What is the main difference between liberals and conservatives moral foundations?

Do liberals and conservatives use different moral languages? Two replications and six extensions of Graham, Haidt, and Nosek’s [2009] moral text analysis

Author links open overlay panelJeremy A.FrimerEnvelope

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Abstract

Do liberals and conservatives tend to use different moral languages? The Moral Foundations Hypothesis states that liberals rely more on foundations of care/harm and fairness/cheating whereas conservatives rely more on loyalty/betrayal, authority/subversion, and purity/degradation in their moral functioning. In support, Graham, Haidt, and Nosek [2009; Study 4] showed that sermons delivered by liberal and conservative pastors differed as predicted in their moral word usage, except for the loyalty foundation. I present two high-powered replication studies in religious contexts and six extension studies in politics, the media, and organizations to test ideological differences in moral language usage. On average, replication success rate was 30% and effect sizes were 38 times smaller than those in the original study. A meta-analysis [N = 303,680] found that compared to liberals, conservatives used more authority r = 0.05, 95% confidence interval = [0.02, 0.09] and purity words, r = 0.14 [0.09, 0.19], fewer loyalty words, r = −0.08 [−0.10, −0.05], and no more or less harm, r = 0.00 [−0.02, 0.02], or fairness words, r = −0.03 [−0.06, 0.01].

Introduction

Why do liberals and conservatives have such difficulty relating to one another? An influential proposition, advanced in Moral Foundations Theory [e.g., Graham et al., 2009, Haidt, 2012], is that liberals’ and conservatives’ moralities are built upon qualitatively different foundations. The moral foundations hypothesis [MFH] is that liberals’ morality is primarily built on the foundations of harm/care and fairness/cheating whereas conservatives’ morality is based in these foundations to a lesser degree while drawing more from foundations of loyalty/betrayal, authority/subversion, and purity/degradation [henceforth called harm, fairness, loyalty, authority, and purity, respectively]. MFT has been groundbreaking in the way it expanded the moral domain to consider the roles of loyalty, authority, and purity; these moral concerns had received relatively little attention in moral psychology compared to harm and fairness. MFT was also groundbreaking for proposing a theory of moral development that integrated both nativism [the foundations are innate] and constructivism [they are elaborated through cultural learning].

Evidence for the MFH has come in many forms. For example, liberals and conservatives self-report that they use the foundations as predicted by the MFH. The focus of the present investigation is language: Do liberals and conservative use different moral languages? It is important to know whether liberals and conservatives differ in their moral languages because “words do the work of politics” [Graham et al., 2009; p.1038]. Language is a primary mechanism by which people learn about and convince others of their beliefs and opinions [Lakoff, 2004, Luntz, 2007], and thus plays an important role in maintaining a shared sense of reality [Echterhoff, Higgins, & Levine, 2009] which binds together members of moral communities. The present article presents 2 replication studies and 6 extensions that test whether liberals and conservatives use different moral languages.

Moral Foundations Theory is pluralistic in two ways. First, “foundations are not [just] values” [Graham et al., 2009, p.1031]. Instead, foundations are psychologically pluralistic in that each foundation is made up of attitudes, cognitions, values, emotions, physiological reactions, and linguistic styles [Graham et al., 2013]. The theory is also pluralistic in the sense that there is not just one moral foundation—there are five or possibly more. To illustrate the point, Haidt and Joseph [2012] suggested that moral foundations are analogous to distinct taste receptors [for salt, sourness, sweetness, etc.] insofar as they are irreducibly pluralistic innate modules. Each moral foundation is an “evolved psychological mechanism” that become elaborated and revised through cultural leaning [Graham et al., 2009, p.1031].

According to the theory, foundational differences materialize not only between countries but also within countries, such as in Culture Wars between liberals and conservatives. This is because liberals tend to have an optimistic view of human nature [Sowell, 2002], which posits that only freedom and liberty are needed to bring out the best in people. Liberals thus rely on the “individualizing” foundations of harm/care and fairness/cheating as a basis of their morality. Conservatives’ more pessimistic view of human nature [Sowell, 2002] prescribes the need for social constraints, institutions, social structure, and order to rein in inherently selfish tendencies and thus make civil society possible; in this way, the “binding” foundations of loyalty, authority, and purity also play a prime role in conservative morality [see Graham et al., 2009; p.1029]. During the developmental period, exposure to practices, lessons, and stories elaborate the various psychological components of different foundations within the moral minds of young liberals and conservatives, rendering them markedly different by adulthood. This might explain why liberals and conservatives might communicate using different moral languages.

What is the evidence that adult liberals and conservatives default to different moral languages? In the seminal empirical article on the topic and precedent for the current investigation, Graham et al. [2009] reported four studies, the first three of which relied on self-report methods and are thus tangential to the present question concerning language. In the crucial Study 4, they showed that the sermons of liberal and conservative pastors varied in their moral languages. For foundations of harm, fairness, authority, and purity, the effects were supportive of the MFH: liberals used more harm and fairness, and less authority and purity language than conservatives. However, liberals also used loyalty words more frequently than conservatives, a finding not predicted by the MFH. It is this particular study and this set of linguistic analyses that the present paper aims to replicate and extend.

Given the extensive support for the MFH coming from other methods [e.g., self-report], it remains unclear why liberals might use more loyalty words than conservatives, rather than vice versa. Graham et al. [2009] attempted to somewhat diminish the significance of this disconfirming finding. Their argument was based on the premise that word frequencies cannot assess whether a speaker has a positive or negative attitude toward a foundation. To illustrate the critique, consider the hypothetical situation in which a liberal were to say “group loyalties have become problematic in this country.” The linguistic analysis would score this statement as being high [dense] in loyalty language because of the mention of the words group and loyalties, both of which are words in the loyalty dictionary, even though the speaker’s attitude toward loyalty is clearly critical. To accommodate this concern, they also had people read and code the texts for the speaker’s attitudes toward each foundation—whether statements were supportive or critical of a foundation—and claimed that “these usage scores are more valid indicators than the raw counts of how speakers value each of the five foundations” [p.1039; emphasis added]. They then tested the MFH with respect to whether liberals and conservatives differed as predicted in their verbalized valuation of each foundation. Unequivocal support the MFH on all five foundations, including loyalty, resulted.

Note that Graham et al. [2009] did not dismiss the relevance of word counts vis-à-vis the MFH. Their point was subtler: that word counts are less relevant to measuring how much a person values a foundation than are contextual usage assessments. I make the theoretical case here that the topics that people bring up [operationalized as word frequencies] are also relevant to MFT and the MFH.

Beginning with first principles, “foundations are not [just] values or virtues” [Graham et al., 2009, p.1031]. Instead, foundations are “psychological systems” and thus psychologically pluralistic in that each foundation is made up of not just values, but also cognitions, emotions, physiological reactions, and languages [Graham et al., 2013]. Following from this principle of psychological pluralism, both the moral languages that people use [operationalized by word counts] and individuals’ verbalized attitudes toward those topics [operationalized by contextualized usage ratings] should both be included within the large umbrella of construct plurality that MFT proposes.

This position is consistent with a long psychological research tradition that has accepted the basic premise that what a person talks about [in positive or negative terms] reflects their psychological states and personality traits. Freud [1901] believed that one could draw inferences about a person’s hidden intentions through observation of slips of the tongue. The Rorschach [1921] inkblot test and the Thematic Apperception Test [e.g., McClelland, 1979, Winter, 1998] asked people to make sense of ambiguous images; what they spontaneously said [or projected] was thought to be revealing of personalities and psychopathologies. Gottschalk [e.g., Gottschalk, Gleser, Daniels, & Block, 1958] and McAdams [1995] continued the tradition by coding stream-of-consciousness thoughts and autobiographical life narratives, respectively. With the advent and expanding availability of computers, researchers began developing computerized methods of quantifying word usage [e.g., Rosenberg and Tucker, 1978, Stone et al., 1966, Weintraub, 1981, Weintraub, 1989].

A century after Freud’s writings on slips-of-the tongue, Pennebaker, Francis, and Booth [2001] released the first version of Linguistic Inquiry and Word Count [LIWC], which has since become enormously popular among psychologists for using text to understand individuals’ states, situations, and personalities [see Tausczik & Pennebaker, 2010, for a review; and used in Graham et al., 2009]. LIWC is built on the premise that “the words we use in daily life reflect what we are paying attention to, what we are thinking about, what we are trying to avoid, how we are feeling, and how we are organizing and analyzing our worlds” [Tausczik & Pennebaker, 2010; p.30]. Indeed, a number of studies have found that word usage [assessed via word counts and LIWC] is a stable feature of personality [e.g., Pennebaker and Graybeal, 2001, Pennebaker and King, 1999] and correlates with personality traits [e.g., Holtgraves, 2011, Hirsh and Peterson, 2009, Lee et al., 2010].

If LIWC analyses are revealing of individuals’ characteristic thoughts and stable personalities and if the finding that liberals use more loyalty words turns out to be robust [i.e. it replicates], then either [a] the MFH regarding loyalty is not supported with respect to word usage; or [b] future moral foundation theorists and researchers ought to tackle the question of why liberals spend so much time criticizing loyalty when loyalty is not a foundation upon which their morality is based. More generally, determining whether there are differences in the moral languages that liberals and conservatives use informs the broader question of whether political ideology should be conceived of as a feature of personality. Evidence that liberals and conservatives use different moral languages would support the notion that ideology is personological.

To my knowledge, 10 years after the publication of Graham et al. [2009], there have been no close or conceptual replications of the moral foundation word frequency analysis. However, some studies are somewhat relevant. One study conceptually “replicated” 1the linguistic attitudinal analysis with a politically diverse set of religious and political active adults and using human coding [McAdams et al., 2008]. Other studies have assessed word frequencies within specific contexts and produced mixed evidence. For instance, when expressing opinions about stem cell research, liberals used more harm [MFH support] and fewer purity words [MFH support] than conservatives [Clifford & Jerit, 2013], and when talking about abortion or same-sex marriage, liberals used more fairness [MFH support] and fewer purity words [MFH support] than conservatives [Frimer et al., 2016, Sagi and Dehghani, 2014].

Environmental issues have produced mixed results vis-à-vis the MFH. Some studies found that liberals talked about climate change and pollution primarily in terms of harm [Feinberg & Willer, 2013; MFH support], whereas others found that liberals drew from the fairness [MFH support], authority [MFH challenge], and purity foundations [Frimer, Tell, & Haidt, 2015; MFH challenge]. And Frimer et al. [2016] found that liberals used more purity [MFH challenge] and fewer fairness words [MFH challenge] than conservatives when talking about the Keystone XL oil pipeline, implying a complete, context-specific reversal of the MFH. In sum, this research shows that the MFH is not uniformly supported across various issues and topics. Whether or not some enduring dispositional difference between liberals and conservatives exists, as the MFH posits, remains inconclusive from the scientific record. The goal of the present research is to inform that question.

The impetus for the present replication studies is threefold. First, Moral Foundations Theory has been hugely influential. According to Google Scholar, the seminal empirical paper [Graham et al., 2009] has been cited ~2500 times, and the popular book that followed [Haidt, 2012] received ~4100 citations as of October 2019. Second, the original study confirmed the MFH for four foundations and disconfirmed it for the fifth. Replication studies may shed light on whether the MFH is indeed supported when it comes to moral language. Third, there seem to be no close or conceptual published replications of the word count version of the MFH.

The goal of the present studies is to apply the “the replication recipe” [Brandt et al., 2014] to test whether the finding that liberals and conservatives use different moral languages [Table 1 of Graham et al., 2009] replicates and generalizes to other contexts. The original study reported that liberals use more harm, fairness, and loyalty words whereas conservatives use more authority and purity words.

Effect sizes and their 95% confidence intervals from the original study are shown in Table 1. The weakest ideological differences in word usage from the five foundations in Graham et al. [2009; Study 4] is |d| = 0.56 or an equivalent |r| = 0.257 [harm foundation]. Assuming that the true effect is smaller than those reported in the original, I designed studies conservatively—to have a 90% chance of detecting an effect that is half the size of the smallest effect in the original paper [|d| = 0.28; equivalent |r| = 0.139] at p 3000 words on average], I will create 1000-word segments and use the text segment as the unit of analysis. In studies in which text files that are comparatively small [ 2], I converted identically zero scores to a score of 0.001%. I chose this number because the smallest possible output of LIWC is 0.01%, which is 10 times larger than the replacement value. The conversion of 0% scores to 0.001% retains the ordinal placement of these data and means that they are retained in all analyses. I then log transformed the data to reduce or eliminate skewness [see Table S1].

While addressing the issue of skewness, this conversion created a new interpretive problem. The main goal of the present work is to replicate a study that may have not addressed non-normality before conducting analyses that assume normality. The raw-data analysis would be a close replication but psychometrically questionable, whereas the log-transformed analysis would be more analytically sound but more distant from the original. The approach I adopt is to report both analyses and note their significance and meaning in the text.

In all multilevel models, I standardize all variables [z-scores] before running analyses. This has the effect of making the unstandardized estimate into a standardized estimate and thus a measure of effect size and directly comparable across studies and contexts.

I report replication success in two ways: [a] the Effect Size Overlap Criterion is that the replication effect size estimate [with its 95% confidence interval] overlaps with that of the original [and its 95% confidence interval], and [b] the Direction and Significance Criterion is that the replication test is in the same direction as the original, and is statistically significant at p 

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