ORIGINAL RESEARCH article

Front. Polit. Sci., 08 December 2025

Sec. Comparative Governance

Volume 7 - 2025 | https://doi.org/10.3389/fpos.2025.1612506

Measurement of democratic values: a cross-country comparison with ESS round 10

  • 1. GESIS Leibniz Institute for the Social Sciences, Cologne, Germany

  • 2. German Center for Gerontology (DZA), Berlin, Germany

  • 3. University of Giessen, Giessen, Germany

  • 4. University of Marburg, Marburg, Germany

Abstract

This study examines the measurement and cross-national comparability of democratic values utilizing the Citizens’ Models of Democracy scale included in the European Social Survey round 10. Given the ambiguous understanding of psychological constructs in empirical legitimacy research, we propose a conceptualization drawing on Norris’s framework of political support to facilitate a decisive operationalization of survey indicators. Acknowledging the challenges inherent in comparative research, we emphasize the intentional differentiation between social identities, values, and attitudes as the underlying constructs of measurement. Applying this framework to the study of political support, we conceptualize democratic values as the underlying measurement of support for regime principles and empirically assess whether and how associated latent structures can be discovered. As it remains unclear whether the scale is intended to capture value dimensions or types of value holders that are consistent across countries, we employ both variable- and person-centered as well as current state of the art approaches on measurement quality and invariance. Our findings suggest a meaningful but overlapping structure of liberal, social-democratic, and populist democratic values, while the comparability across cultural contexts reveals to be hampered. Further, a meaningful structure of value-holder profiles does not occur, but rather clusters of respondents who consistently seize either most or few democratic regime principles as important. We conclude with implications for democracy research and recommendations for future empirical studies on political support.

1 Introduction

Undoubtedly, illiberal and authoritarian ideas are increasingly spreading in Western societies. However, the ongoing discussion on “the crisis of democracy” is certainly not a new one; it has been particularly revived and augmented because of far-reaching political, economic, and social changes (Mounk and Foa, 2018; Norris, 2025). In recent years, democratic backsliding, distrust in democratic decision making, and eroding satisfaction with the way democracy works has become evident not only during the COVID-19 pandemic, but especially in so-called illiberal democracies (e.g., Rupnik, 2011; Wodak, 2019; Foa et al., 2020; Lewkowicz et al., 2022; Angiolillo et al., 2024). Even before the rise of strongman leaders such as Trump, the advent of social media, or the electoral success of populist radical movements, scholars had argued that many democracies were facing or had already surpassed a critical inflection point. In light of Linz and Stepan’s (1996) conditions for democratic consolidation, there are indeed signs of a reversal in progress.

Culturally, support for liberal democracy and its underlying principles seems to erode in the respective societies, accompanied by a growing dissatisfaction with democratic governance (Norris, 2017a; Foa and Mounk, 2019; Claassen and Magalhães, 2023). Simultaneously, the resurgence of nationalism and protectionism (Mudde, 2007; Joppke, 2021) and the weakening of social cohesion and trust (Putnam, 2000; Aruqaj, 2023) further destabilize democratic rule. At the constitutional level, an illiberal transformation of the state is unfolding, with political actors increasingly circumventing democratic norms and procedures (Norris and Inglehart, 2019; Pappas, 2019; Rachman, 2022; Boese et al., 2022). And behaviorally, the growing constituency and influence of populist and authoritarian forces intensify political polarization, especially on social media (Kubin and von Sikorski, 2021; Svolik et al., 2023). This rhetoric, in turn, fosters an environment conducive to democratic backlash (Vierus and Ziller, 2025) and amplifies the risk of violent escalation, ultimately leading to direct challenges to governmental stability.

However, there is broad agreement among scholars that political regimes need supportive citizens and that a disconnect between what citizens want and what government produces is problematic (Norris, 2011; Claassen, 2020). Consequently, ongoing democratic backsliding aligned by constitutional de-consolidation poses a real risk to the stability and durability of supposedly settled democracies around the globe. As disciplines dedicated to analyzing political and social transformations, the social sciences play a crucial role in explaining democratization and its reversal, thereby helping to prevent autocratization, a process through which politics becomes increasingly repressive and power more arbitrary, typically marked by weakened executive constraints, eroding freedoms in all spheres of society, and diminishing political competition and participation. This underscores the need for robust theoretical frameworks and cross-cultural survey research assessing what citizens think about democracy, what they expect from it, and what they are willing to do to uphold it.

Research on democratic legitimacy and legitimation spans either macro- and micro-level perspectives or a combination of them (cf. Harfst and Wiesner (2024), who distinguish between internal and external measures). Macro approaches typically employ performance indicators or aggregated data to investigate developments and trends among countries (e.g., Treier and Jackman, 2008), while micro-level studies seek to explore individual beliefs and judgments toward political systems and the normative ideas underpinning them. Not surprisingly, given the diversity of methodologies and the multidimensionality of concepts in the field of research, the question of appropriate operationalization and measurement remains a central—and often contested—topic of debate (e.g., Canache et al., 2001; Linde and Ekman, 2003; Bratton et al., 2005; Magalhães, 2014; Anstötz et al., 2020; Poses and Revilla, 2022; Wiesner and Harfst, 2022; Claassen et al., 2024).

By emphasizing empirical legitimacy at the individual level, this study investigates whether citizens endorse or reject a political regime and its governmental cornerstones, conceptualized through the widely recognized umbrella term of political support. It comprises the interplay of support necessary for regimes to maintain stability with the ways in which the governed evaluate the state, its principles, institutions, and political actors. However, three key concerns persist: (a) the blurred distinction between diffuse and specific political support, (b) the ambiguous (causal) relationships among different types of support, and (c) the unclear measurement of support components and objects. As a result, many findings in this long-established field of research remain inconsistent and challenging to compare or generalize. In this study, we thereby scrutinize often-studied concepts in quantitative survey research on political support by focusing on two broader research questions:

  • First, how can measurement approaches of political support be conceptualized from a cross-cultural perspective?

  • Second, how can these measurements be empirically assessed, validated, and refined to enhance their reliability and validity across cultural contexts?

In doing so, we develop a framework of political support measurements specifically tailored to meet the needs of empirical survey research. Considering that the relevant concepts often intersect at the nexus of regime legitimacy and legitimation, we begin with a brief theoretical overview, accentuating both the normative ideas and norms of regimes, their attributed rightfulness and authority, and the public’s response to these principles of political rule and their implementation. While legitimacy is of course relevant to all forms of governance, our focus is on democratic regimes, as these uniquely derive their authority from citizen consent and accountability. In our framework, we rely on Norris’s (2011, 2017b) conceptualization of political support, which provides an empirically oriented approach that mitigates theoretical and methodological challenges while integrating the strengths of previous attempts. Additionally, we adopt a largely overlooked cross-cultural perspective on values and attitudes from social psychology to underscore our mapping of political support and its underlying measurements, emphasizing that these implicit psychological constructs lie at the core of the survey measures employed.

Unfortunately, to the best of our knowledge, open (high quality) data for European countries containing suitable and reliable indicators that capture the full spectrum of support components, as defined by Norris, are not available (cf. Booth and Seligson, 2009, for Latin America). This well-documented data gap significantly constrains the analytical leeway, making it impossible to simultaneously assess the assumed hierarchy and interrelations among the different concepts. Therefore, in the empirical section, we apply our approach to the measurement of support for regime principles and in particular of democratic values utilizing the Citizens’ Models of Democracy scale (CMoD) included in the European Social Survey (ESS) round 10. Our analysis strategy is built on two pillars, integrating both variable- and person-centered approaches. Exploratory and confirmatory methods are employed to uncover and validate latent structures of democratic values captured by the scale. Acknowledging the challenges inherent in comparative research, we investigate whether the observed cross-national response patterns reflect genuine variation in the endorsement of regime principles rather than artifacts of measurement. Our aim is to advance the rigor and applicability of theory-driven survey research on political support by adopting state-of-the-art approaches to assess measurement quality and invariance. In the conclusion, we summarize and critically reflect on the findings, culminating in three recommendations to advance cross-national research on political support and democratic legitimacy.

2 Contemporary measurement of political support

Empirical approaches on political support can be traced back to Weber’s notion of legitimacy. According to Weber (1972), regimes cannot maintain their power solely through repression and violence; instead, they require a degree of acceptance from those they govern. Following Lauth (2020, p. 833), today’s legitimacy research focuses on two main questions: “why should people obey their rulers, and why do people obey a particular political system?” This highlights the inherent interrelation between theoretical and empirical aspects of political support, demonstrating that not only the associated concepts but also the terms themselves and their usage are far from any semblance of consistency. As a result, social science has responded with broad conceptual diversity and multidimensional frameworks, alongside numerous measurement approaches and operationalizations.

In this study, we distinguish between legitimacy as a normative concept, which refers to the justification of norms and the rightfulness of regimes, and legitimation as a descriptive category that captures individual’s beliefs in the rightfulness and responsiveness of a regime (Garzón Valdés, 1988). Accordingly, political support at the individual level is understood as citizens’ expressed willingness and normative acceptance of political regimes and institutions, empirically reflected in perceived duty to obey, moral alignment, and evaluative judgments—thus constituting expressions of individual orientations and attitudes toward political authority.

2.1 Democratic legitimacy

The normative underpinnings of a democratic regime emphasize principles so desirable that citizens are expected to recognize its legitimacy. In a simplified form, this question is primarily related to political philosophy. Specifically, according to Dahl (1989) and Lijphart (1984), the relationship between responsibility and responsiveness lies at the core of democracy. Responsibility refers to the degree of accountability within the decision-making processes of a political regime. Responsiveness ensures that the decisions of a political regime adequately reflect public preferences (Lauth, 2020, p. 836). However, these two aspects of democratic rule are partly in conflict. The resolution of this contradiction is explored through various conceptual frameworks ranging from narrower to broader conceptualizations, each reflecting different strands of democracy theory (e.g., Habermas, 1992; Rawls, 1993; Peter, 2008).

Schumpeter (1942) minimalist understandings of democracy are all centered on the electoral process as a solution of both the chain of responsiveness and accountability (Bühlmann and Kriesi, 2013). Dahl (1989) extended this concept and included minimum conditions for democracy besides elections, which is commonly sub-summarized under the term liberal understanding of democracy. In practice, he suggested that democratic regimes—or in his words polyarchies—can be identified by the presence of certain key political institutions and civil liberties: (1) elected officials, (2) free and fair elections, (3) inclusive citizenship, (4) the right to run for office, (5) freedom of expression, (6) alternative information, and (7) associational autonomy. All these aspects are rooted in the rule of law, which asserts that no one, including those in power, is above the law (republicanism), while also ensuring that certain civil freedoms remain inviolable (liberal guarantee).

Some scholars argue that these criteria are not sufficient and have developed a broader understanding of democracy. For instance, another important normative principle associated with democratic rule is the concept of procedural and distributive justice. According to Buchanan (2002), political systems gain moral authority to exercise power not merely through democratic procedures, but also through a reasonable degree of justice in the distribution of material resources, the protection of fundamental human rights, and inclusive political participation. Since democracies are not necessarily just, their legitimacy would have to be questioned. Consequently, Diamond and Morlino (2005) argued that political equality could only be achieved through a certain degree of social and economic equality. Other refinements of democracy theory include deliberative and direct democratic elements (e.g., Pateman, 1970; Barber, 1984; Habermas, 1992). For instance, direct democracy encompasses mechanisms such as initiatives or referenda, which allow citizens to propose specific issues for the political agenda and challenge decisions made by those in power. These elements seek to enhance the direct involvement of citizens in the political process, thereby expanding the scope of democracy beyond representative systems.

It is therefore important to recognize the pluralistic nature of democracy theory and its wide spectrum. At one end, core liberal elements shape any (supposedly) democratic system. At the other end, broader approaches advocate for greater flexibility and variation in how democracy is understood and institutionalized. This distinction serves as a crucial heuristic for navigating the diversity of democratic models and their translation into related measurements intended to capture democratic legitimacy.

2.2 Democratic legitimation

In contrast, we distinguish attitudinal components that reflect citizens’ legitimation of political rule on the micro-level, drawing primarily on theory-building in political sociology and political culture research. Typically, the focal point is—according to Weber’s Legitimitätsglaube (faith in legitimation)—the relationship between rulers and the ruled, and the extent to which the latter regards the former’s authority as justified (Weber, 1972).

Both the work of Almond and Verba (1963) and Easton’s conceptualization (Easton 1965, 1975) have become widely recognized as benchmarks, significantly shaping research on orientations toward the nation-state, its principles, institutions, and political actors. Almond and Verba (1963) distinguished four objects of political orientation at a descriptive level: (a) the political system as a whole, including its fundamental values and institutions, (b) participatory processes (input dimension), (c) the performance of the political system (output dimension), and (d) the self as a political actor.

Another influential concept introduced by Easton (1975) is the distinction between diffuse (or generalized) and specific support. While the former is characterized by long-term approval of the fundamental principles of political authority and abstract feelings toward the nation-state, the latter is based on the short-term evaluation of the performance of elected and appointed officials responsible for making and implementing political decisions. Both generalized and specific support—understood as a continuum rather than a dichotomy—are integral to the legitimation process of a political system.

Especially since the 1990s, Easton’s concept, originally open to all forms of government, has been further differentiated, with research showing a clear shift toward democratic systems as the primary focus (e.g., Buchanan, 2002). In addition to numerous empirical studies that address the operationalizability of certain components by using survey-based data to analyze different conceptualizations (cf. Kornberg and Clarke, 1992; Weatherford, 1992; Fuchs et al., 1995; Klingemann, 1999; Dalton, 2004; Anderson et al., 2005; Gilley, 2006; Westle, 2007; Booth and Seligson, 2009), conceptual-theoretical work that further developed the original conceptualization are particularly worth mentioning here. Emphasis is placed on the differentiation of possible support motives (e.g., Fuchs, 1989) or the specification of support objects and types (e.g., Westle, 1989).

Furthermore, the conceptualization has been extensively reinterpreted as a hierarchical continuum, as seen in works by Diamond (1999) and especially Norris (1999, 2001), thereby offering a more nuanced account of its multidimensional nature. In this study, we rely on a conceptualization as proposed by Norris (2011, p. 23 and following) which we consider well-suited to the multidimensionality of democratic legitimation and combines the strengths of other attempts. The approach facilitates a decisive separation of political understandings, expectations, and behaviors relating to the normative ideas of democracy, institutional arrangements and their outcomes, therefore moving beyond the Eastonian dichotomy. It allows, for example, the exploration of why individuals may express strong commitment to democratic ideals while simultaneously exhibiting skepticism toward the political institutions that represent them and the politicians responsible for implementing policy. Most notably, the framework disaggregates political support into five analytically distinct components: (1) national identity, (2) regime principles, (3) regime performance, (4) political institutions, and (5) incumbent officeholders. This differentiation of support components is comparatively straightforward to operationalize, making the framework particularly well-suited for survey research.

2.3 Values and attitudes in cross-cultural research: a conceptual overview

It is important to emphasize that citizens’ notions of democratic legitimacy, that is, their perceptions of normative ideals, which may be established, unfulfilled, or subject to change in the prevailing political-constitutional order, and the processes of regime legitimation, both settled at the micro level, are closely intertwined and should not be viewed in isolation. They are inherently evaluative, reflect different degrees of support or disapproval toward political entities and principles, and operate at both conscious and unconscious levels. Nevertheless, many empirical studies implicitly conceptualize the multitude of surveyed indicators related to political support as a psychological orientation (e.g., Norris, 2011, p. 20), yet they often do so without explicitly considering the conceptual distinctions between various forms of values and attitudes. In our theoretical reflection, we aim to address these shortcomings by drawing on social psychology, which has extensively studied these concepts for decades (for a comprehensive overview, see Hewstone and Stroebe, 2020).

Values are conceptualized as normative ideals that are highly generalized and serve as fundamental guiding principles across broad areas of human life. Prominent theories posit that both the content of values and their structure exist across all cultures, as seen in the works of Inglehart (1971, 1997), Rokeach (1973), and Schwartz (1992). According to Rokeach (1973, p. 5), values are “enduring beliefs that a specific mode of conduct or end-state of existence is personally or socially preferable to an opposite or converse mode of conduct or end-state of existence.” Schwartz, for example, defines values as “desirable, transsituational goals, varying in importance, that serve as guiding principles in the life of a person or other social entity” (Schwartz, 1994, p. 21). Whereas Rokeach proposed a classification of value holders based on ranking scales, adopting a more person-centered perspective, Schwartz advocated the use of rating scales, which imply a linear relationship between adjacent values, thereby supporting a more variable-centered approach. Regardless, values tend to remain relatively stable over time as they are deeply internalized, allowing little room for questioning or change (Maio and Olson, 1998).

Attitudes are conceptualized as “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor” (Eagly and Chaiken, 1993, p. 1). In addition, the three-component model of attitudes (Rosenberg and Hovland, 1960; Eagly and Chaiken, 1993), also called the multi-component model (Zanna and Rempel, 1988), postulates that an attitude does not only contain feelings, which is the affective component, but also a cognitive dimension, which reflects attitude-relevant beliefs and finally a behavioral component. Notably, the behavioral attitude component assumes that people deduce their attitudes toward an object by recalling their past behavior relevant to the issue (Bem, 1972; Olsen, 1990). In principle, the behavioral dimension can also consist of intended behavior or hypothetical scenarios, which are empirically queried via item formulations. Therefore, it is also called the conative component of an attitude. Moreover, behavioral evaluations of an attitude object can be part of this dimension. Heyder et al. (2022, p. 4) suggested that, unlike values, attitudes might be classified as either generalized, which can be summarized into ideological concepts (Jost et al., 2008), or specific, directed at concrete attitude objects (Prislin and Ouellette, 1996).

Furthermore, with respect to the central aspect of evaluation, there are two basic views on the structure of attitudes. The one-dimensional and the bi-dimensional perspective (Maio and Haddock, 2015). The first is a view that all attitudes can be represented on a single bipolar evaluative dimension that extends from a maximum negative endpoint to a maximum positive endpoint and has a neutral center. The second postulates that two unipolar dimensions are necessary (positivity and negativity) to depict attitudes exhaustively. In most cases, Likert scales (Likert, 1932) are used to measure the intensity and direction of attitudes, following the first conception mentioned above.

Compared to attitudes, values serve as fundamental principles that stipulate what is considered right or wrong, whereas attitudes are more concrete to particular attitude objects and tend to be more flexible and context-dependent. In other words, values function as moral or ethical prescriptions that shape behavior more rigidly than attitudes (Maio et al., 2003). Accordingly, most researchers expect that changes at higher levels of abstraction (values) have a stronger influence on lower levels of abstraction, such as generalized and specific attitudes. For instance, Rokeach (1973) hypothesizes that a relatively small set of values can influence a larger set of attitudes.

2.4 A framework for the measurement of political support

Building on our review of the relevant literature, we propose that common measurements of political support, drawing on Norris’s support continuum, can be situated at the intersection of two key analytical axes: (1) the processes of democratic legitimation at the micro level, which necessarily refer to the normative cornerstones of a political regime that claims legitimacy for itself, and (2) the underlying psychological construct that characterizes the nature of the measurement object. Specifically, we differentiate whether support components are conceptualized as social perceptions of group membership at a high level of abstraction (social identities), as idealistic guiding principles (values), or as cognitive, emotional, or behavioral evaluations of an attitude object (attitudes).

Following the value–attitude–behavior (VAB) model proposed by Kahle (1980, 1983, see also Homer and Kahle, 1988), we argue that social identities provide individuals with a foundational sense of political belonging, shaping the interpretation of values and influencing the formation of attitudes. They function as a relatively stable lens through which political meaning is filtered and expressed. Values, in turn, represent deeply internalized and enduring orientations that inform more specific and situationally responsive attitudes, which ultimately guide (political) behavior. As in Norris’s approach, our framework does not posit a strict unidimensional continuum reducible to a single latent structure. Rather, the components are analytically and empirically distinct—yet interrelated—and can be broadly ordered from abstract and stable (identities and values) to more concrete and evaluative (attitudes), thereby reflecting both the temporal depth and hierarchical organization of political support:

(1) National belonging (Norris, 2011, p. 25) may be defined as a form of social identity (Tajfel and Turner, 1979, 1986) and arguably represents the most uncontested component of political support, grounded in a coherent theoretical and a well-established empirical tradition. Often operationalized through indicators such as national attachment, pride, or patriotism, it represents not only a form of political allegiance but also an expression of in-group favoritism, and, in its more intensified form, national idealization (i.e., nationalism), as conceptualized in social identity theory (Blank and Schmidt, 2003; Roccas and Berlin, 2016). We argue that Norris’s conception aligns with these assumptions, emphasizing that group-level characteristics help individuals to categorize and structure their social environment. As a result, widely used operationalizations of national belonging capture not only positive or emotional attachment but may also imply elements of outgroup devaluation. At the national level, this component primarily reflects citizens’ social identity in relation to the perceived dēmos—that is, the political community or population regarded as entitled to legitimate authority within the regime.

(2) Support for regime principles can be understood as values that reflect individual guiding orientations within a given political-constitutional context (Norris, 2011, p. 26). However, this component remains conceptually ambiguous, as democracy is defined in multiple and often contested ways. Diverging views on which features are considered essential contribute to ongoing debates about legitimacy crises in established democracies (van Ham et al., 2017). Empirically, approaches vary: Some adopt minimalist indicators, such as abstract support for the idea of democracy, while others employ broader operationalizations that incorporate constitutional or institutional features and perceived gaps between democratic ideals and reality. Substantively, support for democratic principles can be distinguished by the values they prioritize. For instance, liberal democratic values emphasize the rule of law and the protection of individual rights, egalitarian values stress equality, pluralist values reflect commitments to societal diversity, and populist values highlight the need for increased citizen participation.

(3) Evaluations of regime performance refer to citizens’ assessments of the perceived quality of democratic governance (Norris, 2011, p. 28) and encompasses affective, cognitive, and behavioral attitudes. They reflect public perceptions of how effectively particular democratic procedures and institutions deliver fair representation, accountability, and policy responsiveness. Empirical research often relies on the widely used satisfaction with democracy (SWD) item as a proxy for generalized affective attitudes toward the prevailing regime. However, researchers have raised concerns about the conceptual ambiguity of the SWD measure, arguing that it conflates multiple dimensions of political evaluation (Poses and Revilla, 2022). In contrast, more specific survey items frequently focus on the regime’s performance in safeguarding rights, ensuring accountability, promoting economic welfare, or addressing societal needs, thus primarily capturing cognitive evaluations. Scholars increasingly emphasize the need for refined and valid measurement approaches to advance the empirical assessment of regime performance (Singh and Mayne, 2023).

(4) Confidence in regime institutions and approval of political authorities reflect a combination of attitudes and social cognitions shaped by (collective) experiences, historical trajectories, and sociopolitical contexts. These attitudes emerge through processes of knowledge formation, activation, and diffusion, and vary depending on the level of abstraction or specificity of the evaluated object. Confidence in regime institutions typically refers to the legislative, executive, and judicial branches, as well as other core state institutions (Norris, 2011, p. 29). Conceptually, it remains contested whether this dimension reflects an affective attitude component, akin to interpersonal or social trust (Putnam, 2000), or whether it constitutes a more abstract form of systemic confidence (Luhman, 1989). As a result, empirical findings remain inconclusive as to whether political trust primarily signals diffuse or specific support, and whether it is essential for regime stability (e.g., Westle, 1999; Levi and Stoker, 2000; Denters et al., 2007; Harteveld et al., 2013). Approval of incumbent officeholders, by contrast, relates to citizens’ attitudes toward specific political leaders, parties, and public officials, including those occupying roles within the executive and legislative branches (Norris, 2011, p. 30). Yet debates persist regarding whether such approval is driven by short-term performance assessments, ideological proximity, or deeper trust in institutional authority. Moreover, it remains unclear to what extent citizens distinguish between political offices as formal institutions and the individuals who occupy them. Accordingly, common survey measurements tap into perceptions of trustworthiness, credibility, and accountability toward both institutional and personal representations of state authority.

3 Measuring support for regime principles

The empirical section demonstrates the necessary steps for conducting a rigorous scale validation in a cross-national context, an essential prerequisite for meaningful comparative and inferential analyses considering possible drivers and consequences of regime evaluation and support. In this study, we focus on regime principles as the object of support and, within our theoretical framework encompassing various psychological orientations, specifically on democratic values. Recent debates on the erosion of societal value consensus in liberal democracies highlight their importance for regime stability, functioning, and resilience. This view is not new, as Easton (1965) already hypothesized that the legitimacy of a regime depends on how closely the political order and its values align with citizens’ personal moral principles and beliefs.

3.1 State of empirical research

Unsurprisingly, the body of empirical literature that addresses support for regime principles cross-culturally—at least from our perspective—is vast and multifaceted, highlighting both the theoretical and methodological challenges within this research domain. On the one hand, a remarkable diversity can be recognized in the use of terminology (see also Osterberg-Kaufmann et al., 2020, p. 306). For instance, while Kirsch and Welzel (2019) employ measurements fielded in the World Values Survey (WVS) to define liberal and authoritarian notions of democracy, Chapman et al. (2024) use the same scale to assess a typology of democratic conceptualization.

On the other hand, however, a major shortcoming is the lack of analytical sensitivity to cultural and national differences when comparing permutations of regime support. Examples include studies applying Mokken scale analysis with ESS data to examine the structure of Europeans’ views of democracy (Kriesi et al., 2016; Kriesi, 2025), single item responses as concepts of democracy with WVS data (Zagrebina, 2020), or non-compensatory composite scores to measure solid democratic support with Americas Barometer data (Moncagatta et al., 2023) and citizens’ democratic knowledge with WVS data (Wegscheider and Stark, 2020). Other studies, in contrast, calculate mean indices for Europeans’ expectations of democracy with ESS data (Heyne, 2018) or additive indices to capture core dimensions of democracy with EVS data (Seyd, 2020).

Yet relatively few studies explicitly stress systematic biases to enhance a broader comprehension of the underlying measurements among diverse cultural contexts. For instance, multi-group confirmatory factor analysis (MGCFA), Bayesian factor models, and alignment have been used to assess the comparability of liberal and authoritarian notions of democracy with WVS data (Sokolov, 2021; for Germany, see Jacobsen and Fuchs, 2020) and the evaluation of democracy with ESS data (Quaranta, 2018). Additionally, latent class analysis (LCA) has been applied to explore patterns that democracy takes in the minds of citizens with WVS data (Davis et al., 2020) or democratic ideals held by citizens with ESS data (Oser and Hooghe, 2018).

While this overview is neither exhaustive nor representative, it provides insight into survey-based approaches to assessing support for regime principles. Apparently, empirical research in this field reveals methodological diversity and is characterized by competing terminologies, insufficient conceptual differentiation of the constructs under study, and measurement strategies ranging from rudimentary to complex. Scholars predominantly adopt either an emic approach, which emphasizes country-specific explanations for regime support, or an etic approach, which seeks to develop universal theories incorporating cultural dimensions (Miller-Loessi and Parker, 2006). However, considering the state of the literature, fundamental ambiguities remain regarding the often-neglected questions (a) of what precisely constitutes the underlying psychological construct (b) and whether related measurements employing multiple indicators should be conceptualized as dimensions or typologies.

3.2 Democratic values in the ESS round 10

The “Europeans’ understandings and evaluations of democracy” module, included in the ESS round 10 and initially introduced in round 6 (see Ferrín and Kriesi, 2016), offers a valuable opportunity to study democratic values in times of turmoil. Surveys were conducted between September 2020 and September 2022 in 31 European countries. The national samples are considered representative for all persons aged 15 and over residing in private households. In round 10, nine countries transitioned from face-to-face interviews (CAPI) to self-completion modes (CAWI and PAPI) due to the impact of the COVID-19 pandemic, which also extended the fieldwork period (see Comanoru and Fitzgerald, 2025). Response rates varied significantly by survey mode, ranging from 20.9% in the United Kingdom to 72.8% in the Czech Republic. In our analysis, we mitigate potential biases from mode effects by restricting the sample to the 22 countries that administered the surveys as face-to-face interviews (ESS round 10, 2023a). Comprehensive details on sampling, sample sizes, questionnaires, and data access are available in the ESS Data Portal (ESS round 10, 2023b).

The module includes the CMoD scale, designed to measure democratic views by asking respondents to indicate the importance of a carefully selected set of elements drawn from theoretical models of democracy (Kriesi et al., 2016, p. 67). The conceptualization proposes five dimensions referring to the (1) liberal model which emphasizes procedural aspects, including minority rights, the rule of law, competitive elections, government accountability, and press freedom. The (2) social-democratic model expands on this by stressing income equality and protection from poverty. The (3) direct model focuses on referendums and direct citizen participation over representation. In contrast, the (4) populist model challenges the liberal approach by promoting an anti-elitist, anti-pluralist perspective, asserting that the people’s will should override constitutional safeguards, while the (5) multilevel model focuses on national sovereignty in decision-making processes (see Table 1).

Table 1

Item stemQuestion wording: How important do you think it is for democracy in general…Models of democracy*
natgovthat key decisions are made by national governments rather than the European Union?Multilevel model
willthat the will of the people cannot be stopped.Populist model
elitethat the views of ordinary people prevail over the views of the political elite.
incomethat the government takes measures to reduce differences in income levels?Social-democracy model
povertythat the government protects all citizens against poverty.
accountthat governing parties are punished in elections when they have done a bad job.Liberal model
lawthat the courts treat everyone the same.
minoritythat the rights of minority groups are protected?
mediathat the media are free to criticize the government.
partythat different political parties offer clear alternatives to one another.
fairthat national elections are free and fair.
referendthat citizens have the final say on the most important political issues by voting on them directly in referendums.Direct model

Indicators of the CMoD scale in the ESS round 10.

*Dimensions based on Kriesi et al. (2016). Responses were measured on an 11-point scale from 0 (“Not at all important for democracy in general”) to 10 (“Extremely important for democracy in general”).

According to the underlying rationale of the CMoD, democratic principles and values would be assumed to be internalized or at least known and regarded as important or not by the governed (Kriesi, 2013). Consequently, support for a given regime and its realized or absent features would depend on the perceived (in)congruence between the status quo and individual notions of democracy. In our point of view, a key aspect here is the term importance, which conceptually as well as semantically follows the abovementioned classical works on the measurement of value orientations. Emphasis on individually perceived importance in combination with certain aspects of democratic rule suggests that the scale aims to capture enduring, deeply held guiding principles that transcend specific situations, reinforcing its alignment with the measurement of democratic values. While ESS items begin with a personal pronoun (“How important do you think…”), it should be noted that they also address democratic ideals at a generalized level (“…for democracy in general”). We argue, however, that the items unequivocally evoke individual value preferences, as it prompts respondents to reflect on how democracy should ideally be composed, based on their personal convictions.

Nevertheless, the theoretical foundations of the CMoD scale remain ambiguous concerning whether it is intended to capture a priori defined, more or less correlated value dimensions (variable-centered) or profiles, that is, relatively stable group patterns of individuals sharing similar configurations of democratic values (person-centered). Both methodological approaches would be plausible within a pluralist framework of democracy theory, given the diversity of political systems and citizens’ varying notions of an ideal regime. To illustrate this briefly, liberal and social-democratic elements may jointly form the core of democracy in some cases, while those of the social-democratic and populist model may melt together in others. Moreover, citizens may desire absent regime features like press freedom, while longstanding regime principles, such as minority rights, receive less attention. Conversely, where press freedom is widely seen as granted, importance may lie in unfulfilled or threatened minority rights as a crucial regime cornerstone. Consequently, it is uncertain whether latent value factors or profiles should be regarded as country-specific phenomena related to political support (emic perspective) or as cross-culturally equivalent and thus comparable (etic perspective).

3.3 Analysis strategy and methods

Based on the premise that the CMoD scale is designed to measure democratic values across different sociopolitical and cultural contexts, yet both person- and variable-centered approaches may be suitable for exploring latent structures, our empirical analysis adopts an exploratory and circular approach, as illustrated in Figure 1. The guiding question, succinctly summarized by Horn and McArdle (1992, p. 117), is “whether under different conditions of observing and studying phenomena, measurement operations yield measures of the same attribute.” Since the principal investigators neither explicitly specify an appropriate analytical approach nor clarify whether the instrument and its underlying indicators should meet cross-country equivalence, eight measurement hypotheses were formulated and addressed step by step, each accounting for different assumptions about the occurrence and comparability of value factors and profiles (see Table 2). They reflect recommendations derived from the current literature on best practices for analyzing survey data with reflective indicators (Davidov et al., 2018; Leitgöb et al., 2023) and offer a systematic framework for assessing whether the patterns identified are consistent and comparable. Strictly speaking, only when the assumptions underlying the analysis are met, the observed differences can be interpreted as genuine variations in individuals’ support for regime principles, allowing for meaningful comparisons that extend beyond the investigation of latent structures emerging within each individual country.

Figure 1

Table 2

HypothesesMeasurement implicationsApplied methodsEvaluated parameters
H1: Within each country, exploratory factor analysis indicates the same number of factors.Model identificationEFAEigenvalue, Factor loadings, AIC, BIC, RMSEA, CFI
H2: Within each country, exploratory profile analysis indicates the same number of profiles.Model identificationExploratory LPASample-size adjusted BIC, Entropy, Density
H3: The structure of latent value factors appears to be meaningfully comparable across countries.Configural MIMGCFACFI, TLI, SRMR, RMSEA
H4: The factor loadings of the latent values dimensions are invariant across countries.Metric MIMGCFA, AlignmentΔCFI, ΔTLI, ΔSRMR, ΔRMSEA, Number of invariant parameters
H5: The intercepts of the latent value dimensions are invariant across countries.Scalar MIMGCFA, Alignment, BAMIΔCFI, ΔTLI, ΔSRMR, ΔRMSEA, BRMSEA, BCFI, BTLI, Number of invariant parameters
H6: The structure of latent value profiles appears to be meaningfully comparable across countries.Heterogeneous MIMGLPAaBIC, AIC, LMR, Entropy
H7: The relationships between the items and the latent value profiles are invariant across countries.Partial homogeneous MIMGLPAaBIC, AIC, LMR, Entropy
H8: The average item-values of a latent value profile are invariant across countries.Structural homogeneous MIMGLPAaBIC, AIC, LMR, Entropy

Summary of the measurement hypotheses.

MI = Measurement invariance, EFA = Exploratory factor analysis, LPA = Latent profile analysis, MGCFA = Multi-group confirmatory factor analysis, BAMI = Bayesian approximate measurement invariance; MGLPA = Multi-group latent profile analysis.

Our analysis begins with an exploration of response distributions using the resquin package (Kraemer et al., 2024) in R Studio (RStudio Team, 2020), as we are interested not just in individual item performance but in overall response behavior across the full scale. This preliminary step helps assess whether latent variable models capture substantive constructs rather than being distorted by systematic response styles. Consequently, before estimating global results, we first investigate the underlying latent structures within countries, addressing the first two measurement hypotheses (H1 and H2). To this end, we conduct exploratory factor analysis (EFA) using lavaan (Rosseel, 2012) to determine the number of meaningful latent dimensions and their associated indicators. To improve interpretability, Oblimin (oblique) rotation is applied, allowing for correlated factors. Furthermore, we conduct latent profile analysis (LPA) in Mplus 8.4 (Muthén and Muthén, 2017) to identify response-based profiles at the individual level.

To compare the latent structures discovered cross-culturally, we employ multi-group confirmatory factor analysis (MGCFA) in Mplus to assess factorial measurement invariance (MI; H3–H5). Accordingly, we first investigate whether the empirically derived factor structure, which appears reasonable from our perspective, holds across countries (configural MI). Second, we test whether factor loadings could be constrained as equal (metric MI), a prerequisite for comparing structural associations. Third, we assess whether scalar measurement invariance could be secured, which would allow for meaningful comparisons of latent means.

Although MGCFA is the predominant approach in testing exact measurement invariance, previous research shows that survey data often do not support strict model assumptions across many groups (Marsh et al., 2018). Therefore, supplementing our analysis, we apply alignment optimization in Mplus (Asparouhov and Muthén, 2014), an approximate and more flexible approach that allows up to 25 percent of model parameters to be non-invariant while still yielding trustworthy results. Additionally, we employ Bayesian approximate measurement invariance (BAMI) in Mplus (Muthén and Asparouhov, 2012), which allows small parameter variances between groups, thereby typically improving the cross-country comparability of latent means.

Moreover, we apply multi-group latent profile analysis (MGLPA) as a special application of mixture modeling with Mplus to assess the comparability of the initially discovered profile solution (Van Lissa et al., 2023). Unlike MGCFA, this approach assumes a categorical latent variable that represents the distribution of individuals sharing similar (dis-)approval patterns over all indicators of the CMoD. Here, measurement invariance relates to assumptions (H6–H8) on the occurrence of profiles across countries (heterogeneity), the similarity of item relationships within profiles (partial homogeneity), and the comparability of average item values across latent profiles (structural homogeneity).

All analyses using Mplus were retrieved with the MplusAutomation package in R Studio (Hallquist et al., 2024). For data manipulation and visualization, aimed at facilitating the interpretation of complex statistical analyses, we used the packages tidyverse (Wickham et al., 2019), tidyLPA (Rosenberg et al., 2018), here (Müller and Bryan, 2020), glue (Hester and Bryan, 2024), sf (Pebesma and Bivand, 2023), gt (Iannone et al., 2025), and a shapefile of European countries (Sevdari and Marmullaku, 2023).

Full information maximum likelihood (FIML) was used in all exploratory and confirmatory models as the estimation procedure to account for item non-response. In all group models, Belgium (selected alphabetically first) serves as the reference group to ensure result comparability. For factor models estimated using maximum likelihood (ML), the following cut-off criteria are applied to assess acceptable model fit: a comparative fit index (CFI) above .90, a Tucker-Lewis index (TLI) above .90, a root mean square error of approximation (RMSEA) below .06, and a standardized root mean square residual (SRMR) below .08 (Hu and Bentler, 1999). To evaluate nested models, we followed the recommendations by Chen (2007), considering ΔCFI/TLI below .01, ΔRMSEA below .015, and ΔSRMR below .03 as indicators of measurement invariance. In the Bayesian analyses, we test multiple prior values (.05–.0001) and visualize outcomes to assess latent mean stability (Arts et al., 2021). Prior selection can affect model fit, particularly the posterior predictive p-value (PPP), which may be less reliable with large samples (Asparouhov and Muthén, 2020). As values above .05 generally indicate acceptable fit, we also report Bayesian fit indices (BRMSEA, BCFI, BTLI) using similar cut-offs values. Given the extensive data with 22 country samples, the Gelman-Rubin convergence criterion was relaxed to .10 instead of .05. For mixture models focusing on latent profiles, model evaluation is commonly based on a combination of fit indices (Arts et al., 2021) and interpretability of the classes and their boundaries. We rely on the Bayesian information criterion (BIC), the Akaike information criterion (AIC), sample-size adjusted BIC (SABIC), entropy, and the Lo–Mendell–Rubin test (LMR) to assess model fit.

3.4 Descriptive analysis of response patterns and quality

Figure 2 presents density and box plots, offering a comprehensive overview of distributional response indicators, including the mean, median, standard deviation across multiple items per respondent, and the Mahalanobis distance. An average standard deviation of 1.38 across all items suggests that individual responses typically vary by less than one and a half points from the overall mean. Respondents with a Mahalanobis distance above 5 exhibit highly divergent response patterns, though such cases are rare, as indicated by the third quartile remaining below this threshold. These outliers may signal potential data quality concerns. The mean response of 8.23 across all indicators of the CMoD suggests a distribution skewed toward high approval. With 50% of responses falling below approximately 8.6, half of the respondents provide values up to 9 for at least half of their answers. Thus, central tendency measures indicate a clustering of responses around 8 and 9, reflecting respondents’ inclination toward strong agreement.

Figure 2

Response style indicators, including midpoint response style (MRS), acquiescence response style (ARS), and extreme response style (ERS), reinforce this pattern, as shown in Figure 3. On average, respondents’ rate 10.5 out of 12 items as highly important (ARS). Additionally, respondents select extreme responses (ERS) for an average of about 5 items, typically rating them as extremely important. Notably, half of the sample did not choose a middle option. While the CMoD scale is unipolar, these tendencies highlight potential problems for model estimation: Many respondents assign the highest importance to most indicators. However, when examining straightlining—providing identical answers to items in a battery of questions using the same scale—we find that only 7.98% of respondents exhibit this behavior, suggesting that the scale does not primarily encourage careless responses.

Figure 3

3.5 Exploratory analysis of latent structures

To explore the latent structures and assess both cross-cultural consistency and deviation, we conducted country-wise EFA, testing factor solutions ranging from one up to five factors, what aligns with the five models of democracy that the CMoD is designed to capture theoretically. Based on eigenvalues (Kaiser-Guttman criterion) and scree plot analysis (see Figure 4), a common three-factor solution was uncovered in most countries, explaining a substantial share of the variance. However, this dimensional structure did not hold in Bulgaria, Greece, Croatia, Hungary, Ireland, Italy, Lithuania, Montenegro, North Macedonia, and Slovakia, where, at first glance, a two-factor solution appeared to provide a better fit to the data.

Figure 4

Subsequently, a global EFA employing the three- and two-factor solution was conducted on pooled data to assess its cross-country validity. As robustness check, we repeated the analysis on two separate subsets: one comprising only countries where the three-factor structure did not emerge and the other including all remaining countries. Additionally, we tested a two-factor solution for both groups to further validate the findings. The suitability of the number of factors extracted was supported by a significant Bartlett’s test of sphericity across all six models. A comparison of the three-factor and two-factor solutions—regardless of whether the Kaiser-Guttman criterion was applied—consistently showed superior model fit for the three-factor solution (see Appendix A). Regarding the first measurement hypothesis, H1, a three-dimensional structure appears empirically viable across all countries, albeit with certain limitations that will be addressed later.

However, contrary to the CMoD’s theoretical rationale, no clear empirical distinction emerges between the direct-democratic and populist models. Instead, they form a unified dimension (see Figure 5), with strong factor loadings (>.70) for unrestricted majoritarian rule and elite criticism, a solid loading (.51) for national political sovereignty, and a modest but acceptable loading (.45) for direct democracy. This pattern persists even in countries where the EFA did not support a three-factor solution. Notably, despite Norway’s non-EU status, the indicator on subsidiarity still loads satisfactorily. Since this dimension encapsulates key aspects of populism as a conception of political rule—namely, the belief that politics should directly reflect the unified will of the “true people,” often in opposition to elites and perceived institutional constraints—we label it populist democratic values.

Figure 5

Both indicators addressing material equality (.91) and redistribution (.67) in democracy exhibit satisfactory factor loadings on a factor we label social-democratic values, following Ferrín and Kriesi (2016). Similarly, items concerning the rule of law (.56), minority rights (.56), free media (.69), party pluralism (.67), and fair elections (.82) load convincingly onto a single factor representing liberal democratic values, elements that many theorists consider as minimal criteria for liberal democracy. Vertical government accountability (.30) falls below the threshold for sufficient validity and exhibits substantial cross-loadings in the pooled-data analysis, suggesting that it might represent a regime feature associated with support for democracy more broadly. Country-level analyses revealed outliers that affected the average validity coefficients; however, the indicator was retained in the measurement model due to its theoretical relevance and its satisfactory associations with the liberal democratic values factor in most countries.

To apply the person-centered approach empirically, we conducted an LPA with model solutions ranging from 1 to 11 classes, reflecting the total number of indicators in the CMoD. The results indicate that model fit improved with an increasing number of profiles, as shown by decreasing sample-size adjusted BIC values (see Figure 6), particularly between the one- and two-profile solutions. While BIC and AIC values continued to decline for higher-profile solutions, entropy values generally dropped between the second and third profile, suggesting greater classification accuracy for the two-profile solution. Exceptions include Hungary, Iceland, Italy, Portugal, Slovenia, and Finland, where the suitable number of profiles is varying to some extent (see Appendix B). Additionally, the substantive meaning of latent profiles was a key factor in model selection. Balancing statistical fit indices with theoretical interpretability, we determined that a two-profile solution best fits the data.

Figure 6

Figure 7 illustrates the latent profile distributions across the indicator scales. One class predominantly consists of respondents who consistently selected high importance values (i.e., 8, 9, and 10), while the other comprises individuals who avoid extreme response categories. This pattern is moderated for items with highly skewed distributions—such as fair elections and rule of law—where agreement is relatively high across both classes. Given the potential influence of measurement artifacts, particularly due to skewness because of extreme acquiescence, we refrain from interpreting this as indication of distinct types of regime supporters (e.g., strong vs. weak liberal democrats; cf. Kriesi et al., 2016; Kriesi, 2025). Rather, the findings suggest that respondents tend to evaluate all regime features either as uniformly important (acquiescence profile) or not (nuanced profile), without meaningful patterns of value-holder groups based on the proposed models of democracy. Still, in line with measurement hypothesis H2, the results indicate a consistent number of latent profiles across countries.

Figure 7

3.6 Confirmatory analysis of latent structures

Following the establishment of the latent structures, we proceeded with confirmatory analyses. Model fit indices for the MGCFA are reported in Table 3 (see Appendix C for country-level CFA results). The configural model indicates a moderate fit, with a CFI exceeding .92 and TLI above .90, although RMSEA falls slightly below the cut-off. When comparing the configural and metric model, ΔSRMR surpasses the .015 threshold, and ΔCFI falls just below the .01 cut-off value. In contrast, ΔTLI and ΔRMSEA remain within acceptable limits. Given that CFI and TLI both reach .90, the metric model is acceptable under lenient fit criteria, whereas the scalar model is clearly rejected.

Table 3

Modeldfp-valueCFI (Δ)TLI (Δ)SRMR (Δ)RMSEA (Δ)90% CI lower90% CI upper
Configural15,347.121,1220.925.903.051.086.085.088
Metric17,933.282,586.181,3110.912 (−.013).903 (.000).082 (+.031).086 (.000).085.087
Scalar28,141.7910,208.511,5000.859 (−.051).864 (−.039).098 (+.016).102 (+.016).101.103

Model fit for the MGCFA.

N = 37,464. χ2 = Chi-square statistic, df = Degrees of freedom, p-value = Significance of Chi-square difference test, CFI = Comparative fit index, TLI = Tucker-Lewis index, SRMR = Standardized root mean square residual, RMSEA = Root mean square error of approximation, 90% CI = 90% Confidence interval for RMSEA.

To examine the impact of different measurement assumptions on model estimation and data fit, we plotted the factor loadings for each item across all countries (see Figure 8). Substantial variation in loadings across models with increasing constraints may indicate problems of model misspecification. For the populist democratic values dimension, the items referring to majoritarian rule and elite criticism exhibit strong and consistent factor loadings across countries, with only minor deviations. In contrast, the items representing direct citizen participation and, in particular, subsidiarity demonstrate weaker and less stable measurement properties, as indicated by low reliability coefficients. The social-democratic values factor shows satisfactory and stable loading patterns across all countries. For the liberal democratic values dimension, the picture is more complex. As noted in the exploratory analysis, the indicators for vertical accountability and party competition reveal insufficient cross-cultural measurement quality. Furthermore, the item on minority rights falls below acceptable thresholds in Bulgaria and Czech Republic. By contrast, the remaining items exhibit satisfactory factor loadings. Nonetheless, according to these findings, imposing exact equality constraints across all groups may be overly rigid.

Figure 8

Given that even minor cross-country differences can lead to the rejection of substantively meaningful models, we extended our analysis using approximate measurement invariance (MI) approaches, which relax strict equality constraints while preserving the comparability of latent structures. Table 4 presents the results of alignment, distinguishing between invariant and non-invariant parameters. Of the 264 estimated factor loadings in the three-dimensional model, 78 were identified as non-invariant, with substantial variation across indicators. For example, only two loadings were non-invariant for the will of the people item, while up to 12 parameters (54.5%) were non-invariant for the referendum item. A higher degree of non-invariance was observed for the intercepts, with 145 identified parameters. In total, 223 out of 528 parameters (42.2%) were found to be non-invariant—substantially exceeding the 25% threshold proposed by Asparouhov and Muthén (2014).

Table 4

Item stemLoadings patterns across countries (metric)Non-invariant (percent)
natgovBE BG CH (CZ) EE FI (FR) (GB) (GR) HR HU IE (IS) IT LT (ME) MK NL NO PT (SI) (SK)36.4
willBE BG CH CZ EE FI FR GB GR HR HU IE IS IT LT (ME) MK NL NO PT SI (SK)9.1
eliteBE (BG) CH (CZ) EE FI FR GB (GR) (HR) HU IE IS IT LT (ME) MK NL NO PT SI SK22.7
incomeBE (BG) CH (CZ) EE FI FR GB GR HR (HU) (IE) IS (IT) (LT) (ME) (MK) NL NO (PT) SI SK40.9
povertyBE (BG) CH CZ EE FI FR GB GR HR HU IE (IS) (IT) LT (ME) MK NL (NO) PT (SI) SK27.3
accountBE BG (CH) CZ EE FI FR GB GR HR HU (IE) IS (IT) LT ME MK NL (NO) PT SI SK18.2
law(BE) (BG) CH (CZ) EE FI (FR) GB GR HR HU IE IS IT LT ME MK (NL) NO PT SI (SK)27.3
referendBE BG (CH) (CZ) EE FI (FR) GB (GR) HR (HU) IE (IS) (IT) LT (ME) MK NL (NO) (PT) (SI) (SK)54.5
minorityBE BG (CH) CZ (EE) (FI) FR (GB) GR HR HU (IE) (IS) (IT) LT (ME) MK (NL) (NO) PT SI SK45.5
mediaBE (BG) CH CZ EE FI FR GB GR HR HU IE (IS) (IT) LT (ME) MK (NL) (NO) PT SI SK27.3
partyBE (BG) (CH) CZ EE FI FR GB GR HR HU IE IS IT LT ME MK (NL) NO (PT) (SI) SK22.7
fair(BE) BG CH CZ EE (FI) (FR) GB GR HR HU IE IS IT LT (ME) (MK) NL NO PT SI SK22.7
Intercept patterns across countries (scalar)
natgov(BE) BG (CH) CZ (EE) (FI) FR GB (GR) HR (HU) (IE) (IS) (IT) (LT) (ME) MK NL NO PT (SI) (SK)59.1
willBE (BG) CH (CZ) EE (FI) FR GB (GR) HR (HU) (IE) (IS) (IT) LT (ME) (MK) (NL) NO PT (SI) (SK)59.1
elite(BE) BG CH (CZ) (EE) (FI) (FR) (GB) (GR) (HR) (HU) (IE) IS (IT) (LT) (ME) MK (NL) (NO) (PT) (SI) (SK)81.8
referendBE (BG) CH CZ EE FI FR (GB) (GR) (HR) (HU) IE IS IT (LT) (ME) MK (NL) (NO) (PT) SI (SK)50.0
incomeBE (BG) CH CZ EE FI FR (GB) (GR) (HR) (HU) IE IS IT (LT) (ME) MK (NL) (NO) (PT) SI (SK)50.0
poverty(BE) (BG) (CH) CZ (EE) (FI) FR (GB) (GR) HR (HU) (IE) (IS) IT LT ME (MK) (NL) (NO) PT SI SK59.1
account(BE) (BG) (CH) (CZ) (EE) FI FR GB (GR) HR (HU) (IE) IS (IT) LT (ME) MK (NL) (NO) PT SI (SK)59.1
lawBE (BG) (CH) CZ (EE) (FI) FR GB (GR) HR HU IE IS (IT) (LT) ME MK NL NO (PT) (SI) SK40.9
minorityBE (BG) CH (CZ) (EE) (FI) FR GB (GR) HR (HU) IE IS IT (LT) ME (MK) NL (NO) PT SI (SK)50.0
mediaBE (BG) CH (CZ) (EE) (FI) (FR) GB (GR) (HR) (HU) (IE) IS IT (LT) (ME) (MK) NL NO PT SI (SK)45.5
partyBE (BG) (CH) CZ EE FI (FR) GB (GR) HR (HU) (IE) IS (IT) (LT) (ME) MK NL (NO) (PT) SI (SK)54.5
fair(BE) BG (CH) CZ (EE) (FI) (FR) (GB) (GR) HR (HU) IE (IS) IT LT ME MK (NL) (NO) PT SI SK50.0

Results of alignment optimization.

N = 37,464. Non-invariant parameters are enclosed in parentheses.

Interestingly, only one of the tested Bayesian approximate MI models successfully converged with a PPP of zero, which typically indicates poor model fit. However, given the large number of country groups and the small prior variances applied, these results must be interpreted with caution (Asparouhov and Muthén, 2019; Arts et al., 2021). At a prior variance of .001, the BCFI with .89, BTLI with .88, and BRMSEA with .09 fail to meet the thresholds for acceptable model fit. Overall, the findings suggest that even under relaxed assumptions of parameter equality, the data do not support scalar invariance.

Figure 9 presents the country-wise ranking of estimated latent means derived from models that would support robust cross-national comparisons, namely, the scalar MI model, alignment, and the BAMI model, thereby illustrating the substantive implications of lacking scalar invariance. In this ranking, a value of 1 corresponds to the highest estimated latent mean, while 22 indicates the lowest. Crucially, the comparison reveals notable fluctuations in latent means, as reflected in shifting country rankings across different modeling approaches. For the populist value dimension, only France and Montenegro retain stable positions across all models. In the social-democratic dimension, 10 countries consistently maintain their relative rankings, suggesting higher measurement stability. Regarding the liberal value dimension, Norway, Slovenia, France, Great Britain, and Montenegro show no change in ranking across models. Notably, across all value dimensions and modeling strategies, no clear or consistent cultural or regional clustering emerges in terms of high or low ranks in democratic values.

Figure 9

Regarding measurement hypotheses H3, H4, and H5, the findings indicate that the data support meaningful dimensions of democratic values that hold cross-nationally (H3). The relationship and relevance of certain indicators vary somewhat, but metric invariance can be achieved (H4). However, comparisons of latent means remain problematic due to unsecured scalar invariance, regardless of whether strict or more flexible approaches are applied (H5).

To conclude our analysis, Table 5 presents the model results from the MGLPA, focusing on the cross-national comparability of the two identified latent profiles. The log-likelihood difference tests indicate that more restrictive models are not supported by the data. Only the heterogeneous model shows an acceptable fit, while the full homogeneous model failed to converge. Figure 10 illustrates profile membership across Europe on a map, alongside the estimated item means for each profile. As demonstrated in the previous analyses, most indicators show pronounced skewness toward strong agreement, particularly those related to fair elections and the rule of law. Accordingly, a key distinction between the acquiescence and nuanced profiles lies in their overall level of agreement across the scale. The observed response patterns may therefore reflect a widespread consensus on the importance of all regime features, rather than substantively distinct value-holder profiles, for instance, individuals who favor social-democratic over populist democratic values. Although our analysis excludes several European states whose inclusion would allow for a more comprehensive assessment, the profiles do not reveal any plausible clustering, for instance, along cultural traditions or regime similarities between countries.

Table 5

ModelLLΔLLdfp-valueBICaBICAICEntropy
Heterogenous−977,184.058301,960,5081,958,6551,955,534.876
Partial homogenous−992,753.131,138.27901,986,3381,986,0871,985,664.906
Full HomogenousModel estimation did not terminate normally

Model fit for the MGLPA.

N = 37,464. LL = Log-likelihood, df = Degrees of freedom, p-value = Significance level, BIC = Bayesian information criterion, aBIC = Sample size adjusted BIC, AIC = Akaike information criterion, Entropy = Classification accuracy of the model.

Figure 10

In summary, with respect to measurement hypotheses H6, H7, and H8, the two-profile solution is supported across countries (H6). However, profile membership probabilities vary substantially between countries (H7), and the estimation problems of the homogeneous model (H8) indicates that profile-specific item means cannot be meaningfully compared across national contexts. Table 6 provides a brief tabular overview of all measurement hypotheses, corresponding results, and their respective implications.

Table 6

HypothesesResultsImplications for further research
H1: Within each country, exploratory factor analysis indicates the same number of factors.YesProblematic items revealed. Theoretical refinement and redevelopment of scale indicators to capture value dimensions adequately.
H2: Within each country, exploratory profile analysis indicates the same number of profiles.YesProblematic items revealed. Identification of profiles based on high and low scorers; critical examination of response behaviors.
H3: The structure of latent value factors appears to be meaningfully comparable across countries.YesThe findings support three meaningful value dimensions: social-democratic, populist, and liberal democratic values.
H4: The factor loadings of the latent values dimensions are invariant across countries.Not satisfactoryCross-cultural comparability is hampered; the issues appear item-specific rather than culture-specific.
H5: The intercepts of the latent values dimensions are invariant across countries.RejectedResearchers should exercise caution when comparing latent means across countries using the full scale; nonetheless, the social-democratic dimension remains the most robust.
H6: The structure of latent value profiles appears to be meaningfully comparable across countries.YesFindings suggest agreement response behavior, with item skewness needing consideration; however, the two-profile solution offers limited theoretical value.
H7: The relationships between the items and the latent value profiles are invariant across countries.RejectedThe analysis does not support comparable class probabilities of high and low scorers across countries.
H8: The average item-values of a latent value profile are invariant across countries.UncertainThe model did not terminate normally, likely due to the encountered measurement problems.

Summary of the results.

4 Discussion

Political support remains a widely used—yet frequently contested—concept in the social sciences. As a scholarly umbrella term, it comprises diverse efforts to explain why, how, and to what extent citizens support a given regime. From an Eastonian perspective, support of those subject to governance constitutes one of the most vital resources for the legitimacy of political authority, arguably even its most crucial. This holds especially in contexts where regime legitimacy is presumed to rest on consent rather than coercion. Yet recent developments suggest that even in long-standing democracies, disparaging views on the state and core principles of liberal democracy, norms long regarded as indispensable, are becoming increasingly evident and also politized (van Ham and van Elsas, 2024), whereas support for authoritarian and fear-based leadership practices is on the rise (Bloeser et al., 2024).

Given the rise of illiberal ideas and right-wing forces across the globe, cross-cultural research is increasingly relevant, particularly in examining and understanding contemporary permutations of political support. However, a central drawback in the vast topic-related field of survey research lies in the frequent lack of sensitivity to the suitability, quality, and invariance of measurement across countries, demographic groups, and over time (see, e.g., Meuleman et al., 2022, Fischer and Rudnev, 2024; Fischer et al., 2025). Although robust and conceptually rich theoretical frameworks exist, ambiguities remain in translating the conceived components into empirical research (König et al., 2022; Wiesner and Harfst, 2022). To address these limitations, we argue that linking democracy theory and terminology with the specification of empirical observations is essential both for the development of new instruments and in the analysis of already collected data. Doing so not only strengthens the validity of empirical findings and enhances their cross-national comparability but also increases their relevance for evidence-based policies intended at countering democratic backsliding and deconsolidation.

We therefore aim to conceptualize Norris’s framework of political support by explicitly integrating a more differentiated perspective on the nature of psychological orientations in cross-cultural research. Recognizing the pluralistic bedrock of democracy theory, we propose an approach that engages with the normative underpinnings of democratic procedures and to the substantive outcomes they generate. Additionally, we differentiate between social identities, values, and attitudes as the underlying psychological constructs of measurement. Our framework facilitates a decisive operationalization of surveyed indicators and contributes to a more nuanced understanding of the dimensions, meanings, and boundaries of regime support.

Periods of intense polarization and conflict related to traditional and new emerging societal cleavages are characteristic of political dynamics in times of upheaval, underscoring that shared societal values are sought to be essential for social cohesion and regime stability (Schwartz and Sagie, 2000). As a vital component, we focused on support for regime principles, conceived as abstract, idealized values that shape individuals’ preferences regarding the organization and exercise of political authority. Although convergence in the endorsement of values varies across European nations (Akaliyski et al., 2021), the perception of sharing such values is considered essential for fostering identification with the European Union (EU) as a political community. Research shows, for instance, that the belief in shared values, such as freedom, tolerance, and equality, which are characteristic of liberal democracy, strengthens citizens’ sense of belonging, serving as an important reservoir for the process of European integration (Kleiner and Bücker, 2024). However, given that prior research often overlooks issues of measurement quality and invariance, our study aims to address this gap by placing particular emphasis on the comparability of democratic values across European countries.

The analyses based on the CMoD scale from the ESS round 10 demonstrate that a meaningful structure of democratic value dimensions can be empirically identified, although it partly diverges from the underlying components drawn from theoretical models of democracy (Ferrín and Kriesi, 2016). Thus, we found that the data support the existence of liberal, social-democratic, and populist democratic value dimensions. However, the factors show notable intercorrelations varying significantly across European countries, indicating that the value dimensions are indeed not mutually exclusive. Contrary, populist demands on the state do not necessarily contradict that citizens appreciate the merits of liberal democracy or social welfare and vice versa (Zaslove and Meijers, 2023). More broadly, this underscores that support for regime principles is inherently complex and context-dependent, as it is also shaped by short-term economic and institutional developments that strongly influence public evaluations of the regime (Mishler and Rose, 2002; Quaranta and Martini, 2016; Claassen, 2020).

However, we observed considerable variation in how the indicators relate across both factors and countries, posing vocal challenges to the question of generalizability across different samples. Yet such applications are often central in comparative democracy research, for example, to examine whether national democratic traditions are reflected in citizens’ regime preferences, or to assess whether public support for core democratic principles has consolidated in transitional democracies or eroded in established ones. According to our findings, irrespective of whether strict or approximate invariance approaches were applied, empirical ambiguities persist, offering only limited support for substantively interpreting latent mean structures. Consequently, if latent variable models that explicitly account for measurement error fail to produce reliable results, reliance on simple sum or composite scores becomes even more problematic.

As it remains unclear whether the CMoD should be conceptualized as a survey instrument for capturing latent value dimensions or typologies of value holders based on multiple indicators, we additionally applied person-centered approaches to assess clusters of respondents (cf. Kriesi et al., 2016; Kriesi, 2025). However, the results suggest that respondents primarily differ in the overall degree to which they uniformly endorse all regime features included in the scale, rather than forming substantively distinct profiles of democratic values as indicated by the factor-analytical findings. This, in turn, is consistent with the finding that the dimensions of democratic values are strongly interrelated, as indicated by substantial positive correlations. Furthermore, pronounced acquiescence tendencies and highly skewed item distributions appear to impede the identification of meaningful person-level heterogeneity across cultural contexts.

Taken together, how should researchers deal with findings that reveal serious measurement limitations? Most fundamentally, our results underscore the importance of cultivating greater sensitivity to the measurement properties of survey instruments and caution against the overly sanguine assumption that cross-national comparability is inherently given. As the analyses using the CMoD scale have demonstrated, meaningful cross-country comparisons may be feasible—at least within the variable-centered approach—but the robustness of such comparisons should be critically evaluated and carefully qualified when interpreting and generalizing findings.

When sufficient measurement invariance cannot be confidently secured, researchers should adopt both theoretical and methodological strategies to mitigate potential limitations (Leitgöb et al., 2023). This includes examining social subgroups within countries that may contribute to measurement non-invariance and considering, for instance, how survey mode or specific respondent characteristics, such as sociodemographic factors, language proficiency, or political ideology, may systematically bias responses to survey questions evaluating and interpreting democratic regimes. In light of these considerations, we propose three key recommendations to advance cross-national research on political support:

(1) Researchers should explicitly define whether the constructs being measured reflect guiding principles such as values, generalized or specific attitudes, or emotional-cognitive responses. Conceptual clarity is essential not only for theoretical coherence but also for ensuring consistency between the assumed latent constructs and empirical observations. A clear definition also fosters stronger alignment with the rich theoretical frameworks from social psychology, for example, by linking regime support to normative beliefs or perceived gaps between democratic ideals and lived political realities.

(2) The nature and number of latent dimensions underlying political support should be guided by theory and subject to empirical scrutiny. Researchers should clearly specify whether they treat political support as a unidimensional or multidimensional concept and, thinking of applications such as those employing the CMoD scale, whether respondents are understood as value rankers or value holders. However, theoretical models of democracy and their resonance or divergence across different societal contexts should ultimately be subjected to rigorous empirical validation. When measurement invariance is not established, further investigation is needed into whether and how respondents understand complex concepts and interpret item wording and response scales. Pretesting techniques such as cognitive interviewing, web probing, or qualitative fieldwork can help identify and address such challenges, thereby enhancing the validity of survey instruments.

(3) Comparative research should explicitly evaluate whether, and to what extent, measurement instruments should meet invariance criteria across countries or populations. Appropriate methods include MGCFA, mixture modeling such as LPA, and state-of-the-art approaches such as alignment optimization or BAMI, ideally supplemented by visualizations of model output to detect systematic patterns across multiple groups. These methods, employed in conjunction with established fit criteria and sensitivity analyses, are essential for determining whether cross-national comparisons are both statistically tenable and substantively meaningful.

In conclusion, this study calls for a more reflective and methodologically rigorous approach to measurement in comparative survey research on political support. Addressing the conceptual, empirical, and methodological challenges posed by measurement (non-)invariance is not merely a technical requirement—it is foundational to robust, interpretable, and culturally sensitive empirical findings related to the dynamics of democratic legitimacy.

Statements

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://doi.org/10.21338/NSD-ESS10-2020. Replication materials are available at https://gitlab.com/oliver.platt-research/democratic-values.

Ethics statement

The studies involving humans were approved by ESS ERIC Research Ethics Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

PK-A: Writing – review & editing, Conceptualization, Writing – original draft, Formal analysis, Methodology. OP: Formal analysis, Writing – review & editing, Writing – original draft, Data curation, Methodology, Visualization, Conceptualization. PS: Writing – review & editing, Methodology. AH: Writing – review & editing, Methodology.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpos.2025.1612506/full#supplementary-material

References

  • 1

    AkaliyskiP.WelzelC.HienJ. (2021). A community of shared values? Dimensions and dynamics of cultural integration in the European Union. J. Eur. Integr.44, 569590. doi: 10.1080/07036337.2021.1956915

  • 2

    AlmondG. A.VerbaS. (1963). The civic culture – political attitudes and democracy in five nations. Princeton, NJ: Princeton University Press.

  • 3

    AndersonC. J.BlaisA.BowlerS.DonovanT.ListhaugO. (2005). Loser’s consent: elections and democratic legitimacy. Oxford: Oxford University Press.

  • 4

    AngiolilloF.LundstedtM.NordM.LindbergS. I. (2024). State of the world 2023: democracy winning and losing at the ballot. Democratization31, 9831013. doi: 10.1080/13510347.2024.2341435

  • 5

    AnstötzP.SchmidtP.HeyderA. (2020). “Wie valide ist die empirische Messung der Through- und Outputlegitimität politischer Systeme? Eine kritische Betrachtung” in Legitimität und Legitimation: Vergleichende Perspektiven. eds. WiesnerC.HarfstP. (Wiesbaden: Springer VS), 3355. doi: 10.1007/978-3-658-26558-8_3

  • 6

    ArtsI.FangQ.van de SchootR.MeitingerK. (2021). Approximate measurement invariance of willingness to sacrifice for the environment across 30 countries: the importance of prior distributions and their visualization. Front. Psychol.12:624032. doi: 10.3389/fpsyg.2021.624032,

  • 7

    AruqajB. (2023). Social cohesion in European societies. Conceptualising and assessing togetherness. New York, NY: Routledge.

  • 8

    AsparouhovT.MuthénB. (2014). Multiple-group factor analysis alignment. Struct. Equ. Model.21, 495508. doi: 10.1080/10705511.2014.919210

  • 9

    AsparouhovT.MuthénB. (2019). Latent variable centering of predictors and mediators in multilevel and time-series models. Struct. Equ. Model.26, 119142. doi: 10.1080/10705511.2018.1511375

  • 10

    AsparouhovT.MuthénB. (2020). Advances in Bayesian model fit evaluation for structural equation models. Struct. Equ. Model.28, 114. doi: 10.1080/10705511.2020.1764360

  • 11

    BarberB. R. (1984). Strong democracy: participatory politics for a new age. Berkeley, Los Angeles, London: University of California Press.

  • 12

    BemD. J. (1972). Self-perception theory. Adv. Exp. Soc. Psychol.6, 162. doi: 10.1016/S0065-2601(08)60024-6

  • 13

    BlankT.SchmidtP. (2003). National identity in a united Germany: nationalism or patriotism? An empirical test with representative data. Polit. Psychol.24, 289312. doi: 10.1111/0162-895X.00329

  • 14

    BloeserA. J.WilliamsT.CrawfordC.HarwardB. M. (2024). Are stealth democrats really committed to democracy? Process preferences revisited. Perspect. Polit.22, 116130. doi: 10.1017/S1537592722003206

  • 15

    BoeseV. A.LundstedtM.MorrisonK.SatoY.LindbergS. I. (2022). State of the world 2021: autocratization changing its nature?Democratization29, 9831013. doi: 10.1080/13510347.2022.2069751

  • 16

    BoothJ. A.SeligsonM. A. (2009). The legitimacy puzzle in Latin America – political support and democracy in eight nations. Cambridge: Cambridge University Press.

  • 17

    BrattonM.MattesR.Gyimah-BoadiE. (2005). Public opinion, democracy, and market reform in Africa. Cambridge: Cambridge University Press.

  • 18

    BuchananA. (2002). Political legitimacy and democracy. Ethics112, 689719. doi: 10.1086/340313

  • 19

    BühlmannM.KriesiH. (2013). “Models for democracy” in Democracy in the age of globalization and mediatization. eds. KriesiH.LavenexS.EsserF.MatthesJ.BühlmannM.BochslerD. (Houndmills, Basingstoke, Hampshire: Palgrave Macmillan), 4468.

  • 20

    CanacheD.MondakJ. J.SeligsonM. A. (2001). Meaning and measurement in cross-national research on satisfaction with democracy. Public Opin. Q.65, 506528. doi: 10.1086/323576

  • 21

    ChapmanH. S.HansonM. C.DzutsatiV.DeBellP. (2024). Under the veil of democracy: what do people mean when they say they support democracy?Perspect. Polit.22, 97115. doi: 10.1017/S1537592722004157

  • 22

    ChenF. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Struct. Equ. Modeling14, 464504. doi: 10.1080/10705510701301834

  • 23

    ClaassenC. (2020). In the mood for democracy? Democratic support as thermostatic opinion. Am. Polit. Sci. Rev.114, 3653. doi: 10.1017/S0003055419000558

  • 24

    ClaassenC.AckermannK.BertsouE.BorbaL.CarlinR. E.et al. (2024). Conceptualizing and measuring support for democracy: a new approach. Comp. Polit. Stud.58, 11711198. doi: 10.1177/00104140241259458

  • 25

    ClaassenC.MagalhãesP. C. (2023). Public support for democracy in the United States has declined generationally. Public Opin. Q.87, 719732. doi: 10.1093/poq/nfad039,

  • 26

    ComanoruR.FitzgeraldR. (2025). “The challenges of repeat measurement in times of the COVID-19 pandemic” in How Europeans view and evaluate democracy revisited. Ten years later. eds. FerrínM.KriesiH. (Oxford: Oxford University Press), 2954.

  • 27

    DahlR. A. (1989). Democracy and its critics. New Haven, London: Yale University Press.

  • 28

    DaltonR. J. (2004). Democratic challenges, democratic choices. New York: Oxford University Press.

  • 29

    DavidovE.SchmidtP.BillietJ.MeulemanB. (Eds.) (2018). Cross-cultural analysis: Methods and applications. 2nd Edn. New York: Routledge.

  • 30

    DavisN. T.GoidelK.ZhaoY. (2020). The meanings of democracy among mass publics. Soc. Indic. Res.153, 849921. doi: 10.1007/s11205-020-02517-2

  • 31

    DentersB.GabrielO. W.TorcalM. (2007). “Political confidence in representative democracies: socio-cultural vs. political explanations” in Citizenship and involvement in European democracies. A comparative analysis. eds. van DethJ. W.MonteroJ. R.WestholmA. (London, New York: Routledge), 6687.

  • 32

    DiamondL. J. (1999). Developing democracy – toward consolidation. Baltimore, MD: Johns Hopkins University Press.

  • 33

    DiamondL. J.MorlinoL. (2005). Assessing the quality of democracy. Baltimore: Johns Hopkins University Press.

  • 34

    EaglyA. H.ChaikenS. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich.

  • 35

    EastonD. (1965). Systems analysis of political life. New York, NY: John Wiley & Sons Ltd.

  • 36

    EastonD. (1975). A re-assessment of the concept of political support. Br. J. Polit. Sci.5, 435457. doi: 10.1017/S0007123400008309

  • 37

    ESS round 10 (2023a). ESS10 - integrated file, edition 3.2 [Data set]. Sikt - Norwegian Agency for Shared Services in Education and Research. doi: 10.21338/ess10e03_2

  • 38

    ESS round 10 (2023b). ESS round 10–2020. Democracy, digital social contacts. Sikt - Norwegian Agency for Shared Services in Education and Research. doi: 10.21338/NSD-ESS10-2020

  • 39

    FerrínM.KriesiH. (2016). How Europeans view and evaluate democracy. Oxford: Oxford University Press.

  • 40

    FischerR.KarlJ. A.Luczak-RoeschM.HartleL. (2025). Why we need to rethink measurement invariance: the role of measurement invariance for cross-cultural research. Cross-Cult. Res.59, 147179. doi: 10.1177/10693971241312459

  • 41

    FischerR.RudnevM. (2024). From misgivings to mise-en-scène: the role of invariance in personality science. Eur. J. Personal.39, 662673. doi: 10.1177/08902070241283081

  • 42

    FoaR. S.KlassenA.SladeM.RandA.CollinsR. (2020). The global satisfaction with democracy report 2020. Cambridge, UK: Centre for the Future of Democracy.

  • 43

    FoaR. S.MounkY. (2019). Democratic deconsolidation in developed democracies, 1995–2018. doi: 10.17863/CAM.90283

  • 44

    FuchsD. (1989). Die Unterstützung des politischen Systems der Bundesrepublik Deutschland. Opladen: Westdeutscher Verlag.

  • 45

    FuchsD.GuidorossiG.SvenssonP. (1995). “Support for the democratic system” in Citizens and the state. eds. KlingemannH.-D.FuchsD. (Oxford: Oxford University Press), 323353.

  • 46

    Garzón ValdésE. (1988). Die Stabilität politischer Systeme. Analyse des Begriffs mit Fallbeispielen aus Lateinamerika. Freiburg i. Br: Alber.

  • 47

    GilleyB. (2006). The meaning and measure of state legitimacy: results for 72 countries. Eur. J. Polit. Res.45, 499525. doi: 10.1111/j.1475-6765.2006.00307.x

  • 48

    HabermasJ. (1992). Faktizität und Geltung. Beiträge zur Diskurstheorie des Rechts und des demokratischen Rechtsstaates. Frankfurt am Main: Suhrkamp.

  • 49

    HallquistM.WileyJ.Van LissaC. J.MorilloD. (2024). MplusAutomation: an R package for facilitating large-scale latent variable analyses in Mplus. doi: 10.32614/CRAN.package.MplusAutomation

  • 50

    HarfstP.WiesnerC. (2024). Measuring political legitimacy in two dimensions: internal and external measures. Front. Polit. Sci.6:999743. doi: 10.3389/fpos.2024.999743

  • 51

    HarteveldE.van der MeerT.De VriesC. E. (2013). In Europe we trust? Exploring three logics of trust in the European Union. Eur. Union Polit.14, 542565. doi: 10.1177/14651165134910

  • 52

    HesterJ.BryanJ. (2024). Glue: Interpreted string literals. doi: 10.32614/CRAN.package.glue

  • 53

    HewstoneM.StroebeW. (2020). An introduction to social psychology. Hoboken, NJ: Wiley & Sons.

  • 54

    HeyderA.AnstötzP.EisentrautM.SchmidtP. (2022). “20 years after…” GFE 2.0: a theoretical revision and empirical testing of the concept of “group-focused enmity” based on longitudinal data. Front. Polit. Sci.4:752810. doi: 10.3389/fpos.2022.752810

  • 55

    HeyneL. (2018). The making of democratic citizens: how regime-specific socialization shapes Europeans’ expectations of democracy. Swiss Polit. Sci. Rev.25, 4063. doi: 10.1111/spsr.12338

  • 56

    HomerP. M.KahleL. R. (1988). A structural equation test of the value–attitude–behavior hierarchy. J. Pers. Soc. Psychol.54, 638646. doi: 10.1037/0022-3514.54.4.638

  • 57

    HornJ. L.McardleJ. J. (1992). A practical and theoretical guide to measurement invariance in aging research. Exp. Aging Res.18, 117144. doi: 10.1080/03610739208253916,

  • 58

    HuL.-T.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model.6, 155. doi: 10.1080/10705519909540118

  • 59

    IannoneR.ChengJ.SchloerkeB.HughesE.LauerA.SeoJ.et al. (2025). Gt: easily create presentation-ready display tables. doi: 10.32614/CRAN.package.gt

  • 60

    InglehartR. (1971). The silent revolution in Europe: intergenerational change in post-industrial societies. Am. Polit. Sci. Rev.65, 9911017. doi: 10.2307/1953494

  • 61

    InglehartR. (1997). Modernization and post modernization: cultural, economic and political change in 43 societies. Princeton: Princeton University Press.

  • 62

    JacobsenJ.FuchsL. M. (2020). Can we compare conceptions of democracy in cross-linguistic and cross-national research? Evidence from a random sample of refugees in Germany. Soc. Indic. Res.151, 669690. doi: 10.1007/s11205-020-02397-6

  • 63

    JoppkeC. (2021). Neoliberal nationalism. Immigration and the rise of the populist right. Cambridge: Cambridge University Press.

  • 64

    JostJ. T.NosekB. A.GoslingS. D. (2008). Ideology: its resurgence in social, personality, and political psychology. Perspect. Psychol. Sci.3, 126136. doi: 10.1111/j.1745-6916.2008.00070.x,

  • 65

    KahleL. R. (1980). Stimulus condition self-selection by males in the interaction of locus of control and skill-chance situations. J. Pers. Soc. Psychol.38, 5056. doi: 10.1037/0022-3514.38.1.50

  • 66

    KahleL. R. (1983). Social values and social change: adaptation to life in America. New York: Praeger.

  • 67

    KirschH.WelzelC. (2019). Democracy misunderstood: authoritarian notions of democracy around the globe. Soc. Forces98, 5992. doi: 10.1093/sf/soy114

  • 68

    KleinerT. M.BückerN. (2024). Is a sense of community based on similarity? The perception of shared values and citizens’ EU identity. J. Contemp. Eur. Stud.1–17, 117. doi: 10.1080/14782804.2024.2317947,

  • 69

    KlingemannH.-D. (1999). “Mapping political support in the 1990s: a global analysis” in Critical citizens. Global support for democratic governance. ed. Norris (Oxford: Oxford University Press), 3156.

  • 70

    KönigP. D.SiewertM. B.AckermannK. (2022). Conceptualizing and measuring citizens’ preferences for democracy: taking stock of three decades of research in a fragmented field. Comp. Polit. Stud.55, 20152049. doi: 10.1177/00104140211066213

  • 71

    KornbergA.ClarkeH. D. (1992). Citizens and community: political support in a representative democracy. Cambridge: Cambridge University Press.

  • 72

    KraemerF.EserA.YıldızC. (2024). Assessing response quality and careless responding in multi-item scales. KODAQS, Toolbox. GESIS – Leibniz Institute for the Social Sciences. Available online at: https://github.com/kraemefe/resquin-tool-application

  • 73

    KriesiH. (2013). Democratic legitimacy: is there a legitimacy crisis in contemporary politics?Polit. Vierteljahresschr.54, 609638. doi: 10.5771/0032-3470-2013-4-609

  • 74

    KriesiH. (2025). “Stability and change in the structure of Europeans' views of democracy” in How Europeans view and evaluate democracy revisited. ten years later. eds. FerrínM.KriesiH. (Oxford: Oxford University Press), 104126.

  • 75

    KriesiH.SarisW.MoncagattaP. (2016). “The structure of Europeans’ views of democracy: citizens’ models of democracy” in How Europeans view and evaluate democracy. eds. FerrínM.KriesiH. (Oxford: Oxford University Press), 6489.

  • 76

    KubinE.von SikorskiC. (2021). The role of (social) media in political polarization: a systematic review. Ann. Int. Commun. Assoc.45, 188206. doi: 10.1080/23808985.2021.1976070

  • 77

    LauthH.-J. (2020). “Legitimacy and legitimation” in The SAGE handbook of political science. eds. BertrandB.Berg-SchlosserD.MorlinoL. L. (Thousand Oaks, CA: Sage), 841859.

  • 78

    LeitgöbH.SeddigD.AsparouhovT.BehrD.DavidovE.De RooverK.et al. (2023). Measurement invariance in the social sciences: historical development, methodological challenges, state of the art, and future perspectives. Soc. Sci. Res.110:102805. doi: 10.1016/j.ssresearch.2022.102805,

  • 79

    LeviM.StokerL. (2000). Political trust and trustworthiness. Annu. Rev. Polit. Sci.3, 475507. doi: 10.1146/annurev.polisci.3.1.475

  • 80

    LewkowiczJ.WoźniakM.WrzesińskiM. (2022). COVID-19 and erosion of democracy. Econ. Model.106:105682. doi: 10.1016/j.econmod.2021.105682,

  • 81

    LijphartA. (1984). Democracies. Patterns of majoritarian and consensus government in twenty-one countries. New Haven, CT and London: Yale University Press.

  • 82

    LikertR. (1932). A technique for the measurement of attitudes. Arch. Psychol.140, 155.

  • 83

    LindeJ.EkmanJ. (2003). Satisfaction with democracy: a note on a frequently used indicator in comparative politics. Eur. J. Polit. Res.42, 391408. doi: 10.1111/1475-6765.00089

  • 84

    LinzJ. J.StepanA. (1996). Problems of democratic transition and consolidation: Southern Europe, South America, and post-communist Europe. Baltimore: Johns Hopkins University Press.

  • 85

    LuhmanN. (1989). Vertrauen. Ein Mechanismus der Reduktion Sozialer Komplexität. Stuttgart: Fredinand Enke Verlag.

  • 86

    MagalhãesP. C. (2014). Government effectiveness and support for democracy. Eur. J. Polit. Res.53, 7797. doi: 10.1111/1475-6765.12024

  • 87

    MaioG. R.HaddockG. (2015). The psychology of attitudes and attitude change. London: Sage Publications.

  • 88

    MaioG. R.OlsenJ. M.BernardM. M.LukeM. A. (2003). “Ideologies, values, attitudes, and behavior” in Handbook of social psychology. ed. DelamaterJ. (New York: Kluwer Academic/Plenum Publishers), 283308.

  • 89

    MaioG. R.OlsonJ. M. (1998). Values as truisms: evidence and implications. J. Pers. Soc. Psychol.74, 294311. doi: 10.1037/0022-3514.74.2.294

  • 90

    MarshH. W.GuoJ.ParkerP. D.NagengastB.AsparouhovT.MuthénB.et al. (2018). What to do when scalar invariance fails: the extended alignment method for multi-group factor analysis comparison of latent means across many groups. Psychol. Methods23, 524545. doi: 10.1037/met0000113,

  • 91

    MeulemanB.ŻółtakT.PokropekA.DavidovE.MuthénB.OberskiD. L.et al. (2022). Why measurement invariance is important in comparative research. A response to Welzel et al. (2021). Sociol. Methods Res.52, 14011419. doi: 10.1177/00491241221091755

  • 92

    Miller-LoessiK.ParkerJ. N. (2006). “Cross-cultural social psychology” in Handbook of social psychology. ed. DelamaterJ. (Boston, MA: Springer).

  • 93

    MishlerW.RoseR. (2002). Learning and re-learning regime support: the dynamics of post-communist regimes. Eur. J. Polit. Res.41, 536. doi: 10.1111/1475-6765.00002

  • 94

    MoncagattaP.SarisW. E.FierroM. (2023). Same same…but different? Support for the ideal of democracy vs. solid democratic support. Revista Latinoamericana de Opinión Pública12, 740. doi: 10.14201/rlop.31204

  • 95

    MounkY.FoaR. S. (2018). The end of the democratic century – autocracy’s global ascendance. Foreign Aff.97, 2936.

  • 96

    MuddeC. (2007). Populist radical right parties in Europe. New York: Cambridge University Press.

  • 97

    MüllerK.BryanJ. (2020). here: A simpler way to find your files. doi: 10.32614/CRAN.package.here

  • 98

    MuthénB.AsparouhovT. (2012). Bayesian structural equation modeling: a more flexible representation of substantive theory. Psychol. Methods17, 313335. doi: 10.1037/a0026802,

  • 99

    MuthénL. K.MuthénB. O. (2017). Mplus: Statistical analysis with latent variables: User’s guide (Version 8). Los Angeles, CA: Authors.

  • 100

    NorrisP. (1999). Critical citizens: global support for democratic government. Oxford: Oxford University Press.

  • 101

    NorrisP. (2001). Digital divide: civic engagement, information poverty, and the internet worldwide. Cambridge: Cambridge University Press.

  • 102

    NorrisP. (2011). Democratic deficit: critical citizens revisited. New York: Cambridge University Press.

  • 103

    NorrisP. (2017a). Is Western democracy backsliding? Diagnosing the risks. HKS Faculty Research Working Paper Series RWP17-012, March 2017.

  • 104

    NorrisP. (2017b). “The conceptual framework of political support” in Handbook on political trust. eds. ZmerliS.van der MeerT. W. G. (Cheltenham, UK; Northampton, MA: Edward Elgar Publishing), 1932.

  • 105

    NorrisP. (2025). The cultural roots of democratic backsliding. New York: Oxford University Press.

  • 106

    NorrisP.InglehartR. F. (2019). Cultural backlash. Trump, Brexit, and authoritarian populism. Cambridge: Cambridge University Press.

  • 107

    OlsenJ. M. (1990). “Self-inference processes in emotion” in Self-inference processes: the Ontario symposium. eds. OlsenJ. M.ZannaM. P. (Hillsdale, NJ: Erlbaum), 1741.

  • 108

    OserJ.HoogheM. (2018). Democratic ideals and levels of political participation: the role of political and social conceptualisations of democracy. Br. J. Polit. Int. Relat.20, 711730. doi: 10.1177/1369148118768140

  • 109

    Osterberg-KaufmannN.StarkT.Mohamad-KlotzbachC. (2020). Challenges in conceptualizing and measuring meanings and understandings of democracy. Z. Vergl. Polit.14, 299320. doi: 10.1007/s12286-020-00470-5

  • 110

    PappasT. S. (2019). Populism and liberal democracy: a comparative and theoretical analysis. Oxford, UK: Oxford University Press.

  • 111

    PatemanC. (1970). Participation and democratic theory. Cambridge: Cambridge University Press.

  • 112

    PebesmaE.BivandR. (2023). Spatial data science: with applications in R. New York: Chapman and Hall/CRC. doi: 10.1201/9780429459016

  • 113

    PeterF. (2008). Democratic legitimacy. New York: Routledge.

  • 114

    PosesC.RevillaM. (2022). Measuring satisfaction with democracy: how good are different scales across countries and languages?Eur. Polit. Sci. Rev.14, 1835. doi: 10.1017/S1755773921000266

  • 115

    PrislinR.OuelletteJ. (1996). When it is embedded, it is potent: effects of general attitude embeddedness on formation of specific attitudes and behavioral intentions. Personal. Soc. Psychol. Bull.22, 845861. doi: 10.1177/0146167296228007

  • 116

    PutnamR. D. (2000). Bowling alone. The collapse and revival of American community. New York: Simon and Schuster.

  • 117

    QuarantaM. (2018). How citizens evaluate democracy: an assessment using the European social survey. Eur. Polit. Sci. Rev.10:191–217. doi: 10.1017/S1755773917000054

  • 118

    QuarantaM.MartiniS. (2016). Does the economy really matter for satisfaction with democracy? Longitudinal and cross-country evidence from the European Union. Electoral Stud.42, 164174. doi: 10.1016/j.electstud.2016.02.015

  • 119

    RachmanG. (2022). The age of the strongman: how the cult of the leader threatens democracy around the world. New York: Other Press, LLC.

  • 120

    RawlsJ. (1993). Political liberalism. New York: Columbia University Press.

  • 121

    RoccasS.BerlinA. (2016). “Identification with groups and national identity: applying multidimensional models of group identification to national identification” in Dynamics of national identity: media and societal factors of what we are. eds. SchmidtP.GrimmJ.HuddyL.SeethalerJ. (London, New York: Routledge), 2243.

  • 122

    RokeachM. (1973). The nature of human values. New York: The Free Press.

  • 123

    RosenbergJ. M.BeymerP. N.AndersonD. J.van LissaC. J.SchmidtJ. A. (2018). TidyLPA: an R package to easily carry out latent profile analysis (LPA) using open-source or commercial software. J. Open Source Softw.3:978. doi: 10.21105/joss.00978

  • 124

    RosenbergM. J.HovlandC. I. (1960). “Cognitive, affective and behavioral components of attitudes” in Attitude organization and change: an analysis of consistency among attitude components. eds. RosenbergM. J.HovlandC. I. (New Haven, CT: Yale University Press), 114.

  • 125

    RosseelY. (2012). Lavaan: an R package for structural equation modeling. J. Stat. Softw.48, 136. doi: 10.18637/jss.v048.i02

  • 126

    RStudio Team (2020). RStudio: integrated development for R. Boston, MA: RStudio, PBC.

  • 127

    RupnikJ. (2011). “From democracy fatigue to populist backlash” in Democracy, state and society: European integration in central and Eastern Europe. eds. GóraM.ZielińskaK. (Kraków, Poland: Jagiellonian University Press), 95104.

  • 128

    SchumpeterJ. A. (1942). Capitalism, socialism, and democracy. New York: Harper & Brothers.

  • 129

    SchwartzS. H. (1992). “Universals in the content and structure of values: theoretical advances and empirical tests in 20 countries” in Advances in experimental social psychology. ed. ZannaM. P. (New York: Academic Press), 165.

  • 130

    SchwartzS. H. (1994). Are there universal aspects in the content and structure of values?J. Soc. Issues50, 1945.

  • 131

    SchwartzS. H.SagieG. (2000). Value consensus and importance: a cross-national study. J. Cross-Cult. Psychol.31, 465497. doi: 10.1177/0022022100031004003

  • 132

    SevdariM.MarmullakuD. (2023). Shapefile of European countries: Technical University of Denmark. Dataset. doi: 10.11583/DTU.23686383

  • 133

    SeydB. (2020). Political legitimacy in western Europe: comparing people’s expectations and evaluations of democracy. J. Elect. Public Opin. Parties33, 5473. doi: 10.1080/17457289.2020.1856121

  • 134

    SinghS. P.MayneQ. (2023). Satisfaction with democracy: a review of a major public opinion indicator. Public Opin. Q.87, 187218. doi: 10.1093/poq/nfad003

  • 135

    SokolovB. (2021). Measurement invariance of liberal and authoritarian notions of democracy: evidence from the world values survey and additional methodological considerations. Front. Polit. Sci.3:642283. doi: 10.3389/fpos.2021.642283

  • 136

    SvolikM. W.AvramovskaE.LutzJ.MilačićF. (2023). In Europe, democracy erodes from the right. J. Democr.34, 520. doi: 10.1353/jod.2023.0000

  • 137

    TajfelH.TurnerJ. (1979). “An integrative theory of intergroup conflict” in The social psychology of intergroup relations. eds. AustinW. G.WorchelS. (Monery, CA: Brooks/Cole), 3347.

  • 138

    TajfelH.TurnerJ. C. (1986). “The social identity theory of intergroup behavior” in Psychology of intergroup relations. eds. WorchelS.AustinW. G. (Chicago: Nel-son-Hall), 724.

  • 139

    TreierS.JackmanS. (2008). Democracy as a latent variable. Am. J. Polit. Sci.52, 201217. doi: 10.1111/j.1540-5907.2007.00308.x

  • 140

    van HamC.ThomassenJ. A.AartsK.AndewegR. (2017). Myth and reality of the legitimacy crisis: explaining trends and cross-national differences in established democracies. Oxford: Oxford University Press.

  • 141

    van HamC.van ElsasE. (2024). When legitimacy becomes the object of politics: the politicization of political support in European democracies. Front. Polit. Sci.6:1363083. doi: 10.3389/fpos.2024.1363083,

  • 142

    Van LissaC. J.Garnier-VillarrealM.AnadriaD. (2023). Recommended practices in latent class analysis using the open-source R-package tidySEM. Struct. Equ. Model.31, 526534. doi: 10.1080/10705511.2023.2250920

  • 143

    VierusP.ZillerC. (2025). Political support in times of progressive policy change and radical-right populist party success. West Eur. Polit.1–28, 128. doi: 10.1080/01402382.2025.2477420,

  • 144

    WeatherfordM. S. (1992). Measuring political legitimacy. Am. Polit. Sci. Rev.86, 149166. doi: 10.2307/1964021

  • 145

    WeberM. (1972). Die protestantische Ethik. 2 Bände. Gütersloh: Gütersloher Verlagshaus Gert Mohn.

  • 146

    WegscheiderC.StarkT. (2020). What drives citizens’ evaluation of democratic performance? The interaction of citizens’ democratic knowledge and institutional level of democracy. Z. Vergl. Polit.14, 345374. doi: 10.1007/s12286-020-00467-0

  • 147

    WestleB. (1989). Politische Legitimität - Theorien, Konzepte, empirische Befunde. Baden-Baden: Nomos.

  • 148

    WestleB. (1999). Kollektive Identität im vereinten Deutschland: Nation und Demokratie in der Wahrnehmung der Deutschen. Opladen: Leske & Budrich.

  • 149

    WestleB. (2007). “Political beliefs and attitudes: legitimacy in public opinion research” in Legitimacy in an age of global politics. eds. HurrelmannA.SchneiderS.SteffekF. (Houndmills, UK: Palgrave Macmillan), 93125.

  • 150

    WickhamH.AverickM.BryanJ.ChangW.McGowanL.FrançoisR.et al. (2019). Welcome to the tidyverse. J. Open Source Softw.4:1686. doi: 10.21105/joss.01686

  • 151

    WiesnerC.HarfstP. (2022). Conceptualizing legitimacy: what to learn from the controversies related to an “essentially contested concept”. Front. Polit. Sci.4:867756. doi: 10.3389/fpos.2022.867756

  • 152

    WodakR. (2019). Entering the ‘post-shame era’: the rise of illiberal democracy, populism and neo-authoritarianism in Europe. Glob. Discourse9, 195213. doi: 10.1332/204378919X15470487645420

  • 153

    ZagrebinaA. (2020). Concepts of democracy in democratic and nondemocratic countries. Int. Polit. Sci. Rev.41, 174191. doi: 10.1177/0192512118820716

  • 154

    ZannaM. P.RempelJ. K. (1988). “Attitudes: a new look at an old concept” in The social psychology of knowledge. eds. Bar-TalD.KruglanskiA. W. (Cambridge: Cambridge University Press), 315334.

  • 155

    ZasloveA.MeijersM. (2023). Populist democrats? Unpacking the relationship between populist and democratic attitudes at the citizen level. Polit. Stud.72, 11331159. doi: 10.1177/00323217231173800

Summary

Keywords

political support, democratic legitimacy and legitimation, democratic values, measurement invariance (MI), cross-cultural research, factor analysis, latent profile analysis, ESS round 10

Citation

Kolkwitz-Anstötz P, Platt O, Schmidt P and Heyder A (2025) Measurement of democratic values: a cross-country comparison with ESS round 10. Front. Polit. Sci. 7:1612506. doi: 10.3389/fpos.2025.1612506

Received

15 April 2025

Accepted

03 November 2025

Published

08 December 2025

Volume

7 - 2025

Edited by

Philipp Harfst, University of Göttingen, Germany

Reviewed by

Zsofia S. Ignacz, Goethe University Frankfurt, Germany

Valery Dzutsati, Southern Illinois University, United States

Updates

Copyright

*Correspondence: Pascal Kolkwitz-Anstötz,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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