghue, T. (2002). Time discounting and time

preference: A critical review. Journal of economic literature, 40(2), 351-401.

Fujita, K., & Han, H. A. (2009). Moving beyond deliberative control of impulses: The

effect of construal levels on evaluative associations in self-control

conflicts. Psychological Science, 20(7), 799-804. 70

Fujita, K., Trope, Y., Liberman, N., & Levin-Sagi, M. (2006). Construal levels and self-

control. Journal of personality and social psychology, 90(3), 351-367.

Gervais, W. M., & Norenzayan, A. (2012). Analytic thinking promotes religious

disbelief. Science, 336(6080), 493-496.

Gervais, W. M., & Norenzayan, A. (2012a). Like a camera in the sky? Thinking about

God increases public self-awareness and socially desirable responding. Journal of

experimental social psychology, 48(1), 298-302.

Goodwin, G. P., & Darley, J. M. (2008). The psychology of meta-ethics: Exploring

objectivism. Cognition, 106(3), 1339-1366.

Gorsuch, R. L. (1984). Measurement: The boon and bane of investigating

religion. American psychologist, 39(3), 228-236.

Graham, J., & Haidt, J. (2010). Beyond beliefs: Religions bind individuals into moral

communities. Personality and Social Psychology Review, 14(1), 140-150.

Green, L., Fry, A. F., & Myerson, J. (1994). Discounting of delayed rewards: A life-span

comparison. Psychological science, 5(1), 33-36.

Green, L., & Myerson, J. (2004). A discounting framework for choice with delayed and

probabilistic rewards. Psychological bulletin, 130(5), 769-792.

Green, L., Myerson, J., Lichtman, D., Rosen, S., & Fry, A. (1996). Temporal discounting

in choice between delayed rewards: the role of age and income. Psychology and

aging, 11(1), 79-84. 71

Green, L., Myerson, J., & Ostaszewski, P. (1999). Amount of reward has opposite effects

on the discounting of delayed and probabilistic outcomes. Journal of

Experimental Psychology-Learning Memory and Cognition, 25(2), 418-427.

Hamilton, K. R., & Potenza, M. N. (2012). Relations among delay discounting,

addictions, and money mismanagement: Implications and future directions. The

American journal of drug and alcohol abuse, 38(1), 30-42.

Hardisty, D. J., & Weber, E. U. (2009). Discounting future green: money versus the

environment. Journal of Experimental Psychology: General, 138(3), 329-340.

Harrison, J. M. D., & McKay, R. T. (2013). Do religious and moral concepts influence

the ability to delay gratification? A priming study. Journal of Articles in Support

of the Null Hypothesis, 10(1), 25-40.

Hayes, A. F. (2016). The PROCESS macro for SPSS and SAS.

Hayes, A. F., & Preacher, K. J. (2014). Statistical mediation analysis with a

multicategorical independent variable. British Journal of Mathematical and

Statistical Psychology, 67(3), 451-470.

Hommel, B., Colzato, L. S., Scorolli, C., Borghi, A. M., & van den Wildenberg, W. P.

(2011). Religion and action control: faith-specific modulation of the Simon effect

but not stop-signal performance. Cognition, 120(2), 177-185.

Howard, J. L., Gagné, M., Morin, A. J., & Forest, J. (2016). Using bifactor exploratory

structural equation modeling to test for a continuum structure of

motivation. Journal of Management. 72

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure

analysis: Conventional criteria versus new alternatives. Structural Equation

Modeling, 6, 1–55.

Izuma, K., Saito, D. N., & Sadato, N. (2008). Processing of social and monetary rewards

in the human striatum. Neuron, 58(2), 284-294.

James, W. (1985). The varieties of religious experience (Vol. 15). Cambridge: Harvard

University Press. (Original work published in 1902)

Jeffrey, S. A., & Adomdza, G. K. (2010). Incentive salience and improved

performance. Human Performance, 24(1), 47-59.

Johnson, M. W., & Bickel, W. K. (2002). Within‐subject comparison of real and

hypothetical money rewards in delay discounting. Journal of the experimental

analysis of behavior, 77(2), 129-146.

Kahneman, D. (2015). Thinking, fast and slow. New York: Farrar, Straus and Giroux.

Kaplan, B. A., Amlung, M., Reed, D. D., Jarmolowicz, D. P., McKerchar, T. L., &

Lemley, S. M. (2016). Automating scoring of delay discounting for the 21-and

27-item monetary choice questionnaires. The Behavior Analyst, 39(2), 293-304.

Kaskutas, L. A., Bond, J., & Humphreys, K. (2002). Social networks as mediators of the

effect of Alcoholics Anonymous. Addiction, 97(7), 891-900.

Kim, H., Schnall, S., & White, M. P. (2013). Similar psychological distance reduces

temporal discounting. Personality and Social Psychology Bulletin, 39(8), 1005-

1016. 73

Kim-Spoon, J., McCullough, M. E., Bickel, W. K., Farley, J. P., & Longo, G. S. (2015).

Longitudinal associations among religiousness, delay discounting, and substance

use initiation in early adolescence. Journal of Research on Adolescence, 25(1),

36-43.

Kirby, K. N. (2009). One-year temporal stability of delay-discount rates. Psychonomic

bulletin & review, 16(3), 457-462.

Kirby, K. N., & Finch, J. C. (2010). The hierarchical structure of self-reported

impulsivity. Personality and individual differences, 48(6), 704-713.

Kirby, K. N., & Maraković, N. N. (1996). Delay-discounting probabilistic rewards: Rates

decrease as amounts increase. Psychonomic bulletin & review, 3(1), 100-104.

Kirby, K. N., & Petry, N. M. (2004). Heroin and cocaine abusers have higher discount

rates for delayed rewards than alcoholics or non‐drug‐using

controls. Addiction, 99(4), 461-471.

Kirby, K. N., Petry, N. M., & Bickel, W. K. (1999). Heroin addicts have higher discount

rates for delayed rewards than non-drug-using controls. Journal of Experimental

psychology: general, 128(1), 78-87.

Klapproth, F. (2008). Time and decision making in humans. Cognitive, Affective, &

Behavioral Neuroscience, 8(4), 509-524.

Kopp, C. B. (1982). Antecedents of self-regulation: A developmental

perspective. Developmental psychology, 18(2), 199-214.

Laibson, D. (1997). Golden eggs and hyperbolic discounting. The Quarterly Journal of

Economics, 112(2), 443-478. 74

Liberman, N., & Förster, J. (2009). The effect of psychological distance on perceptual

level of construal. Cognitive science, 33(7), 1330-1341.

Liberman, N., & Trope, Y. (1998). The role of feasibility and desirability considerations

in near and distant future decisions: A test of temporal construal theory. Journal

of personality and social psychology, 75(1), 5-18.

Löckenhoff, C. E., O'donoghue, T., & Dunning, D. (2011). Age differences in temporal

discounting: the role of dispositional affect and anticipated emotions. Psychology

and aging, 26(2), 274-284.

Loewenstein, G. (1987). Anticipation and the valuation of delayed consumption. The

Economic Journal, 97(387), 666-684.

Loewenstein, G. (1996). Out of control: Visceral influences on behavior. Organizational

behavior and human decision processes, 65(3), 272-292.

Loewenstein, G., & Prelec, D. (1992). Anomalies in intertemporal choice: Evidence and

an interpretation. The Quarterly Journal of Economics, 107(2), 573-597.

Logue, A. W., & Anderson, Y. D. (2001). Higher-education administrators: When the

future does not make a difference. Psychological Science, 12(4), 276-281.

Luhrmann, T. M., Nusbaum, H., & Thisted, R. (2010). The absorption hypothesis:

learning to hear God in evangelical Christianity. American

Anthropologist, 112(1), 66-78.

MacKillop, J., Amlung, M. T., Few, L. R., Ray, L. A., Sweet, L. H., & Munafò, M. R.

(2011). Delayed reward discounting and addictive behavior: a meta-

analysis. Psychopharmacology, 216(3), 305-321. 75

Madden, G. J., Begotka, A. M., Raiff, B. R., & Kastern, L. L. (2003). Delay discounting

of real and hypothetical rewards. Experimental and clinical

psychopharmacology, 11(2), 139-145.

Madden, G. J., Bickel, W. K., & Jacobs, E. A. (1999). Discounting of delayed rewards in

opioid-dependent outpatients: exponential or hyperbolic discounting

functions?. Experimental and clinical psychopharmacology, 7(3), 284-293.

Malkoc, S. A., Zauberman, G., & Bettman, J. R. (2010). Unstuck from the concrete:

Carryover effects of abstract mindsets in intertemporal

preferences. Organizational Behavior and Human Decision Processes, 113(2),

112-126.

Marshall, D. A. (2002). Behavior, belonging, and belief: A theory of ritual

practice. Sociological Theory, 20(3), 360-380.

Mazur, J. E. (1987). “An adjusting procedure for studying delayed reinforcement" In J. E.

Mazur, J. A. Nevin, & H. Rachlin (Eds.), Quantitative analysis of behavior: The

effect of delay and intervening events on reinforcement value (Vol. 5, pp. 55-73).

Hillsdale: Erlbaum.

Mazur, J. E., & Biondi, D. R. (2009). Delay‐amount tradeoffs in choices by pigeons and

rats: Hyperbolic versus exponential discounting. Journal of the experimental

analysis of behavior, 91(2), 197-211.

McCullough, M. E., & Boker, S. M. (2007). “Dynamical modeling for studying self-

regulatory processes: An example from the study of religious development over 76

the life span” In A. D. Ong & M. V. Dulmen (Eds.), Handbook of methods in

positive psychology (pp. 380–394). New York: Oxford University Press.

McCullough, M. E., & Carter, E. C. (2013). Religion, self-control, and self-regulation:

How and why are they related. APA handbook of psychology, religion, and

spirituality, 1, 123-138.

McCullough, M. E., & Willoughby, B. L. (2009). Religion, self-regulation, and self-

control: Associations, explanations, and implications. Psychological

bulletin, 135(1), 69-93.

Mindel, C. H., & Vaughan, C. E. (1978). A multidimensional approach to religiosity and

disengagement. Journal of Gerontology, 33(1), 103-108.

Mischel, W., Ayduk, O., Berman, M. G., Casey, B. J., Gotlib, I. H., Jonides, J., Kross, E.,

Teslovich, T., Wilson, N. L., Zayas, V., & Shoda, Y. (2011). Willpower over the

life span: decomposing self-regulation. Social Cognitive and Affective

Neuroscience, 6(2), 252-256.

Mischel, W., Shoda, Y., & Rodriguez, M. L. (1989). Delay of gratification in

children. Science, 244(4907), 933-938.

Mitchell, J. M., Fields, H. L., D'esposito, M., & Boettiger, C. A. (2005). Impulsive

responding in alcoholics. Alcoholism: Clinical and Experimental

Research, 29(12), 2158-2169.

Monroe, N., & Jankowski, P. J. (2016). The effectiveness of a prayer intervention in

promoting change in perceived attachment to God, positive affect, and

psychological distress. Spirituality in Clinical Practice, 3(4), 237-249. 77

Morgan, J., Clark, D., Tripodis, Y., Halloran, C. S., Minsky, A., Wildman, W. J., ... &

McNamara, P. (2016). Impacts of religious semantic priming on an intertemporal

discounting task: Response time effects and neural

correlates. Neuropsychologia, 89, 403-413.

Muthén, L. K., & Muthén, B. O. (2018). Mplus (Version 8.0). Los Angeles: Muthén &

Muthén.

Nielsen, K. (1990). Ethics without God (revised ed.). Amherst, NY: Prometheus Books.

Norenzayan, A., Gervais, W. M., & Trzesniewski, K. H. (2012). Mentalizing deficits

constrain belief in a personal God. PloS one, 7(5).

Ohmura, Y., Takahashi, T., & Kitamura, N. (2005). Discounting delayed and

probabilistic monetary gains and losses by smokers of

cigarettes. Psychopharmacology, 182(4), 508-515.

Okada, E. M., & Hoch, S. J. (2004). Spending time versus spending money. Journal of

consumer research, 31(2), 313-323.

Öner-Özkan, B. (2007). Future time orientation and religion. Social Behavior and

Personality: an international journal, 35(1), 51-62.

Pacini, R., & Epstein, S. (1999). The relation of rational and experiential information

processing styles to personality, basic beliefs, and the ratio-bias

phenomenon. Journal of personality and social psychology, 76(6), 972-987.

Paglieri, F., Borghi, A. M., Colzato, L. S., Hommel, B., & Scorolli, C. (2013). Heaven

can wait. How religion modulates temporal discounting. Psychological

research, 77(6), 738-747. 78

Pennycook, G., Cheyne, J. A., Koehler, D. J., & Fugelsang, J. A. (2013). Belief bias

during reasoning among religious believers and skeptics. Psychonomic Bulletin &

Review, 20(4), 806-811.

Petry, N. M. (2001). Pathological gamblers, with and without substance abuse disorders,

discount delayed rewards at high rates. Journal of abnormal psychology, 110(3),

482-487.

Piazza, J., & Landy, J. F. (2013). " Lean not on your own understanding": Belief that

morality is founded on divine authority and non-utilitarian moral

judgments. Judgment and Decision Making, 8(6), 639-661.

Preston, J. L., & Ritter, R. S. (2013). Different effects of religion and God on prosociality

with the ingroup and outgroup. Personality and Social Psychology Bulletin,

39(11), 1471-1483.

Rachlin, H. (2006). Notes on discounting. Journal of the experimental analysis of

behavior, 85(3), 425-435.

Read, D., & Read, N. L. (2004). Time discounting over the lifespan. Organizational

behavior and human decision processes, 94(1), 22-32.

Reimers, S., Maylor, E. A., Stewart, N., & Chater, N. (2009). Associations between a

one-shot delay discounting measure and age, income, education and real-world

impulsive behavior. Personality and Individual Differences, 47(8), 973-978.

Ritter, R. S., & Preston, J. L. (2013). Representations of religious words: Insights for

religious priming research. Journal for the Scientific Study of Religion, 52(3),

494-507. 79

Ritter, R. S., Preston, J. L., & Hernandez, I. (2014). Happy tweets: Christians are happier,

more socially connected, and less analytical than atheists on Twitter. Social

Psychological and Personality Science, 5(2), 243-249.

Rounding, K., Lee, A., Jacobson, J. A., & Ji, L. J. (2012). Religion replenishes self-

control. Psychological science, 23(6), 635-642.

Samuelson, P. A. (1937). A note on measurement of utility. The review of economic

studies, 4(2), 155-161.

Saroglou, V., Delpierre, V., & Dernelle, R. (2004). Values and religiosity: A meta-

analysis of studies using Schwartz’s model. Personality and individual

differences, 37(4), 721-734.

Schacter, D. L., & Addis, D. R. (2007). The cognitive neuroscience of constructive

memory: remembering the past and imagining the future. Philosophical

Transactions of the Royal Society B: Biological Sciences, 362(1481), 773-786.

Scheres, A., Dijkstra, M., Ainslie, E., Balkan, J., Reynolds, B., Sonuga-Barke, E., &

Castellanos, F. X. (2006). Temporal and probabilistic discounting of rewards in

children and adolescents: effects of age and ADHD

symptoms. Neuropsychologia, 44(11), 2092-2103.

Schoenfelder, T. E., & Hantula, D. A. (2003). A job with a future? Delay discounting,

magnitude effects, and domain independence of utility for career

decisions. Journal of Vocational Behavior, 62(1), 43-55.

Shanker, S. & Barker, T. (2016). Self-reg. New York: Penguin. 80

Shariff, A. F., & Norenzayan, A. (2007). God is watching you: Priming God concepts

increases prosocial behavior in an anonymous economic game. Psychological

science, 18(9), 803-809.

Shariff, A. F., Willard, A. K., Andersen, T., & Norenzayan, A. (2016). Religious

priming: A meta-analysis with a focus on prosociality. Personality and Social

Psychology Review, 20(1), 27-48.

Shenhav, A., Rand, D. G., & Greene, J. D. (2012). Divine intuition: cognitive style

influences belief in God. Journal of Experimental Psychology: General, 141(3),

423-428.

Shu, S. B., & Gneezy, A. (2010). Procrastination of enjoyable experiences. Journal of

Marketing Research, 47(5), 933-944.

Simpson, A., Piazza, J., & Rios, K. (2016). Belief in divine moral authority: Validation of

a shortened scale with implications for social attitudes and moral

cognition. Personality and Individual Differences, 94, 256-265.

Sinnott-Armstrong, W. (2009). Morality: Without God? New York: Oxford University

Press.

Sosis, R. (2003). Why aren’t we all Hutterites?. Human nature, 14(2), 91-127.

Sosis, R. (2005). Does Religion Promote Trust?: The Role of Signaling, Reputation, and

Punishment. Interdisciplinary journal of research on religion, 1(7).

Steinberg, L., Graham, S., O’Brien, L., Woolard, J., Cauffman, E., & Banich, M. (2009).

Age differences in future orientation and delay discounting. Child

development, 80(1), 28-44. 81

Thaler, R. H. (1981). Some empirical evidence on dynamic inconsistency. Quasi rational

economics, 1, 127-136.

Tóth-Király, I., Bõthe, B., Rigó, A., & Orosz, G. (2017). An illustration of the

Exploratory Structural Equation Modeling (ESEM) framework on the passion

scale. Frontiers in psychology, 8, 1968.

Trope, Y., & Liberman, N. (2000). Temporal construal and time-dependent changes in

preference. Journal of personality and social psychology, 79(6), 876-889.

Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological review, 110(3),

403-421.

Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological

distance. Psychological review, 117(2), 440-463.

Vallacher, R. R., & Wegner, D. M. (1989). Levels of personal agency: Individual

variation in action identification. Journal of Personality and Social

psychology, 57(4), 660-671.

Wilson, T. D., & Gilbert, D. T. (2005). Affective forecasting: Knowing what to

want. Current Directions in Psychological Science, 14(3), 131-134.

Worthington Jr, E. L., Wade, N. G., Hight, T. L., Ripley, J. S., McCullough, M. E.,

Berry, J. W., ... & O'connor, L. (2003). The Religious Commitment Inventory--

10: Development, refinement, and validation of a brief scale for research and

counseling. Journal of Counseling Psychology, 50(1), 84-96. 82

Yonker, J. E., Edman, L. R., Cresswell, J., & Barrett, J. L. (2016). Primed analytic

thought and religiosity: The importance of individual characteristics. Psychology

of Religion and Spirituality, 8(4), 298-308.

Zimbardo, P. G., & Boyd, J. N. (1999). Putting time in perspective: A valid, reliable

individual-differences metric. Journal of Personality and Social Psychology,

77(6), 1271-1288.

Zinnbauer, B.J., & Pargament, K.I. (2005). “Religiousness and Spirituality” In R.F.

Paloutzian & Park, C.L. (eds.), Handbook of Psychology of Religion and

Spirituality (pp. 21-42). New York: The Guilford Press.

83

APPENDIX

Experimental Manipulation

Personal Religiosity (Faith/Belief) Condition 1. Do you have faith that God exists? 2. Do you believe in divine intervention (i.e., miracles)? 3. How spiritual are you? 4. Do you consider your religious beliefs to be sacred? 5. Do you believe prophets deliver the word of God?

Answer Choices: • Not at all, Very little, Somewhat, Moderately, Very Much, Extremely

Organizational Religiosity (Practice/Experience) Condition 1. Do you think religious scriptures (such as the Bible) should be interpreted literally? 2. How important to you is celebrating your favorite religious holiday (e.g., Christmas)? 3. Do you consider religious sermons important and meaningful? 4. How often do you attend church or church-related activities? 5. Do you feel close to those in your religious community?

Answer Choices: • Not at all, Very little, Somewhat, Moderately, Very Much, Extremely • Never, 1/year, A few times a year, 1 or 2/month, 1/week, More than once per week 84

Monetary-Choice Questionnaire (MCQ-27)

(Kirby, Petry & Bickel, 1999)

For each of the next 27 choices, please indicate which reward you would prefer: the smaller reward today, or the larger reward in the specified number of days.

1. $54 today or $55 in 117 days? 2. $55 today or $75 in 61 days? 3. $19 today or $25 in 53 days? 4. $31 today or $85 in 7 days? 5. $14 today or $25 in 19 days? 6. $47 today or $50 in 160 days? 7. $15 today or $35 in 13 days? 8. $25 today or $60 in 14 days? 9. $78 today or $80 in 162 days? 10. $40 today or $55 in 62 days? 11. $11 today or $30 in 7 days? 12. $67 today or $75 in 119 days? 13. $34 today or $35 in 186 days? 14. $27 today or $50 in 21 days? 15. $69 today or $85 in 91 days? 16. $49 today or $60 in 89 days? 17. $80 today or $85 in 157 days? 18. $24 today or $35 in 29 days? 19. $33 today or $80 in 14 days? 20. $28 today or $30 in 179 days? 21. $34 today or $50 in 30 days? 22. $25 today or $30 in 80 days? 23. $41 today or $75 in 20 days? 24. $54 today or $60 in 111 days? 25. $54 today or $80 in 30 days? 26. $22 today or $25 in 136 days? 27. $20 today or $55 in 7 days?

85

Behavior Identification Form (BIF)

(Vallacher & Wegner, 1989)

Any behavior can be described in many ways. For example, one person might describe a behavior as "writing a paper," while another person might describe the same behavior as "pushing keys on the keyboard." Yet another person might describe it as "expressing thoughts."

This form focuses on your personal preferences for how a number of different behaviors should be described. Below you will find several behaviors listed. After each behavior will be two different ways in which the behavior might be identified.

For example: 1. Attending class o sitting in a chair o looking at a teacher

Your task is to choose the identification, a or b, that best describes the behavior for you. Simply place a checkmark next to the option you prefer. Be sure to respond to every item. Please mark only one alternative for each pair. Remember, mark the description that you personally believe is more appropriate for each pair.

1. Making a list o Getting organized* o Writing things down 2. Reading o Following lines of print o Gaining knowledge* 3. Joining the Army o Helping the Nation's defense* o Signing up 4. Washing clothes o Removing odors from clothes* o Putting clothes into the machine 5. Picking an apple o Getting something to eat* o Pulling an apple off a branch 6. Chopping down a tree o Wielding an axe o Getting firewood* 7. Measuring a room for carpeting o Getting ready to remodel* o Using a yard stick 86

8. Cleaning the house o Showing one's cleanliness* o Vacuuming the floor 9. Painting a room o Applying brush strokes o Making the room look fresh* 10. Paying the rent o Maintaining a place to live* o Writing a check 11. Caring for houseplants o Watering plants o Making the room look nice* 12. Locking a door o Putting a key in the lock o Securing the house* 13. Voting o Influencing the election* o Marking a ballot 14. Climbing a tree o Getting a good view* o Holding on to branches 15. Filling out a personality test o Answering questions o Revealing what you're like* 16. Toothbrushing o Preventing tooth decay* o Moving a brush around in one's mouth 17. Taking a test o Answering questions o Showing one's knowledge* 18. Greeting someone o Saying hello o Showing friendliness* 19. Resisting temptation o Saying "no" o Showing moral courage* 20. Eating o Getting nutrition* o Chewing and swallowing 21. Growing a garden o Planting seeds o Getting fresh vegetables* 22. Traveling by car o Following a map 87

o Seeing countryside* 23. Having a cavity filled o Protecting your teeth* o Going to the dentist 24. Talking to a child o Teaching a child something* o Using simple words 25. Pushing a doorbell o Moving a finger o Seeing if someone's home*

* Higher level alternative.

Total score is the sum of higher level alternative choices.

88

Abbreviated Morality Founded on Divine Authority (A-MFDA)

(Simpson, Piazza, & Rios, 2016)

Using a scale where 1 means “strongly disagree” and 9 means “strongly agree,” please indicate the extent to which you agree with the following statements:

1. Everything we need to know about living a moral life God has revealed to us 2. What is morally good and right is what God says is good and right 3. If you want to know how to live a moral life you should look to God 4. Acts that are immoral are immoral because God forbids them 5. It is possible to live a righteous life without knowledge of God’s laws*

*Reverse scored

89

Rational-Experiential Inventory

(Pacini & Epstein, 1999)

Please answer the following items as honestly and accurately as possible. Respond to each item using a 5-point scale, where 1 means “definitely not true of myself” and 5 means “definitely true of myself.”

1. I try to avoid situations that require thinking in depth about something. 2. I'm not that good at figuring out complicated problems. 3. I enjoy intellectual challenges. 4. I am not very good in solving problems that require careful logical analysis. 5. I don't like to have to do a lot of thinking. 6. I enjoy solving problems that require hard thinking. 7. Thinking is not my idea of an enjoyable activity. 8. I am not a very analytical thinker. 9. Reasoning things out carefully is not one of my strong points. 10. I prefer complex to simple problems. 11. Thinking hard and for a long time about something gives me little satisfaction. 12. I don't reason well under pressure. 13. I am much better at figuring things out logically than most people. 14. I have a logical mind. 15. I enjoy thinking in abstract terms. 16. I have no problem in thinking things through carefully. 17. Using logic usually works well for me in figuring out problems in my life. 18. Knowing the answer without having to understand the reasoning behind it is good enough for me. 19. I usually have clear, explainable reasons for my decisions. 20. Learning new ways to think would be very appealing to me. 21. I like to rely on my intuitive impressions. 22. I don't have a very good sense of intuition. 23. Using my "gut-feelings" usually works well for me in figuring out problems in my life. 24. I believe in trusting my hunches. 25. Intuition can be a very useful way to solve problems. 26. I often go by my instincts when deciding on a course of action. 27. I trust my initial feelings about people. 28. When it comes to trusting people, I can usually rely on my gut feelings. 29. If I were to rely on my gut feelings, I would often make mistakes. 30. I don't like situations in which I have to rely on intuition. 31. I think there are times when one should rely on one's intuition. 32. I think it is foolish to make important decisions based on feelings. 33. I don't think it is a good idea to rely on one's intuition for important decisions. 34. I generally don't depend on my feelings to help me make decisions. 90

35. I hardly ever go wrong when I listen to my deepest "gut-feelings" to find an answer. 36. I would not want to depend on anyone who described himself or herself as intuitive. 37. My snap judgments are probably not as good as most people's. 38. I tend to use my heart as a guide for my actions. 39. I can usually feel when a person is right or wrong, even if I can't explain how I know. 40. I suspect my hunches are inaccurate as often as they are accurate.

91

Religious Practice

Please respond to the following questions as honestly and accurately as possible. Remember that your responses are completely anonymous.

1. Do you believe in God? a. Yes b. No c. Not sure

2. How much do you believe in God? Scale from 0 to 10

3. What is your religion? a. Christian-Protestant b. Christian-Catholic c. Christian-Other d. Jewish e. Muslim f. Mormon g. Buddhist h. Hindu i. Atheist j. Agnostic k. Other religious affiliation l. I do not consider myself affiliated with any religion

4. How religious are you? Scale from 0 to 10

5. How often do you attend religious services? a. Never b. 1/year c. A few times a year d. 1 or 2/month e. 1/week f. More than once per week

6. Approximately how much money (in US Dollars $) do you and your immediate family members contribute to your religious organization each year? If the answer is "Nothing" please write 0. If you do not know, please write "I don't know."

7. How often do you engage in group religious prayer (i.e., praying in a group with others)? a. Never 92

b. 1/year c. A few times a year d. 1 or 2/month e. 1/week f. More than once per week

8. How often do you engage in personal religious prayer (i.e., praying in private without others present)? a. Never b. 1/year c. A few times a year d. 1 or 2/month e. 1/week f. More than once per week

9. Other than personal and group prayer, how often do you take part in religious activities? a. Never b. 1/year c. A few times a year d. 1 or 2/month e. 1/week f. More than once per week

10. To what extent do you feel a sense of belonging in your religious community a. Not at all b. Very little c. Somewhat d. Moderately e. Very Much f. Extremely

11. How close do you feel to people in your religious community? a. Not at all b. Very little c. Somewhat d. Moderately e. Very Much f. Extremely

12. To what extent do people in your religious community offer you social support? a. Not at all b. Very little c. Somewhat 93

d. Moderately e. Very Much f. Extremely

13. To what extent do you and those in your religious community have the same goals? a. Not at all b. Very little c. Somewhat d. Moderately e. Very Much f. Extremely

14. To what extent do you and those in your religious community have the same values? a. Not at all b. Very little c. Somewhat d. Moderately e. Very Much f. Extremely

94

Demographics Questionnaire

1. What is your age?

2. What is your sex? a. Man b. Woman c. Prefer not to answer

3. What is your race? a. White/European American b. Black/African American c. Hispanic/Latino d. Asian American or Pacific Islander e. American Indian or Alaska Native f. Middle Eastern or Arab American g. Other

4. What is the highest level of education that you have completed? a. No education b. Some high school c. High school/GED d. Some college e. Trade/technical/vocational training f. Associates degree g. Bachelor’s degree h. Master’s degree i. Professional degree j. Doctorate degree

5. Is English your first language? a. Yes b. No

6. Are you a resident of the United States? a. Yes b. No

7. How do you typically vote? If you have never voted before, indicate how you think you would typically vote. a. Democratic b. Republican c. Independent d. Other 95

e. Non-voter

For the following two questions, please respond by using an 11-point scale where 1 means “extreme left”, 6 means “centrist”, and 11 means “extreme right”

8. What is your political belief regarding social issues?

9. What is your political belief regarding economic issues?

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