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10.1371/journal.pone.0310031
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Psychological capital and social class: A capital approach to understanding positive psychological states and their role in explaining social inequalities
Psychological capital and social class
https://orcid.org/0000-0002-8542-128X
De Moortel Deborah Conceptualization Formal analysis Methodology Writing – original draft 1 2 *
Vos Mattias Conceptualization Formal analysis Visualization Writing – review & editing 1
https://orcid.org/0000-0003-0573-724X
Spruyt Bram Conceptualization Writing – review & editing 1
https://orcid.org/0000-0001-8619-8553
Vanroelen Christophe Conceptualization Methodology Writing – review & editing 1
Hofmans Joeri Conceptualization Methodology Writing – review & editing 3
Dóci Edina Conceptualization Writing – review & editing 4
1 Department of Sociology, Brussels Institute for Social and Population Studies, Vrije Universiteit Brussel, Brussels, Belgium
2 Flanders Research Foundation, Brussels, Belgium
3 Department of Psychology, Work and Organizational Psychology, Vrije Universiteit Brussel, Brussels, Belgium
4 Louvain Research Institute in Management and Organizations, Louvain School of Management, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
Ganotice Fraide Agustin Editor
Li Ka Shing Faculty of Medicine, The University of Hong Kong, HONG KONG
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: deborah.de.moortel@vub.be
9 9 2024
2024
19 9 e03100317 6 2023
22 8 2024
© 2024 De Moortel et al
2024
De Moortel et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Psychological capital (PsyCap) is a multidimensional concept entailing hope, self-efficacy, optimism, and resilience. This paper argues that it can be considered a form of “capital” explaining social inequality. We test whether PsyCap can be integrated into the Bourdieusian capital framework by assessing its relationship with social, economic, and cultural capital. We also identify different types of social positions based on the volume and composition of psychological, economic, cultural, and social capital. We use cross-sectional data from the European Social Survey of 2012 (N = 35,313 respondents; 29 countries). To test the associations with the Bourdieusian capital types, we calculated multilevel spearman rank correlations and performed confirmatory factor analyses (CFA). Latent Class Analysis identified different types of social positions. We found positive weak correlations between PsyCap and the indicators of cultural capital (r ≤ .14) and positive moderate correlations with the indicators of economic and social capital (r ≤ .24). The results of the CFA showed that the fit of the 4-capital model was superior to that of the 3-capital model. We identified six types of social positions: two deprived types (with overall low capital levels); two well-off types (with overall high capital levels) and two types with high psychological and social capital in combination with varying levels of cultural and economic capital. Including PsyCap in the Bourdieusian capital framework acknowledges the power of positive psychological states regarding processes of social mobility and social inequality on the one hand and calls for understanding PsyCap as a social and group-level phenomenon on the other hand. As such, integrating PsyCap into the Bourdieusian framework can help to address the longstanding issue of understanding the relationship between social and individual differences in the study of social inequalities.

Research Foundation Flanders FWO1.2.T82.21N https://orcid.org/0000-0002-8542-128X
De Moortel Deborah This research is facilitated by the research grant ‘FWO1.2.T82.21N’, which is assigned to the first author by the Research Foundation Flanders. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityThe data underlying the results presented in the study are available at https://ess-search.nsd.no/.
Data Availability

The data underlying the results presented in the study are available at https://ess-search.nsd.no/.
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pmcIntroduction

Social inequality is on the rise [1]. More and more people have few possibilities to earn a decent living, or to have a fair chance at a good life [2]. Pierre Bourdieu inspired many scholars to think about social inequality as a result of combinations of economic, cultural, and social capital. However, recent theoretical work called to integrate the notion of psychological capital (PsyCap), a concept originating from positive psychology, into Bourdieu’s capital framework [3, 4]. PsyCap is defined as an individual’s positive psychological state of development, characterized by the psychological resources of (1) self-efficacy (i.e., having the confidence to take on challenging tasks), (2) optimism (i.e., a positive attribution about the future), (3) hope (i.e., persevering towards a goal), and (4) resilience (i.e., bouncing back when having set-backs) [5].

Including PsyCap in the Bourdieusian capital framework acknowledges the power of positive psychological states regarding processes of social mobility and social inequality [4]. Positive psychological states can, just like the Bourdieusian capitals, lead to differences in access to resourceful connections [6], good jobs [7], wealth [8], and high-quality life [9]. Yet, this has largely been ignored in sociological theories that try to explain the drivers of social inequality, like the theory of Pierre Bourdieu [4]. Although, PsyCap has similar qualities as Bourdieu’s capitals, it is distinct from them because PsyCap is, unlike the forms of capital identified by Bourdieu, partly independent from socialization. Up until now, however, these arguments are voiced in theoretical articles while empirical tests of these ideas are still lacking. Addressing this issue, the first aim of this study is to assess whether PsyCap can be empirically integrated into the Bourdieusian capital framework.

Moreover, according to Bourdieu’s social class framework, every form of power or resource in social life (such as capital) is a criterion for distinguishing social positions [10, 11]. Bourdieu explains that the closer one individual is to another individual based on their possession and utilization of various capitals in the social space, the more likely these similarly located individuals are to have the same social position [12]. Because PsyCap, just like economic, social, and cultural capital can be deemed a resource in life and a principle of distinction, we believe it can also be used to define positions in the social space. Therefore, our second aim is to find different types of social positions based on the volume and composition of psychological, economic, cultural, and social capital.

To address these aims, we utilize a transnational approach [13]: we examine the relations of PsyCap with the Bourdieusian capitals and the belonging of individuals to different types of social positions in Europe. Using a transnational approach helps to develop a broadly applicable social class framework. Research comparing social classes across countries, or social class research within a specific country usually adopt an established social class framework that is applicable broadly, such as the Erikson-Goldthorpe-Portocarero (EGP) social class scheme [14] or (neo-)Marxist social class schemes [15]. In that sense, the types of social classes that can be found within the population are assumed to be valid over different high-income countries.

Yet, despite that social class frameworks are applicable in a pan-European context, countries exhibit different degrees of income inequality, varying levels of educational democratization and different welfare regimes [16], which affect the sizes of the classes. Therefore, our final aim is to examine country differences in the distribution of social classes.

We first discuss Bourdieu’s capital approach to explaining social inequalities and review his cultural reproduction theory to understand the origins of social classes. Then, we show that his theory misses the potential of psychological resources to explaining social inequalities. Subsequently, using the European Social Survey (ESS), we analyze how PsyCap is relatively independent from, but also related to other forms of Bourdieusian capital. Then, we present results from latent clustering analysis (LCA) to describe the underlying class structure in Europe based on the four types of capital. We conclude the results section by presenting country-specific distributions of the “integrated” class structure. The paper ends with a discussion on the implications of our results.

Bourdieu’s capital approach

Bourdieu’s capital approach is an attempt to unite the micro- and macro-perspective to understanding social inequality. Capital, as defined by Bourdieu, refers to the whole of means that can be used to effectively defend one’s place in society and possibly improve one’s position in relation to others [10, 11]. On the one hand, Bourdieu underscores the micro-perspective by stressing the importance of individual-level differences in volume and composition of economic, cultural, and social capital for explaining social inequality [11]. Economic capital refers to the resources of individuals which are directly exchangeable for money. Social capital refers to the relations and memberships someone can use to reach their goals. Cultural capital is a configuration of dispositions, behaviors, and accomplishments in cultural practices and value orientations. There are three kinds of cultural capital. Firstly, cultural capital can be embodied. This means that experiences of upbringing will be internalized as lasting tastes and dispositions. This type of cultural capital is closely related to the concept of “habitus”. Tastes, preferences, and certain kinds of (practical) knowledge may become a person’s ‘second nature’ over time. Secondly, cultural capital can be objectified, such as books, paintings, machines, and instruments. Thirdly, cultural capital can be institutionalized, such as titles and diplomas [11]. Research shows that people’s capital volume and composition are clearly related to a variety of outcomes in life such as job status, and even physical health [12].

On the other hand, Bourdieu’s theory of cultural reproduction also explains how capital volume and composition is dependent upon the societal structure (i.e., the macro-perspective). It is especially the concept of habitus that links the individual to the social [17]. He explains that the lower classes also have culture, but that their cultural background is of less value to succeed in life. This is because the dominant groups (i.e., the elite) determine what cultural background is valuable to succeed and because the cultural background of the dominant group is permeated in the institutions [17]. With institutions (e.g., educational systems; labor markets) mostly adapted to the culture of the dominant groups, individuals with a more valued cultural background (thereby higher “cultural capital”) can become more successful than individuals with a less valued cultural background (rendering them to have low cultural capital). In short, Bourdieu was able to introduce the cultural elements in highly deterministic macro-theories and is therefore known for attempting to incorporate human agency into structural theories of social inequality [18].

The notion of the different forms of capital draws attention to the transformation process, whereby one type of capital is used to acquire another. All types of capital can be transformed into another, but this typically takes time and/or effort and some types of capital (e.g., economic capital) are more easily transformed than others. Consequently, two individuals with the same level of one type of capital (e.g., economic capital) can lead totally different lives depending on their time and effort to acquire other types of capital. For instance, a person can invest money (economic capital) to get access to higher education (cultural capital). Each capital can be deployed for investments increasing wealth, knowledge, or the social network and thus ultimately to create more capital. This draws attention to the composition of capital (as different from the volume of capital) that people possess [10]. Based on the composition and volume of capital, Bourdieu defines different fractions in the social strata. For instance, among the middle class there is a fraction who are high in cultural and low in economic capital on the one hand and a fraction who are high in economic and low in cultural capital on the other hand.

In addition, the notion of the different forms of capital draws attention to the intergenerational transmission process, whereby parents transmit their social position to their children. Here too, both the volume and the composition are important. For instance, in general the middle class is more dependent on cultural capital (such as education), than on economic capital for inter-generational transmission of their social position (and the associated economic capital) [19]. There is transmission of resources across generations, through inheritance of material resources and through learning environments (especially schools). So, people come into educational systems with very different “starting” capitals. As a result, inequality increases in the long run.

In sum, people can collect more capital and different types of capital via their own deliberate efforts and via intergenerational transmission. Bourdieu uses the different forms of capital to highlight the fact that there are transformation and transmission processes between the different forms of capital (in all directions). Formulated differently, capital is a set of resources that can be accumulated or depleted over a lifetime and/or over generations. Therefore, capital is also referred to as accumulated history [11].

Positive psychological states as a form of capital

Although Bourdieu paid attention to individual differences when explaining social inequality, he neglected the role of individual dispositions, such as people’s differing tendencies to experience positive psychological states (PsyCap). Yet, this can play an important role in explaining social (im)mobility on the social ladder. For example, due to different dispositions and abilities, two children from the same family—having similar economic, social and cultural capital—can have very different educational outcomes [20]. Bourdieu’s approach falls short on this occasion, because the home environment (i.e., capital transmission) cannot fully explain the differential educational outcomes. While Bourdieu’s capitals are, by definition, a hundred percent socialized, PsyCap has a trait-based component [4]. This might better explain why some children have more chances to succeed, even though everything in their home environment worked against their success. These observations have often been treated as “marginal” and are therefore greatly undertheorized [4]. In Bourdieusian-inspired work, psychological states have often been treated as something that has to be explained, rather than something that has independent explanatory power [21].

However, PsyCap has a “nurture” component as well; through social input, learning and development, PsyCap can grow or decline [5]. Dóci et al. [3] drew attention to social inequalities in people’s access to PsyCap. They postulate that not everyone has the same chances for developing high PsyCap, but that it depends strongly on one’s social position. Dominant social groups will have a better chance to develop high PsyCap for various reasons. Firstly, these groups tend to be more positively perceived by their environment. Consequently, their repeated “success” experiences and the social confirmations they receive make it easier for them to generate positive PsyCap. Secondly, members of dominant social groups get more opportunities to prove their mastery, resulting in higher chances to increase their PsyCap. Thirdly, high-power individuals get more positive responses to their agentic and goal-striving behaviors, and they also have more access to differential pathways towards achieving their goals and attaining success [3].

Objectives

PsyCap can thus be considered a form of capital in the Bourdieusian sense. PsyCap fits nicely the interpretation of (1) “investment” (PsyCap translates into positive career outcomes or social status), (2) a mechanism of differentiation and unequal distribution (see above), (3) “accumulated history” (PsyCap can be transformed into other types of capital and intergenerationally transmitted), and (4) it cannot be confined to any other type of capital (because it is partly independent from socialization, for an elaborate theoretical argumentation see, Dóci et al. [4]). We empirically test the idea of PsyCap being a separate form of capital (Objective 1). On the one hand, to distinguish a form of capital, it needs to be related (correlated) to the other types of capital, because relations of transmission and transformation should be possible. On the other hand, PsyCap also needs to be relatively independent from other forms of capital (no substantial correlations), otherwise it cannot be seen as a different capital type. Based on the theoretical considerations outlined above, we hypothesize that PsyCap should be related to yet independent from the other types of capital.

In addition, based on the capital volume (i.e., having more or less) and composition (i.e., more or less of different types), fractions in the social strata can be defined. Previous research, using a transnational approach in Europe, has found groups with high economic capital (managers, professionals, entrepreneurs and executives), but varying levels of cultural capital, and groups with low economic capital (routine employees, industrial workers) having both high and low levels of cultural capital [22]. Another transnational study in Europe [13], found three groups. One with high levels and one with low levels on all the Bourdieusian capitals and, finally, a group with high informal social capital, but varying levels of economic and cultural capital. These results fit nicely with Bourdieu’s observation that there are meaningful class fractions within classes [17]. By including PsyCap into his social class framework, we expect to find a dominant class with high levels of Bourdieusian capitals, and high levels of PsyCap. Because repeated experiences of success affect PsyCap positively, we expect a positive relation between membership to a dominant group and high PsyCap. On the contrary, because repeated experiences of failure affect PsyCap negatively [23], we expect to find a group with low levels on all the forms of capital (including PsyCap). In addition, we expect to find fractions within the middle class with varying levels on the four types of capital. We set out to reconstruct the social space using all four forms of capital (Objective 2).

Finally, our last aim (Objective 3) is to examine cross-national differences in social classes distribution. The sizes of social classes within European countries are influenced by macro-level factors, such as a country’s level of economic development, income inequality and educational expansion [24, 25]. Countries with less economic development and high-income countries with large income inequality demonstrate larger (self-identified) low social classes [25]. Moreover, educational expansion can lead to large groups of high classes (with high volume of cultural and economic capital) [24]. Because there are transformation processes between all types of capital and because a country’s macro-characteristics influence capital volume and composition, it is hypothesized that there will be a greater proportion of social classes with low general capital volume and deprived capital composition in countries with low economic development (such as Ukraine, Kosovo and Albania), in economically developed countries with high income inequality (such as Israel, Southern European and Anglo-Saxon countries) and in countries with less developed educational expansion (such as Southern European countries), while a greater proportion of social classes with high general capital volume and rich capital composition will be found in their counterparts (such as Western and Northern European countries).

Materials and methods

Data

This study used data of the ESS, which is a biennial cross-national survey in Europe, conducted since 2001 (https://www.europeansocialsurvey.org), with changing thematic modules each year and collected via face-to-face interviews. Round 6, with participants recruited in 2012 and 2013, was used because this wave has the most recent module on personal and social well-being. The ESS6 module sought to incorporate a new validated scale of positive well-being and includes questions on well-being promoting behaviors. Therefore, ESS6 has the most recent and comprehensive scale on psychological capital. The ESS6 includes representative samples of persons aged 15 and over, who are resident in one of 29 European countries (Albania, Belgium, Bulgaria, Switzerland, Cyprus, Czechia, Germany, Denmark, Estonia, Spain, Finland, France, United Kingdom, Hungary, Ireland, Israel, Iceland, Italy, Lithuania, Netherlands, Norway, Poland, Portugal, Russian Federation, Sweden, Slovenia, Slovakia, Ukraine, Kosovo). All countries were included in the analyses. Respondents younger than 21 and older than 65 were excluded from the analyses, to ensure a relatively homogeneous group with similar chances to have completed education and to have a decent financial situation. As the current study utilized secondary data it was exempt from the ethical review process of the Ethics Committee Human Sciences of the Vrije Universiteit Brussel. The authors had no access to information that could identify individual participants during or after data collection.

Variables

Psychological capital

There are 6 questions in the ESS6 which can be used as indicators of a proxy measure of PsyCap: (a) At times I feel as if I am a failure, (b) In general I feel very positive about myself, (c) I am always optimistic about my future, (d) There are lots of things I feel I am good at, (e) When things go wrong in my life it takes a long time to get back to normal, and (f) How difficult or easy do you find it to deal with important problems that come up in your life. For the latter, answer categories ranged from 0 (extremely difficult) to 10 (extremely easy), while the other answer categories ranged from 1 (agree strongly) to 5 (disagree strongly). The missing data for these indicators did not reach 2%. Statements a, b, and d primarily tap into self-efficacy, item c primarily taps into optimism, and statements e and f primarily tap into resilience [26, 27]. Although in the original concept of PsyCap the dimension of hope is represented; in the ESS6, no direct measure for hope was included [26]. Hope consists of three components: agency, pathways, and goal [28]. Whereas pathways and goals are not well represented in our proxy measure, agency is to some extent reflected in the item "How difficult or easy do you find it to deal with important problems that come up in your life". In addition, optimism–included in the ESS6 –refers partly to hopefulness (namely the emotional facet) [27].

All questions were rescaled to the same range and recoded so that a high score reflects high PsyCap. The indicators related to each subscale of PsyCap were added up to create separate scales for efficacy, resilience, and optimism. We conducted a principal axis factor analysis with varimax rotation of the three subscales. The purpose of the analysis was to test the number of factors needed to adequately describe the data. In the factor analysis we found reasons for constructing a single indicator for PsyCap. The factor extracted had an eigenvalue of 1.79 and accounted for 59.7% of the variance. It was the only factor with an eigenvalue above 1. The factor analysis shows strong convergence of the subscales. The factor loadings on the first factor are .78 for efficacy, .61 for optimism and .50 for resilience. In addition, the Cronbach’s alpha for the PsyCap scale was close to .70 (Cronbach’s α = .64). When calculating the Cronbach’s α for each country separately, these were all close to or higher than .70 (except for Switzerland (.34), Italy (.52), Portugal (.53), Russia (.59) and Kosovo (.55)). Yet, when doing the factor analysis for each country separately, each of the 29 countries showed only one factor with an eigenvalue above 1. Based on these empirical considerations, we are confident that the subscales point to a unified underlying construct. Therefore, three subscales were summed to create the PsyCap scale (range 0 to 1, a high value represents a high PsyCap). For the LCA, this variable was recoded into three categories based on its tertiles: ‘low’; ‘middle’ and ‘high’.

Bourdieusian capitals

Previous research [13, 17, 22] offered the groundwork for the selected measurement items used in our study. To adequately test the transferability between different capitals, we selected measures that distinctly represent only one form of capital. Therefore, items that reflect two capital dimensions are excluded. For instance, occupational status reflects both cultural capital (as educational attainment often is a prerequisite for access to certain professions) and economic capital (as a job is a primary means of income and financial security) and is subsequently omitted from our analyses. In addition, we limited the number of measurement items for each capital dimension to maximum two to balance the LCA and simplify its interpretation, while still capturing the essence of each capital type.

Cultural capital was operationalized using respondents’ educational level (missing values .6%). The respondents were grouped into three educational categories according to the International Standard Classification of Education (ISCED): ‘low’ (up to lower secondary); ‘medium’ (up to post-secondary non-tertiary); and ‘high’ (completed tertiary education). We also included the parent’s education (missing values 5.5%), which is the level of educational attainment of the respondent’s most highly educated parent (see also [17]), with three categories (based on ISCED); ‘low’ (up to lower secondary); ‘medium’ (up to post-secondary non-tertiary); and ‘high’ (completed tertiary education). We include the educational level of the most highly educated parent, because cultural capital is to a large extent embodied within the parent and through upbringing transferred [29].

Economic capital is measured using the respondents’ financial situation. This is measured using a question on the perception of the current household income being sufficient or not (missing values 1.5%). Measuring income sufficiency at the household level is appropriate, since income–although related to individual’s situations–is a concern mostly situated at the household level. A partner’s income might make up for a low income, or a high income might be insufficient because of high costs. The answer categories were: (1) Living comfortably on present income; (2) Coping on present income; (3) Difficult on present income and (4) Very difficult on present income. This variable was reversed so that a high value represents the best financial situation and categories 3 and 4 were taken together to simplify the interpretation of the models. Income measured at the household level has been done previously in social class analyses [17]. In our analyses, the net income of the household was not included due to many missing values (over 20%).

Social capital was operationalized using two indicators, weak and strong ties [30]. Firstly, a question measures strong ties (missing values .9%): “To what extent do you receive help and support from people you are close to when you need it?” Answer categories range from 0 (not at all) to 6 (completely). For the LCA, this variable was recoded into three categories based on its tertiles: ‘low’ (0 to 4); ‘middle’ (5) and ‘high’ (6). Secondly, a question measures weak ties (missing values 3.4%): “To what extent do you take part in social activities compared to others of same age?” The answer categories were: (1) much less than most; (2) less than most; (3) about the same; (4) more than most and (5) much more than most. For the LCA, this variable was recoded into three categories: (much) less than most; about the same and (much) more than most.

Analyses

Our total sample comprised 35,313 respondents (non-weighted, with listwise deletion of missing data). We first described the population using percentages, means and standard deviations (SD). These analyses were performed using SPSS version 28.

To test whether PsyCap is a form of capital (Objective 1), some conditions needed to be fulfilled, which were: (1) it needed to be correlated to the other types of capital. By the same token (2) the correlation should not have been too high, as this would have made the idea of adding another type of capital redundant. We deployed different modes of analysis to investigate this. First, we tested Spearman rank correlations between PsyCap and the indicators that make up the Bourdieusian capitals. To account for the nested structure of our cross-national data, we computed multilevel Spearman correlation coefficients. We deemed correlation coefficients that were between zero and < .2 as weak, between .2 and < .4 as moderate and above that as strong [31]. These analyses were performed with the correlation package [32], version 0.6–12 using the R software [33].

Second, using Confirmatory Factor Analysis (CFA), we tested whether the scales of PsyCap were empirically distinct from the indicators belonging to the other forms of capital. To that end, we compared the fit of four competing models. In the first model, we let all items of PsyCap load onto the latent variable for cultural capital, while also including the single-item for economic capital and the latent variable for social capital. In the two subsequent models, the items of PsyCap loaded on a latent variable including the single item of economic capital on the one hand, and on the latent variable for social capital on the other hand (while also including the other types of capital in the model). In the last model, we let the items of PsyCap load onto the latent variable PsyCap, while also including a single item for economic capital and two latent variables for cultural capital and social capital. We assessed the fit of the different models using the following fit indices: root mean square error of approximation (RMSEA) ≤.05 indicated a good fit to data and .05<RMSEA < .08 indicated a satisfactory fit [34]. Comparative fit index (CFI) ≥.95 and standardized root mean square residual (SRMR) < .09 indicated a good fit to data [34]. This was investigated with the Lavaan package [35], version 0.6–12 using the R software [33].

The different types of social positions based on the four distinct capitals (Objective 2) were assessed using LCA [36] (Objective 2). For this, Latent Gold 5.1TM software was used. LCA [37] was conducted on the six capital indicators. Moreover, we have taken the nested structure of our data into account, by including direct effects of the country variable on item responses. As a result, the unique meaning of the item responses is filtered out by the direct effects of the country indicator on each item [38].

LCA uses the distribution of the indicators over the sample to create an empirical typology of–in this case–social positions among the European population. In other words, individuals included in the sample are rearranged in a limited number of groups (classes), based on their degree of similarity on manifest indicators reflecting their capital situation. There are social positions in the population, but they are unobserved (latent). We start with a one-class model and then fit n models. For selecting the final model and subsequently the number of social classes, two criteria are taken into consideration: (1) the best-fitting model based on statistical criteria and (2) theoretical meaning of the model [39]. Regarding the former, the best-fitting model is obtained by evaluating the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC) and the Consistent Akaike Information Criterion (CIAC) [37]. The lower the AIC, BIC, and CAIC, the better the model fits the data (that is, the more accurate the relationships predicted by the model represent the real pattern of relations observed in the data). When comparing the changes in AIC, BIC, and CAIC for each model, a clear drop in improvement of the changes suggests model saturation [39]. Regarding the theoretical interpretation, we examine the relations between the latent classes and capital-indicators (the conditional probabilities) for three statistically acceptable models to assess the meaningfulness of each extra latent class.

Finally, the country-specific distribution of the social class typology is described (Objective 3), using cross-tabulations of the mean cluster probabilities. To test our hypotheses, we have included the Gross Domestic Product (GDP) per capita (in US $), the GINI index and the total government expenditure on education (% of GDP) for each country in 2012, to represent a country’s economic development, income inequality and educational expansion, respectively. These indicators were obtained from the World Bank [40].

Throughout all analyses, data have been weighted using the analysis weights provided by the ESS6, resulting in a corrected sample size of 35,489. These weights correct for differential selection probabilities within each country as specified by sample design, for nonresponse, for noncoverage, and for sampling error related to the four post-stratification variables and considers differences in population size across countries [41].

Results

Descriptive analyses

Table 1 shows the general characteristics of the sample. There are 48.1% men and 51.9% women in the sample and the mean age is 43.2 years (SD = 12.8). The mean of PsyCap is 0.65 (SD = 0.15). As regards the Bourdieusian capitals, most of the sample is middle educated (41.4%) and their highest educated parent is in most cases low educated (44.4%). Most respondents are coping on the present income (44.4%) and have a good network of strong ties (mean 4.92, SD 1.25). Few respondents have a good network of weak ties (15.4% belongs to the group indicating to participate (much) more than most in social activities).

10.1371/journal.pone.0310031.t001 Table 1 Frequency table, ESS6, 29 countries, age 21–65, weighted (N = 35,489).

Indicator	N	%	
Gender			
Male	17,067	48.1	
Female	18,422	51.9	
Age	43.2 (mean)	12.8 (SD)	
PsyCap (range 0–1)	.65 (mean)	.15 (SD)	
Low tertile	11,754	33.1	
Middle tertile	12,152	34.2	
High tertile	11,583	32.6	
Cultural capital			
Own education			
Low	12,859	36.2	
Middle	14,692	41.4	
High	7,939	22.4	
Parent’s education			
Low	15,771	44.4	
Middle	14,471	40.8	
High	5,247	14.8	
Economic capital			
Perception of income			
(Very) difficult	12,028	33.9	
Coping	15,761	44.4	
Comfortable	7,700	21.7	
Social capital			
strong ties (range 0–6)	4.92 (mean)	1.25 (SD)	
Low tertile	9,716	27.4	
Middle tertile	11,320	31.9	
High tertile	14,453	40.7	
weak ties (range 1–5)	2.69 (mean)	.93 (SD)	
(much) less than most	13,301	37.5	
About the same	16,712	47.1	
(much) more than most	5,476	15.4	
Abbreviation: SD: standard deviation

How does PsyCap relate to other forms of capital?

Table 2 shows the multilevel Spearman correlations. We found weak positive correlations between PsyCap and the indicators for cultural capital (r ≤ .14). Positive moderate correlations were found between PsyCap and the indicator for economic capital on the one hand, and two indicators for social capital on the other hand (r ≤ .24). This suggests that PsyCap can be considered a relatively independent form of capital. Yet, because of the (weak/moderate) overlap, we can also infer that PsyCap is also somewhat related to the same social mechanisms (e.g., transformation) as the Bourdieusian forms of capital.

10.1371/journal.pone.0310031.t002 Table 2 Multilevel spearman correlation coefficients between the different indicators of Bourdieusian capital and psychological capital, ESS6, age 21–65, 29 countries, weighted (N = 35,489).

	PsyCap (0 → 1)	Cultural capital	Economic capital	
Own’s education	Parent’s education	Perception of income	
r	p-value	r	p-value	r	p-value	r	p-value	
Cultural capital									
Own education	.14	< .001	na	na	na	na	na	na	
Parent’s education	.11	< .001	na	na	na	na	na	na	
Economic capital									
Perception of income	.23	< .001	.26	< .001	.15	< .001	na	na	
Social capital									
Strong ties	.24	< .001	.06	< .001	.06	< .001	.11	< .001	
Weak ties	.21	< .001	.12	< .001	.08	< .001	.13	< .001	
Abbreviations: na: not applicable

Table 3 shows that the model with the items of PsyCap loading on a separate latent variable for PsyCap had a significant better model fit than the three capital models. The three-capital model with the PsyCap items loading on a factor “cultural capital” had an inadequate model fit, with the RMSEA-, and CFI-values showing unacceptable levels (See Fig A in S1 Text for the structural model). The three-capital models, with the PsyCap items loading on a factor “economic capital” and “social capital” respectively, had an inadequate model fit with CFI-values < .95 (see Figs B and C in S1 Text for the structural model, respectively). In contrast, the four-capital model with a separate factor for PsyCap shows acceptable levels on the RMSEA-, SRMR-, and CFI-values (see Fig D in S1 Text for the structural model). Based on this information, we conclude that the four-capital model fits our data best.

10.1371/journal.pone.0310031.t003 Table 3 Test of model fit of three-factor and four-factor capital models, ESS6, age 21–65, 29 countries, weighted (N = 35,489).

	Model fit	Change in model fit (4-capital model vs. 3-capital model)	
SRMR	RMSEA	CFI	Δχ2/Δdf	p-value	
Model with items of PsyCap loaded on a factor ‘cultural capital’	.07	.11	.72	2290/3	< .001	
Model with items of PsyCap loaded on a factor ‘economic capital’	.04	.06	.92	260/2	< .001	
Model with items of PsyCap loaded on a factor ‘social capital’	.03	.05	.94	84/3	< .001	
Model with separate factor for PsyCap (4-capital model)	.03	.05	.95	na	na	
Abbreviations: Δχ2: change in Chi-square; Δdf: change in degrees of freedom; na: not applicable

Types of social positions based on a four-capital model

Next, we look for different types of social positions based on the four distinct capitals using LCA. Therefore, we firstly need to determine how many types of social positions are present among our respondents. Table 4 provides summary statistics for the LCA models. The table describes models with one to eight latent classes, using the six indicators of capital discussed above. Going up to a 7-class model showed a decrease in change in BIC, AIC, and CAIC. This indicates that the 7-class solution gives us an optimal level of model fit/ model parsimony balance. In a next step, we interpreted a 5-, 6- and 7-class solution. We were able to make the most sensible interpretation using the 6-class solution. When comparing the 5-class to the 6-class solution, the 6-class solution adds a profoundly different profile. Yet, when comparing the 6-class to the 7-class solution, the 7-class solution adds a profile with a very small cluster size (6%) and no profoundly different profile. Based on this information, we decided that the 6-class model best represents reality.

10.1371/journal.pone.0310031.t004 Table 4 Comparison of selected fit indices and degree of model improvement over the different latent class models, ESS6, age 21–65, 29 countries, weighted (N = 35,489).

Model	BIC	AIC	CAIC	ΔBIC	ΔAIC	Δ CAIC	
1 class	432857.6	431331.8	433037.6				
2 classes	418877.4	417054.9	419092.4	13980.2	14276.9	13945.2	
3 classes	416234.9	414115.6	416484.9	2642.6	2939.3	2607.6	
4 classes	414817.8	412401.8	415102.8	1417.1	1713.8	1382.1	
5 classes	413785.5	411072.8	414105.5	1032.3	1329.0	997.3	
6 classes	413312.9	410303.5	413667.9	472.6	769.3	437.6	
7 classes	413054.1	409748.1	413444.1	258.7	555.4	223.7	
8 classes	413180.5	409577.7	413605.5	-126.3	170.4	-161.3	

Table 5 shows the conditional probabilities for the final capital typology, i.e., the 6-class solution. These conditional probabilities contain information on the associations between the latent capital types and the constituting manifest proxy-indicators. Based on these relationships, the types of social positions are given their substantive interpretation, which is reflected in their names.

10.1371/journal.pone.0310031.t005 Table 5 6-class model: Distribution of class conditional probabilities over capital indicators, 29 countries, age 21–65, weighted, ESS6 (N = 35,489).

 	Class 1	Class 2	Class 3	Class 4	Class 5	Class 6	Overall	
	high PsyCap/social/economic capital	Low-educated deprived	Middle-educated deprived	Upward well-off	Generationally well-off	high PsyCap/social/cultural capital		
Cluster Size	29%	25%	16%	11%	10%	9%		
Indicators								
PsyCap								
Low tertile	0.1441	0.5618	0.5764	0.2348	0.2621	0.0808	0.3312	
Middle tertile	0.3497	0.3255	0.3198	0.3884	0.3915	0.2953	0.3424	
High tertile	0.5062	0.1126	0.1038	0.3768	0.3465	0.6239	0.3264	
Mean	2.3622	1.5508	1.5275	2.142	2.0844	2.5430	1.9952	
Cultural capital							
Own’s education							
Low	0.5294	0.6986	0.1763	0.0006	0.0001	0.0809	0.3623	
Middle	0.4568	0.2969	0.7245	0.187	0.0778	0.7027	0.4140	
High	0.0139	0.0045	0.0992	0.8124	0.9221	0.2164	0.2237	
Mean	1.4845	1.3059	1.9230	2.8118	2.9220	2.1355	1.8614	
Parents education							
Low	0.6127	0.8329	0.0272	0.4756	0.0005	0.0096	0.4444	
Middle	0.3799	0.1660	0.7697	0.5072	0.2009	0.6280	0.4078	
High	0.0074	0.0011	0.2031	0.0172	0.7985	0.3624	0.1478	
Mean	1.3947	1.1682	2.1759	1.5416	2.7980	2.3528	1.7034	
Economic capital							
(Very) difficult	0.2068	0.5374	0.5633	0.1037	0.1608	0.3200	0.3389	
Coping	0.5035	0.3965	0.3682	0.4644	0.4549	0.4767	0.4441	
Comfortable	0.2897	0.0661	0.0684	0.4319	0.3843	0.2033	0.217	
Mean	2.0829	1.5288	1.5051	2.3282	2.2235	1.8833	1.8781	
Social capital							
Strong ties								
Low tertile	0.1432	0.4047	0.4615	0.2130	0.2471	0.1208	0.2738	
Middle tertile	0.2982	0.3391	0.3270	0.3326	0.3367	0.2817	0.319	
High tertile	0.5586	0.2562	0.2115	0.4544	0.4162	0.5975	0.4072	
Mean	2.4154	1.8515	1.7500	2.2414	2.1691	2.4767	2.1335	
Weak ties								
(much) less than most	0.2780	0.5437	0.4357	0.3152	0.3309	0.2428	0.3748	
About the same	0.5098	0.392	0.4571	0.5037	0.4991	0.5137	0.4709	
(Much) more than most	0.2122	0.0643	0.1071	0.1811	0.1700	0.2435	0.1543	
Mean	1.9341	1.5206	1.6714	1.8659	1.8391	2.0007	1.7795	
Capital volume *	11.6738	8.9258	10.5529	12.9309	14.0361	13.392	11.3511	
* Capital volume = sum of means of the six capital-indicators

The first capital-constellation, the high PsyCap/social/economic capital group, stands out due to their high score on PsyCap and relatively high probabilities of receiving help from close contacts and engaging in (many) more social activities. This class exhibits the highest probabilities of coping on the present income and higher probabilities than the overall mean of living comfortable on the present income. They show low probabilities of belonging to the high-educated and high-educated parents’ group. This group compromises 29% of the sample.

The second capital-constellation type, labelled as the low-educated deprived, exhibits generally low levels of capital, for all types of capital. This class comprises 25% of the sample. Their probabilities of belonging to the low-educated and the low-educated parents’ group are higher, compared to other classes. They have the highest probability of finding it (very) difficult to cope with the present income, compared to other clusters. Their social capital is marked by the highest probabilities of belonging to the tertile with the least experiences of receiving help and engaging in (much) less social activities than most. In addition, their mean score on PsyCap is notably lower compared to the overall mean.

The third capital-constellation, the middle-educated deprived, is characterized by the lowest mean score on PsyCap compared to the overall mean. This class shows high probabilities of having middle-educated parents and being middle-educated themselves. This class also demonstrates the highest probabilities of finding it (very) difficult to cope with the present income and the lowest probabilities of belonging to the tertile with the best experiences of receiving help, compared to the overall mean. Finally, they exhibit the second-highest probabilities of engaging in (far) fewer social activities than most. Approximately 16% of the sample falls into this class.

The fourth capital-constellation, referred to as the upward well-off, is characterized by the highest probabilities of tertiary education, alongside the lowest probabilities of having high-educated parents. They exhibit the highest probabilities of living comfortably on the present income. In terms of strong ties, they have slightly higher probabilities of belonging to the middle and high tertile group, compared to the overall mean. Regarding weak ties, their probabilities of engaging in about the same or (many) more social activities than most are slightly higher, compared to the overall mean. Additionally, their mean score on PsyCap is the third highest among all clusters. This group consists of approximately 11% of the sample.

The fifth capital-constellation, labeled the intergenerationally well-off, is distinguished by the highest probabilities of tertiary education and having tertiary-educated parents. They also display the second highest probabilities of living comfortably on the present income. In terms of strong ties, they show slightly higher probabilities of belonging to the middle and high tertile group, compared to the overall mean. As regards weak ties, they exhibit slightly higher probabilities of engaging in about the same or (many) more social activities than most, compared to the overall mean. Their mean score on PsyCap is slightly higher than the overall mean. This group consists of approximately 10% of the sample.

Finally, the high PsyCap/social/cultural capital group, is marked by their highest mean score on PsyCap and the highest probabilities of positive experiences of receiving help and engaging in (much) more social activities. Otherwise, this class seems to show low probabilities of living comfortably on the present income. They also exhibit low probabilities of belonging to the low-educated and low-educated parents’ group. This group consists of approximately 9% of the sample.

Country-specific distribution

Table 6 shows clear differences between the 29 European countries regarding the distribution of class probabilities. Within economically less developed countries, i.e., with low GDP per capita (such as the Eastern and South-Eastern European countries), the probability to belong to the high PsyCap/social/economic capital class is zero or close to zero and the probability to belong to Middle-educated deprived class is high. In economically developed countries, the probability to belong to the high PsyCap/social/economic capital class and the two well-off social classes is higher, compared to their counterparts. However, even though the probability to belong to the Upward well-off class is the highest in Norway, it is also high in Portugal, Spain, and Poland. Moreover, a high probability to belong to the Generationally well-off class is also found in Hungary, Slovenia, and Slovakia. For countries with a (relatively) low GDP per capita and low expenditure on education (such as Southern and South-Eastern European countries) and for economically developed countries with high income inequality, the probability to belong to the Low-educated deprived class is high. Finally, the highest probabilities of belonging to the high PsyCap/social/cultural capital class are found in Finland and Eastern European countries.

10.1371/journal.pone.0310031.t006 Table 6 Distribution of the class probabilities over four-capital social classes within 29 countries for persons between 21 and 65 years old, ESS6.

	Macro-indicators	Class probabilities	
 	GDP per capita (US$)	GINI	Educational Expansion1	Low-educated deprived	Middle- educated deprived	High Psy/soc/eco cap.	Upward well-off	High Psy/soc/cult cap.	Generationally well-off	
Kosovo	3,410	29.0	n.a.	47	8	15	4	25	2	
Ukraine	4,004	24.7	6.44	10	37	8	0	35	10	
Albania	4,247	29.0	3.31	70	1	6	15	3	4	
Bulgaria	7,430	36	3.48	35	20	0	2	31	12	
Hungary	12,984	30.8	4.14	24	50	0	0	6	20	
Poland	13,010	33.0	4.86	35	1	30	24	1	8	
Lithuania	14,367	35.1	4.76	33	23	0	4	31	9	
RussianFederation	15,420	40.7	3.79	23	38	12	0	23	3	
Estonia	17,403	32.9	4.72	17	22	2	5	39	16	
Slovakia	17,498	26.1	3.86	15	57	0	0	9	18	
Czechia	19,870	26.1	4.22	1	62	0	0	24	13	
Portugal	20,563	36.0	4.95	54	0	7	33	0	7	
Slovenia	22,641	25.6	5.62	17	36	4	0	25	18	
Spain	28,322	35.4	4.47	36	1	22	26	0	15	
Cyprus	28,910	34.3	5.92	42	11	18	22	0	7	
Israel	33,156	41.3	5.59	29	14	26	11	9	11	
Italy	35,051	35.2	4.06	44	10	27	7	5	8	
France	40,870	33.1	5.46	23	4	51	12	2	9	
United Kingdom	42,497	33.1	5.63	22	6	38	20	1	13	
Germany	43,855	31.1	4.93	16	0	58	18	2	7	
Belgium	44,670	27.5	6.26	25	13	26	17	1	19	
Iceland	45,995	26.8	7.58	0	17	39	24	6	13	
Finland	47,708	27.1	7.15	0	5	39	6	41	9	
Ireland	48,943	33.2	6.162	32	7	37	15	0	8	
Netherlands	50,070	27.6	5.41	36	7	22	20	0	15	
Sweden	58,037	27.6	7.57	11	15	39	15	7	12	
Denmark	58,507	27.8	7.24	6	2	48	17	10	15	
Switzerland	85,836	31.6	4.90	10	0	59	20	0	10	
Norway	102,175	25.7	7.33	0	10	33	33	6	18	
1Government expenditure on education, total (% of GDP); 2 Data from 2009 (data 2012 unavailable); n.a.: not available

Discussion

The aim of this paper was to assess whether PsyCap can be empirically integrated into the Bourdieusian capital framework and to investigate the existing social positions using this extended capital approach in a large and representative European database. Regarding the first aim, our study provides empirical support to recent theoretical claims calling to include PsyCap into Bourdieu’s capital framework [3, 4]. To the best of our knowledge, no scholars have empirically tested these theoretical claims yet.

The integration of PsyCap within Bourdieu’s framework contributes to addressing social inequalities in various ways, both from a psychological and a sociological perspective. Regarding the former, our results emphasize that understanding differences in PsyCap must go beyond attributing them to individual differences and individual efforts, and needs to incorporate external, societal influences. We have highlighted between-group differences in PsyCap where some groups consistently exhibit higher average levels, probably due to shared, beneficial social and contextual factors. Ignoring structural, between-group differences in PsyCap downplays the impact of enduring social inequalities on people’s psychological resources and well-being [4]. Thus, for psychological research, our results call for a shift in research focus from the intra- and inter-individual level to understanding PsyCap as a social and group-level phenomenon as well.

From a sociological perspective, our study offers two key contributions for research on social inequalities. Firstly, our study advocates the use of positive psychological states as explanatory variables rather than outcomes, a departure from the common practice in the current state of sociological research. This perspective views positive psychological states as a resource and a principle of distinction, unequally distributed in society, with potential investments for returns [4]. Secondly, integrating PsyCap into the Bourdieusian framework can help to address the longstanding issue of understanding the relationship between social and individual differences in the study of social inequalities. Sociological studies often focus on the intergenerational transmission and reproduction of social differences, but the concept of PsyCap introduces the idea of individual differences (as it has a trait-like component), providing a better framework to understand variations within families [4]. More specifically, it may help to explain intra-familial differences in social mobility patterns.

Finally, our study adds to the ongoing discussion in psychological science on how to appropriately conceive and measure social class. At the moment, this matter remains unsettled, with psychologists rarely defining social class theoretically [42]. We offer a theoretical underpinning of social class combining insights from both classical sociological theory and psychology. Beneficial about the capital approach is that it focusses more on the underlying dimensions than the exact combinations (i.e., class fractions) present in a specific population. This avoids unproductive discussions about whether specific classifications are still relevant and makes it easier to study processes of class formation with data from different contexts. Indeed, one should not forget that a key characteristic of the capital approach is that it draws attention to the underlying transformation processes rather than the specific outcomes. This reorientates the question from why is there social inequality to why is social inequality so persistent.

In addition, within Europe, we found six types of social positions (Objective 2): Two types with overall high capital volume and two with overall low capital volume. Two social positions have an “imbalanced” capital composition: With relatively high levels of PsyCap and social capital, but varying levels of economic and cultural capital. The existence of the latter social positions has several implications. They are, for example, clearly relevant in light of the insights derived from resistance theories (see amongst others Knight Abowitz (2000)) for defining social classes. According to these theories, social groups in a weak social position rely on different coping strategies to make life bearable. One of these coping mechanisms is the participation in a subculture or an in-group community in which the (otherwise vulnerable) self-image of its members can be protected [43]. Our findings suggests that such coping and compensation strategies might be at work for those deprived of cultural or economic capital and that these strategies might provide groups of a relatively high volume of capital (or means of power) in society. This finding also underscores the importance of the association between social capital and PsyCap. Social interactions heavily influence people’s self-image, sense of self-efficacy, and their general view on the future [44]. Likewise, people with a high volume of PsyCap are more likely to initiate and develop resourceful social relations with others [6].

As regards the deprived types, we found a low-educated and a middle-educated class. The overall low volumes of capital of these social positions can be linked to, amongst others, the centrality of education in what we call the “schooled society” and the importance of education in the access to a wide range of outcomes, including aspects of socio-economic and cultural position [45]. Education can promote feelings of entitlement, that is, the feeling that one deserves to be successful in life, which in turn might lead to positive states like self-confidence etc. [46].

Lastly, two well-off types were found. Because high PsyCap is both the result and the origin of success experiences, it is not surprising that relatively high PsyCap is found among people in well-off types of social positions. These results highlight that PsyCap is unequally distributed in society and that PsyCap is a form of capital, which helps explain the endurance of social hierarchies.

Finally, our results also point in the direction that country policies and economic conditions play a significant role in the development and distribution of social classes. More than half of the European population has no tertiary education, reflected in large classes with low cultural capital: the high PsyCap/social/economic capital class and the two deprived classes. In countries where governments allocate large funds to education systems (like Norway, Finland and Denmark) [16], classes with low cultural capital were less prevalent. In line with our hypotheses, in economically developed countries like Western and Northern Europe, the high PsyCap/social/economic capital class was common. This suggests that these countries can facilitate a relatively well-off social position, despite low cultural capital. In economically developed countries with high GINI indexes, the prevalence of deprived classes for low-educated is noteworthy. In such contexts, when insufficient initiatives are undertaken to mitigate income inequality, stark distinctions emerge between those possessing cultural capital and those without. Yet, more research is needed to dig deeper into differences in social classes distributions across countries.

Several limitations are present within this study. Firstly, the cross-sectional data of 2012 can be considered relatively old. However, the unique module on personal well-being of the ESS6 is to our knowledge the most suitable available data source for our research objectives. Secondly, using ESS6 we were unable to capture all aspects of the Bourdieusian capitals, such as objectified cultural capital (e.g., the possession of books or paintings), embodied cultural capital (e.g., tastes in music and art), or alternative measurement approaches for economic capital (e.g., the ownership of valuable goods, such as a home or a car) [11]. Regarding social capital, ESS6 does provide more indicators (such as civic participation or trust) [13]. Yet, to keep the models balanced, we decided to utilize a maximum of two indicators for each type of capital. The inclusion of alternative measurement approaches would allow a more nuanced and comprehensive view of the Bourdieusian capitals, which could impact the social class cluster solution, e.g., showing more nuanced within-class differences. Unfortunately, we were unable to assess the potential impact of including alternative measurement approaches.

Additionally, the indicator for PsyCap is only a proxy for the underlying theoretical concept and the measurement of the dimension hope was suboptimal. The lack of ‘hope’ in our PsyCap construct can potentially skew our results as PsyCap is a second-order construct based on shared commonalities of first-order psychological resources (hope, resilience, efficacy, and optimism), but also based on their unique characteristics [47]. The discriminant validity of these constructs has been empirically established [48]. Unfortunately, the ESS data do not include a proxy for the ‘hope’ element of PsyCap. Given that we used PsyCap to define clusters, our analysis may have missed a social class cluster distinctly characterized by waypower, which is unique to hope. This ‘overlooked’ social class might also have a specific cross-country distribution as the level of hope varies between countries and may be related to policies like government spending on education and social services [49]. Whether such a cluster exists and how it is distributed over countries is an empirical question that we cannot answer with the available data. There are, however, certain indications that suggest that the component ‘hope’ does not distinctly dominate profiles within populations. Indeed, studies based on person-centered latent profile analyses of the PsyCap components show a gradient in the profiles (ranging from low, to moderate to rich (general) PsyCap) [50–52] or in other cases the other PsyCap components (optimism or resilience) seem to dominate the profiles [53, 54]. Our research shows that there is much to be gained by including PsyCap in comparative research and recommends that a more comprehensive measure for PsyCap is included in large-scale survey projects like the ESS.

Thirdly, we were unable to use the objective measure of income, because of a high number of missing values. Yet, objective income is very much influenced by the country one lives/works in. This might be problematic in a transnational study because one would capture national differences rather than differences in economic capital. Fourth, the scope of this article included all individuals aged 21 to 65. Yet, other demographic groups might be of interest, such as specific age groups (e.g., those at the beginning of their career), the retired, workers, children, etc. Finally, there is the issue of how the LCA solution (or social class typology) would be different if tested within each country separately. Unfortunately, doing a cross-national validation fell out of the scope of this research, as the primary focus was to find support for previous theoretical claims and centered on examining the broad applicability of the social class framework. Nevertheless, we allowed for direct country effects on the indicators when estimating the latent structure. Despite these limitations, the use of a large representative European dataset ensures the generalizability of our results to the European population.

Conclusion

Our study provides empirical support, using a large representative European dataset of 29 countries, to recent theoretical claims calling to include PsyCap into the Bourdieu’s capital framework. Six different social fractions were found, based on the ‘integrated’ social class framework, in Europe. These social fractions show that high PsyCap is unevenly divided among social groups. Our results indicate that country policies and economic conditions significantly influence the development and distribution of social classes.

Supporting information

S1 Text Structural models.

(DOCX)

The authors would like to thank Julie Vanderleyden for help with the cluster analyses.

10.1371/journal.pone.0310031.r001
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PONE-D-23-17518Why can’t we just be more positive? A capital approach towards positive psychological states and their role for explaining social inequalitiesPLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study investigates whether Psychological Capital can be seen as part of the Bourdieusian capital framework including, social, cultural and economical capital. The researchers used existing European panel data to operational the 4 different capitals and investigated 1) whether a confirmatory factor analysis could show a better fit for a 4 factor structure in the data than for a 3 factor structure (whether PsyCap is related to other capitals but conceptually distinct from them and therefore should be included when 'defining' social class). The authors indeed found evidence for this 4 factor structure. Thereafter, they performed latent profile analyses using the 4 different capitals to see whether the data shows latent subgroups. Results showed 6 different latent subgroups, with a different composition on scores on the 4 different capitals included. the researchers also should descriptive results for the different countries included.

The manuscript is well written and contributes to theory development in relation to social class. A large sample was used including several European countries. However, what diminished my enthusiasm for the paper was the following:

1. In the Introduction section, the authors do not explain why they included several European countries in their study, and also do not discuss that the way social class is defined also depends on context. Later on, also country specific results are discussed, and I think this could be also included in the aim of the study.

2. The Methods and analyses used are, to my knowledge, sophisticated. Nevertheless, the section on how authors included the survey weight is unclear (was this weight determined by the authors or by researchers of the European social survey (if yes, I miss a reference), what exactly is controlled for in this weight?). That 2 times a different N is presented in the first paragraph of the analyses is also unclear (how come the first N is lower then the second one??).

3. I was wondering why the authors did not transform their items into ridit-scores, so they can actually be compared over countries. I think, if I understood it correctly, the authors now base their categorization of scores in to low-mid-high on the different items measuring capital on pooled data from all the European countries together. It would have been more comparable over countries to first calculate ridit scores for each item per country before including them into one analyses. The authors only argue that objective income could be influenced by the country one lives in, so it might even be 'better' to use the subjective measure they used, but this is also true for other capitals and it would have been better to take this into account when performing analyses.

4. A lot of the Discussion section is also description of results and is very long. I think this can be more concise and also for a great part presented under results.

5. I think authors use very firm description of and statements about the different latent clusters (also implying causality) in their result and discussion section, however the different items used to measure the different capitals are not optimal (as mentioned in the discussion as a limitation) and it is cross-sectional/correlational data, making statements about causality impossible. the authors should really be more careful in formulating their conclusion. This will also make the discussion more concise.

Reviewer #2: The paper addresses a significant research gap by proposing the integration of psychological capital (PsyCap) into Pierre Bourdieu's capital framework to better understand social inequality. The paper demonstrates a comprehensive understanding of the theoretical background and offers well-defined research objectives. However, there are two areas that need further attention to enhance the paper's quality before publication in PLOS One.

Measurement Tools for Capital

- The paper raises a valid concern about the limited measurement tools for different capitals (31). As the authors suggested, it is essential to acknowledge the limitations of the selected measurement tools and consider exploring alternative methods to ensure the robustness of the findings.

- The paper would benefit from a discussion of potential alternative measurement approaches to strengthen the validity of the research results. For instance, if objective income measures are not comparable across countries, the authors may consider income percentile calculator depending on each country’s income distribution.

- Similarly, while the authors do acknowledge the limitations of cultural capital measures, I still believe it is crucial to discuss the anticipated outcomes if additional indicators are incorporated.

- Due to the potential for results to vary significantly depending on the chosen measurement tool, I believe it is necessary to validate using a wider range of measurement tools.

Significance and Implications

- While the paper rightly acknowledges the research gap in empirical testing of PsyCap within Bourdieu's framework, it could provide a more explicit discussion of the significance of this integration. How can this integration contribute to addressing social inequality and understanding social classes? Clarifying the broader implications of the research would make the paper more compelling.

- For instance, when discussing the "invisible" social positions, elaborate on what makes them invisible, particularly within the context of existing sociological theories. Explain how the traditional Bourdieusian framework may overlook these positions and why they are significant.

- While the authors mentioned the issue of cross-national validity, it would be beneficial to elaborate on the implications of this potential limitation. Discuss how cross-national variations in social policy, economic conditions, or cultural factors may influence the distribution of PsyCap and social positions.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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10.1371/journal.pone.0310031.r002
Author response to Decision Letter 0
Submission Version1
22 Dec 2023

Reviewer 1

The study investigates whether Psychological Capital can be seen as part of the Bourdieusian capital framework including, social, cultural and economical capital. The researchers used existing European panel data to operational the 4 different capitals and investigated 1) whether a confirmatory factor analysis could show a better fit for a 4 factor structure in the data than for a 3 factor structure (whether PsyCap is related to other capitals but conceptually distinct from them and therefore should be included when 'defining' social class). The authors indeed found evidence for this 4 factor structure. Thereafter, they performed latent profile analyses using the 4 different capitals to see whether the data shows latent subgroups. Results showed 6 different latent subgroups, with a different composition on scores on the 4 different capitals included. The researchers also showed descriptive results for the different countries included.

The manuscript is well written and contributes to theory development in relation to social class. A large sample was used including several European countries.

[Authors]: We want to express our sincere appreciation for Reviewer 1’s time and effort in reviewing our paper. Reviewer 1’s valuable insights and feedback has improved the quality of the work, and we are grateful for the thoughtful comments.

1. However, what diminished my enthusiasm for the paper was the following: In the Introduction section, the authors do not explain why they included several European countries in their study, and also do not discuss that the way social class is defined also depends on context. Later on, also country specific results are discussed, and I think this could be also included in the aim of the study.

[Authors]: Thank you for your thoughtful feedback on our manuscript.

1) After careful consideration of this remark, we decided to better explain the “transnational” approach of our study and add a third aim to our objectives (a country comparison).

The revised manuscript has three aims: (1) examining the relations of PsyCap and the Bourdieusian capitals; (2) the construction of an integrated social class framework and (3) – as suggested by REVIEWER 1 – a country comparison of the integrated social class framework.

Only aim 1 and 2 use a transnational approach, which is necessary to construct a broadly applicable social class framework. Research comparing social classes across countries or social class research within a specific country, usually adopts an established social class framework that is applicable broadly, such as the Erikson-Goldthorpe-Portocarero (EGP) social class scheme (Haddon 2015), (neo-)Marxist social class schemes (Kong et al. 2017; Prins et al. 2015), Erik Olin Wright’s social class scheme (Espelt et al. 2008), occupational class models (Hoven et al. 2015), etc. In that sense, the types of social classes that can be found within the population are predetermined based on theoretical grounds (e.g., relations to the means of production, skill gradients, economic sectors, …) and assumed to be valid at least over different high-income countries. Similarly, in our study, we looked for a broadly applicable social class framework, but that is simultaneously able to tap in between country differences.

Moreover, we decided to use a transnational European sample for aim 1 and 2, because these objectives are mostly to support previous theoretical claims: i.e., assessing whether PsyCap can be integrated in the Bourdieusian capital framework and to scrutinize the social classes generated by this four-capital approach. For these objectives a specific country-context is of lesser importance. Including context variation would lead away from the main aim of these objectives, which is finding evidence for previous theoretical claims.

Yet, we agree with Reviewer 1 that we did not explain why we included several countries in our manuscript (for aim 1 and 2); therefore, we included the following sentence in the introduction of the revised manuscript: “Using a transnational approach helps to develop a broadly applicable social class framework. Research comparing social classes across countries or social class research within a specific country, usually adopts an established social class framework that is applicable broadly, such as the Erikson-Goldthorpe-Portocarero (EGP) social class scheme (Haddon 2015), (neo-)Marxist social class schemes (Kong et al. 2017), etc. In that sense, the types of social classes that can be found within the population are assumed to be valid over different high-income countries.”

2) As regards Reviewer 1’s remark on how the country-specific context would influence how social class is defined. We want to, firstly, highlight again that the methodology employed in our study is grounded in common methods utilized in social class research, even in cross-national research (Pförtner et al. 2015). Research on social class (inequalities) usually takes an existing social class framework, such as the Erikson-Goldthorpe-Portocarero (EGP) social class scheme (Haddon 2015), to investigate their research aims. These studies, like our study, also have the limitation that different countries exhibit different income inequalities and varying levels of educational democratization (Aamodt & Kyvik 2005), which again affect the sizes of the classes.

Moreover, our theoretical background, Bourdieu’s capital approach and the PsyCap approach used to scrutinize the social classes, are based on general theoretical concepts (Carmo & Nunes 2013; Donaldson et al. 2020; Wernsing 2013). Therefore, we can use these concepts to study social phenomenon in a transnational context.

Yet we now discuss how social class depends on context, we added extra information before introducing our third aim (see page 10, last paragraph): “Finally, the sizes of social classes within European countries are influenced by a country’s level of income inequality, educational democratization, generosity of the welfare regime, etc. [15,22]. For instance, access to good quality mental health services can enhance an individual’s ability to have high PsyCap [23] and generous replacement income schemes might protect the loss of economic (and subsequently, psychological) capital when unemployed, etc. Therefore, our final aim (objective 3) is to examine cross-national differences in social classes distribution.”

Finally, because we included country differences as an aim, we deleted the gender and age differences in our results. This gives us also the advantage that we were able to reduce the word count of the already lengthy manuscript. Moreover, the overall “story” of the manuscript is now more focussed and cohesive.

We accommodate to REVIEWER 1’s remarks by implementing the subsequent adaptations in the revised manuscript:

- In the introduction, we have provided a more comprehensive rationale for the utilization of all European countries in our analyses. See Page 4, last paragraph.

- In the introduction, we added a final aim which includes presenting country-specific distributions of the ‘integrated’ social class scheme. See page 5, first paragraph.

- In the objectives, we added a final aim referring to the country-specific distribution of the ‘integrated’ class scheme. See Page 10, last paragraph.

- In the discussion, we elaborate on country-specific distributions of the cluster. See page 29 - final paragraph, page 30 – first paragraph).

- In the discussion, we discuss how the country-specific context would influence how social class is defined. See Page 31, first paragraph.

2. The Methods and analyses used are, to my knowledge, sophisticated. Nevertheless, the section on how authors included the survey weight is unclear (was this weight determined by the authors or by researchers of the European social survey (if yes, I miss a reference), what exactly is controlled for in this weight?). That 2 times a different N is presented in the first paragraph of the analyses is also unclear (how come the first N is lower then the second one??).

[Authors]: Thank you for raising our attention to the use of weights in our research. We agree with Reviewer 1 that the section on weights could have been clearer. The survey weights used in the descriptive and advanced statistical analyses are provided by the European Social Survey. This was made more explicit in the methods section and a reference (Kaminska 2020) was added. See page 17, third paragraph.

Drawing on the guide to using weights with ESS data (Kaminska 2020), which states that “it is recommended that by default you should always use “anweight” (analysis weight) as weight in all analyses”, we used anweight in all our analyses. This weight is suitable for all types of analysis, including when you are studying just one country, when you compare across countries, or when you are studying groups of countries (Kaminska 2020). In the new version of the manuscript, we additionally weighted the CFA models and the Spearman rank correlations (which were unweighted in the original version).

Anweight corrects for differential selection probabilities within each country as specified by sample design, for non-response, for noncoverage, and takes into account differences in population size across countries (Kaminska 2020). Regarding the latter, this weight corrects for the fact that most countries taking part in the ESS have very similar sample sizes, no matter how large or small their population. Without weighting, any figures combining two or more country’s data would be incorrect, over-representing smaller countries at the expense of larger ones. The mean of the anweight, of my final sample, is 1.0050, the minimum is 0 and the maximum is 20.24.

We present two different N’s ((a) 35,313 and (b) 35,489) in the manuscript. The first number (35,313) is our total sample, non-weighted, with listwise deletion of missing data. The latter number (35,489) is the same sample, but weighted.

As correctly noticed by Reviewer 1, the weighted sample size is higher than the non-weighted sample size. When weighting data, the sample size can appear higher due to the adjustment applied to certain observations (e.g. some observations in Russia have a value around 20). This adjustment accounts for the varying importance or representation of specific groups or instances in the dataset. Essentially, weighting assigns more influence to certain observations, leading to a perceived increase in the effective sample size. It is crucial to understand that the weighted sample size does not represent the actual number of observations but rather the adjusted size considering the assigned weights.

To make the difference between the numbers clearer, in the table we also put weighted in the title with the number 35,489. In section 2.3. Analyses we wrote: “Our total sample comprised 35,313 respondents (non-weighted, with listwise deletion of missing data)”. And we added a reference to the “Guide on using weights in ESS data” in the methods section.

3. I was wondering why the authors did not transform their items into ridit-scores, so they can actually be compared over countries. I think, if I understood it correctly, the authors now base their categorization of scores in to low-mid-high on the different items measuring capital on pooled data from all the European countries together. It would have been more comparable over countries to first calculate ridit scores for each item per country before including them into one analyses. The authors only argue that objective income could be influenced by the country one lives in, so it might even be 'better' to use the subjective measure they used, but this is also true for other capitals and it would have been better to take this into account when performing analyses.

[Authors]: Thank you for this comment. Regarding your question about ridit-scores: The reviewer seems to suggest that we should remove country-level effects from the data before performing the analyses, because this is essentially what ridit-scores do.

This would be the way to go if we would be interested in within-country differences only, but we think that those between-country differences are important too (note that when using ridit scores there would be no Figure 1 with the suggested approach because the “average” of each country would be identical). In addition, we account for country-level effects in our analyses by (1) computing multilevel correlation coefficients, and (2) modelling the country-level effect in the LCA. So our take on this is that group-mean centering (by using ridit-scores) would make sense if the hypotheses concern within-country differences only. This is not the case here. Moreover, we do control for country-level differences in our analyses, explicitly taking country-level differences into account. For these reasons, we have not followed this suggestion.

4. A lot of the Discussion section is also description of results and is very long. I think this can be more concise and also for a great part presented under results.

[Authors]: Thank you for raising our attention to the descriptive nature of our discussion section. We agree with Reviewer 1. In the revised manuscript, we rewrote the Discussion section, making it more concise (for instance, deleting the descriptive information and the last two paragraphs of the discussion in the original manuscript) and ensuring that relevant content is appropriately presented under the Results section. We appreciate the opportunity to enhance the clarity and organization of the paper. The discussion section now ends with a short conclusion section for the sake of clarity.

5. I think authors use very firm description of and statements about the different latent clusters (also implying causality) in their result and discussion section, however the different items used to measure the different capitals are not optimal (as mentioned in the discussion as a limitation) and it is cross-sectional/correlational data, making statements about causality impossible. the authors should really be more careful in formulating their conclusion. This will also make the discussion more concise.

[Authors]: We appreciate REVIEWER 1’s observation regarding the firm descriptions and implied causality when describing the different latent clusters. Your point about the limitations of the measurement items and the cross-sectional nature of the data is well taken. We agree with REVIEWER 1 that the relations in our study are correlations. In addition, theoretically, it is also logical that all the relationships are bidirectional (for example, economic capital can be transformed into social capital and vice versa). Therefore, in the results sections, we have rewritten the description of the different latent clusters avoiding the use of causal language. We also exercised caution in our discussion: we thoroughly reviewed the manuscript and examined it for the use of causal language.

Reviewer 2

The paper addresses a significant research gap by proposing the integration of psychological capital (PsyCap) into Pierre Bourdieu's capital framework to better understand social inequality. The paper demonstrates a comprehensive understanding of the theoretical background and offers well-defined research objectives. However, there are two areas that need further attention to enhance the paper's quality before publication in PLOS One.

[Authors]: The authors wish to thank Reviewer 2 for the valuable feedback. We appreciate your recognition of the research gap we aimed to address by integrating PsyCap into Bourdieu's capital framework. We committed to addressing the areas that need improvement (see below).

1) Measurement Tools for Capital: The paper raises a valid concern about the limited measurement tools for different capitals (31). As the authors suggested, it is essential to acknowledge the limitations of the selected measurement tools and consider exploring alternative methods to ensure the robustness of the findings. The paper would benefit from a discussion of potential alternative measurement approaches to strengthen the validity of the research results. For instance, if objective income measures are not comparable across countries, the authors may consider income percentile calcula

10.1371/journal.pone.0310031.r003
Decision Letter 1
Ganotice Fraide Agustin Academic Editor
© 2024 Fraide Agustin Ganotice
2024
Fraide Agustin Ganotice
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
22 Mar 2024

PONE-D-23-17518R1Psychological capital and social class: A capital approach to understanding positive psychological states and their role in explaining social inequalitiesPLOS ONE

Dear Dr. De Moortel,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 06 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Fraide Agustin Ganotice, PhD

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear Authors,

Thank you for submitting your paper titled “Psychological capital and social class: A capital approach to understanding positive psychological states and their role in explaining social inequalities ” to PLOS ONE. Two reviewers have examined the manuscript for which one recommended major revision and one recommended minor revision. I also went over the manuscript and agreed with the two reviewers to recommend minor revision.

Thanks so much.

Respectfully yours,

Fred Ganotice

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I have read the revised version of the manuscript with great pleasure. The manuscript has certainly improved, the Results and Discussion section are more concise and have a better focus, and the authors have addressed most of my previous comments in a good way.

Nevertheless, there are a few minor comments still remaining:

The authors response to comment 3 is not completely satisfying. Ridit scores do not remove country-level effects, but make the measures more comparable over countries. For instance, what is considered high wealth or highly educated can differ between countries. When calculating ridit scores for each measure and per country separately, before placing individuals into a low, middle, or high group, makes measures more comparable over countries. In this case you are not comparing oranges with apples. Calculating ridit scores is therefore especially important when using more objective measures. On the other hand, I do not think the results will change much if the authors would have included ridit scores, since they mostly recoded their measures into quite crude groups (categorized indicators into 3 groups (low-mean-high)) and mostly also did not use objective measures but more subjective measures, where one could expect that these should be comparable over countries. However, it might be good to have a statistician have a look, since the sophisticated methods the authors used go beyond my own knowledge.

Regarding the third aim of the paper, where the authors included the suggested aim of examining country differences in the distribution of social classes, it would have been nice if the authors:

a. included hypotheses into the objectives section of the introduction, about the specific country-level indicators they are expecting results.

b. also included the actual country-level indicators they describe in the Introduction and also Discussion section (e.g., country's level of income inequality, educational democratization, gender inequality) into (if possible) the analyses, or in displaying the results (e.g. GINI coefficient, GDP). In this way, their claims in the Discussion section about why such country differences might exist, could be presented more firmly (backed-up with actual data).

Minor comments:

1. sometimes I miss references to claims made in the Introduction section:

- page 3, line 79: PsyCap can .....high-quality life.

- page 8, line 204: However, ....grow or decline.

2. The authors often use 'etc' after giving examples, which is ambiguous and should be removed (line 104, 177, 220, 251, 254).

3. There is still one claim in the Discussion section that I believe should be formulated more carefully since this cannot be concluded from the results of this cross-sectional study, namely on page 29, line 628-629: This finding also underscores....and vice versa.

Reviewer #2: Thank you for your thorough revisions in response to the reviewer's feedback. While I acknowledge the effort invested, several key issues persist:

1. The justification for introducing the concept and measurement of capital remains unclear. The selection of measurement tools appears arbitrary, for example in excluding occupation from capital measures. This raises concerns about the consistency and validity of the measurements and challenges the characterization of these constructs as "capital."

2. Similarly, the exclusion of ‘hope’ from the PsyCap construct due to data constraints raises questions about the integrity of the composite. Clarification is needed regarding whether it is appropriate to continue labeling it as PsyCap or if it should be considered a composite of other factors. Furthermore, the potential contributions of this concept and measurement to existing literature are not clearly articulated.

3. Additionally, the discussion surrounding the number of Latent Class Analysis (LCA) clusters lacks clarity -- which I have missed in my first-round review. While a 6-cluster model is presented, the marginal decrease in Bayesian Information Criterion (BIC) raises questions about the meaningfulness of these clusters (0.11%). The difference in BIC between 1-cluster and 2-clusters is also negligible (3%), suggesting a lack of significance. It is imperative that the authors provide a robust defense for the meaningfulness of these clusters, tying back to the broader question of the necessity of PsyCap.

**********

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10.1371/journal.pone.0310031.r004
Author response to Decision Letter 1
Submission Version2
30 Apr 2024

Reviewer #1:

[Authors]: Dear Reviewer #1, we want to explicitly thank you for taking the time to review our revised manuscript with great care. We appreciate your insights and are confident that the manuscript only improved by adjusting the manuscript in accordance with your feedback.

1) The authors response to comment 3 is not completely satisfying. Ridit scores do not remove country-level effects, but make the measures more comparable over countries. For instance, what is considered high wealth or highly educated can differ between countries. When calculating ridit scores for each measure and per country separately, before placing individuals into a low, middle, or high group, makes measures more comparable over countries. In this case you are not comparing oranges with apples. Calculating ridit scores is therefore especially important when using more objective measures. On the other hand, I do not think the results will change much if the authors would have included ridit scores, since they mostly recoded their measures into quite crude groups (categorized indicators into 3 groups (low-mean-high)) and mostly also did not use objective measures but more subjective measures, where one could expect that these should be comparable over countries.

However, it might be good to have a statistician have a look, since the sophisticated methods the authors used go beyond my own knowledge.

[Authors]: Thank you for pointing our attention to this important discussion. The discussion about the usage of ridit-scores touches upon a fundamental debate regarding comparing social classes across different countries. This debate revolves around whether we should focus on absolute differences or relative differences. We agree with Reviewer 1 that relative differences matter. To some extent, when comparing personal or household income across countries, relative income comparisons allow for a more accurate assessment of the standard of living within and between countries. Comparing income across countries in a relative way takes into account differences in, for example, purchasing power. A certain income level might afford a higher standard of living in a country with a lower cost of living compared to a country with a higher cost of living. By considering relative income, we could get a more accurate picture of what individuals can afford with their income in different countries. So, ridit scores could provide insights into how income levels in one country compare to those in others, taking into account differences in purchasing power and cost of living between countries.

Yet, for household income (or economic capital in our manuscript), we already using a subjective measure of income: perception of the current household income with answer categories: (1) Living comfortably on present income; (2) Coping on present income; (3) Difficult on present income and (4) Very difficult on present income. The consequence is that we already take country-specific issues into account that could impact the “purchasing power” of an income.

The same goes for psychological capital. People evaluate themselves and their circumstances relative to others. Psychological measures that capture constructs like self-esteem, and resilience are influenced by comparisons with others (see, for instance, social comparison theory). Yet, here too, the different sub-questions that make up the PsyCap scale are subjective questions. For instance, “In general I feel very positive about myself”, with answer categories ranging from 1 (agree strongly) to 5 (disagree strongly). By using these subjective questions, we automatically take country-specific issues into account that could impact how high or low a person might evaluate his or her PsyCap.

The same reasonings can be made for the variable “strong ties” and “weak ties” of social capital, as this is also measured using a subjective measure.

However, regarding cultural capital, we are using respondents’ own and their parent’s educational attainment based on the ISCED scale. This is measured in an “absolute” manner. Yet, the International Standard Classification of Education (ISCED) exists precisely because we recognize and categorize educational levels in an absolute sense, irrespective of relative differences between countries. Therefore, we are confident in the presentation of our results.

2) Regarding the third aim of the paper, where the authors included the suggested aim of examining country differences in the distribution of social classes, it would have been nice if the authors:

a. included hypotheses into the objectives section of the introduction, about the specific country-level indicators they are expecting results.

[Authors]: Thank you for this suggestion, we agree with Reviewer 1 that including hypothesis will improve the manuscript. We included two hypotheses based on the research of Andersen & Curtis (2012) and Breen (2010):

Our last aim (objective 3) is to examine cross-national differences in social classes distribution. The sizes of social classes within European countries are influenced by macro-level factors, such as a country’s level of economic development, income inequality and educational expansion (Andersen & Curtis 2012; Breen 2010). Countries with less economic development and high-income countries with large income inequality demonstrate more (self-identified) low social classes (Andersen & Curtis 2012). Moreover, educational expansion can lead to a large group of the upper class (with high volume of cultural and economic capital) (Breen 2010). Because there are transformation processes between all types of capital and because countries’ macro-characteristics influence capital volume and composition, it is hypothesized that there will be a greater proportion of social classes with low general capital volume and deprived capital composition in countries with low economic development (such as Ukraine, Kosovo and Albania), in countries with high income inequality (such as Israel, Southern European and Anglo-Saxon countries) and in countries with low educational expansion (such as Southern European countries). A greater proportion of social classes with high general capital volume and rich capital composition will be found in their counterparts (such as Western and Northern European countries).

b. also included the actual country-level indicators they describe in the Introduction and also Discussion section (e.g., country's level of income inequality, educational democratization, gender inequality) into (if possible) the analyses, or in displaying the results (e.g. GINI coefficient, GDP). In this way, their claims in the Discussion section about why such country differences might exist, could be presented more firmly (backed-up with actual data).

Thank you for this suggestion, we have included GDP per capita (in US$), the GINI index and Expenditure on education (% of the GDP), in Europe in 2012 and added these numbers to our hypothesis (see above). These numbers are also added to the results. We have deleted the figure and inserted a Table in which it was easier to include the macro-indicators. The results and discussion have been rewritten to also include the macro-indicators.

GDP per capita (US$) GINI Educational Expansion1 Low-educated deprived Middle- educated deprived High Psy/soc/eco capital Upward well-off High Psy/soc/cult capital Generationally well-off

Kosovo 3,410 29 n.a. 47 8 15 4 25 2

Ukraine 4,004 24.7 6.44 10 37 8 0 35 10

Albania 4,247 29 3.31 70 1 6 15 3 4

Bulgaria 7,430 36 3.48 35 20 0 2 31 12

Hungary 12,984 30.8 4.14 24 50 0 0 6 20

Poland 13,010 33 4.86 35 1 30 24 1 8

Lithuania 14,367 35.1 4.76 33 23 0 4 31 9

Russian Federation 15,420 40.7 3.79 23 38 12 0 23 3

Estonia 17,403 32.9 4.72 17 22 2 5 39 16

Slovakia 17,498 26.1 3.86 15 57 0 0 9 18

Czechia 19,870 26.1 4.22 1 62 0 0 24 13

Portugal 20,563 36 4.95 54 0 7 33 0 7

Slovenia 22,641 25.6 5.62 17 36 4 0 25 18

Spain 28,322 35.4 4.47 36 1 22 26 0 15

Cyprus 28,910 34.3 5.92 42 11 18 22 0 7

Israel 33,156 41.3 5.59 29 14 26 11 9 11

Italy 35,051 35.2 4.06 44 10 27 7 5 8

France 40,870 33.1 5.46 23 4 51 12 2 9

United Kingdom 42,497 33.1 5.63 22 6 38 20 1 13

Germany 43,855 31.1 4.93 16 0 58 18 2 7

Belgium 44,670 27.5 6.26 25 13 26 17 1 19

Iceland 45,995 26.8 7.58 0 17 39 24 6 13

Finland 47,708 27.1 7.15 0 5 39 6 41 9

Ireland 48,943 33.2 6.162 32 7 37 15 0 8

Netherlands 50,070 27.6 5.41 36 7 22 20 0 15

Sweden 58,037 27.6 7.57 11 15 39 15 7 12

Denmark 58,507 27.8 7.24 6 2 48 17 10 15

Switzerland 85,836 31.6 4.9 10 0 59 20 0 10

Norway 102,175 25.7 7.33 0 10 33 33 6 18

1Government expenditure on education, total (% of GDP); 2 Data from 2009 (data 2012 unavailable); n.a.: not available

Minor comments:

1. sometimes I miss references to claims made in the Introduction section:

- page 3, line 79: PsyCap can .....high-quality life.

Four References have been added to his sentence. The references added are:

PsyCap leads to:

- Good connections (Guo et al. 2020)

- Good jobs (Cenciotti et al. 2017)

- Wealth (Judge & Hurst 2008)

- High quality of life (Santisi et al. 2020)

- page 8, line 204: However, ....grow or decline.

This has been reported in the book “Psychological capital and Beyond” by Luthans et al. (2015). This reference has been added to the manuscript.

2. The authors often use 'etc' after giving examples, which is ambiguous and should be removed (line 104, 177, 220, 251, 254).

In the reported lines with “etc’” in the sentence, we have rewritten the sentences and deleted the “etc”.

3. There is still one claim in the Discussion section that I believe should be formulated more carefully since this cannot be concluded from the results of this cross-sectional study, namely on page 29, line 628-629: This finding also underscores....and vice versa.

We appreciate the alertness of Reviewer 1. The problem with using cross-sectional data is that you cannot make claims about the direction of the relation. With our study, we cannot say that PsyCap leads to social capital or social capital to PsyCap, you can only say that the two are related, in the moment in time under study. Therefore, the sentence has been changed to: This finding also underscores the importance of the association between social capital and PsyCap.

Reviewer 2:

Reviewer #2: Thank you for your thorough revisions in response to the reviewer's feedback. While I acknowledge the effort invested, several key issues persist:

1) The justification for introducing the concept and measurement of capital remains unclear.

Thank you for this comment. We hope we understood it correctly and understand that Reviewer 2 is asking for a justification of PsyCap to be included in the Bourdieusian capital framework. Therefore, we have rewritten the second paragraph of our manuscript. This paragraph is now dedicated to the added value of PsyCap into social inequality research (in a concise manner). The added value of PsyCap, is adding a more “personal” element, free from socialization into the drivers of social inequality.

The selection of measurement tools appears arbitrary, for example in excluding occupation from capital measures. This raises concerns about the consistency and validity of the measurements and challenges the characterization of these constructs as "capital."

Thank you for this comment. We acknowledge the importance of selecting the correct measurement tools for the different types of capital. The capital types that are included are selected with great care (please see Appendix A, for our answer to comment 1 from Reviewer 2 in the first round of revisions, who made a similar remark).

Firstly, we have deleted the sentence “we only selected “pure” capitals” from our manuscript as it might reflect a sense of superiority. This is not the purpose of the selected measurement tools. We simple want to test our first hypothesis (the transferability of capitals), which requires items that distinctly reflect only one type of capital. We want to stress that for other research purposes occupational status can be a good indicator to construct social classes.

The exclusion of “occupation” has a specific reason. Occupation reflects both cultural and economic capital (that is also the reason why many class schemes rely only on the characteristics of people’s occupation). Someone’s occupation is frequently a direct result of someone’s education and training, while occupation is also frequently used to reflect someone’s level of income (i.e., economic capital). Although our data include measures for individuals’ occupational status and parent’s occupational status, we decided to not use these items. Parents’ and the respondents’ own occupational status is not a “distinct” measure of for instance economic capital as it also entails cultural capital.

By using occupational position, we would be unable to test our first aim (the transferability between different capitals). That is the reason why we used “distinct” economic capital variables, such as perception on income and “distinct” cultural capital variables: own’s and parent’s level of education.

This sentence was added to the methods section:

“To adequately test the transferability between different capitals, we selected measures that distinctly represent only one form of capital. Therefore, items that could account for two capital dimensions are excluded. For instance, occupational status reflects both cultural capital (as educational attainment often is a prerequisite for access to certain professions) and economic capital (as a job is a primary mean of income and financial security) and is subsequently omitted from our analyses.”

2) Similarly, the exclusion of ‘hope’ from the PsyCap construct due to data constraints raises questions about the integrity of the composite. Clarification is needed regarding whether it is appropriate to continue labeling it as PsyCap or if it should be considered a composite of other factors. Furthermore, the potential contributions of this concept and measurement to existing literature are not clearly articulated.

Thank you for pointing our attention to the limitations in the PsyCap construct. We partly agree with Reviewer 2 that we need to be more careful in labelling our construct as PsyCap. Therefore, we have now more clearly indicated in the manuscript that we measure “a proxy” of PsyCap.

In large cross-national surveys such as the ESS one often needs to work with proxy variables. For PsyCap this means that we have adequate items for self-efficacy, resilience and optimism, but not for hope. What we did in our revised paper, however, is to explicitly mention that our measure of PsyCap is a proxy measure.

The consequence of using a proxy is one of construct validity. We, however, feel that this issue is not too severe because (a) PsyCap is a higher-order construct that captures shared variance between four lower-order constructs. Because three of those constructs are well represented in our proxy measure and because our analyses show that they clearly load on one factor and are internally consistent, we believe that our proxy captures this common core rather well.

Moreover, (b) Hope consists of three things: agency, pathways, and goal (Snyder & Lopez 2002). Whereas pathways and goals are not well represented in the items, agency is to some extent reflected in the resilience item "How difficult or easy do you find it to deal with important problems that come up in your life". In addition, according to Huppert et al. (2013), optimism also refers to hopefulness, by reflecting its emotional facet. So, hope is somewhat captured in the way optimism and resilience are measured.

We added this issue to our limitations. We added several sentences in the limitations section:

“Additionally, the indicator for PsyCap is only a proxy for the underlying theoretical concept and the measurement of the dimension hope was suboptimal. Yet, because (1) PsyCap is a second-order construct, (2) the subscales clearly load on one factor and are internally consistent and (3) hope is somewhat captured in the way optimism and resilience are measured, the inadequate measurement of one indicator should not be problematic.

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0310031.r005
Decision Letter 2
Ganotice Fraide Agustin Academic Editor
© 2024 Fraide Agustin Ganotice
2024
Fraide Agustin Ganotice
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version2
23 Jun 2024

PONE-D-23-17518R2Psychological capital and social class: A capital approach to understanding positive psychological states and their role in explaining social inequalitiesPLOS ONE

Dear Dr. De Moortel,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Academic Editor

PLOS ONE

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Additional Editor Comments:

Dear Authors,

Thank you for submitting your paper titled “Psychological capital and social class: A capital approach to understanding positive psychological states and their role in explaining social inequalities ” to PLOS ONE. Two reviewers have examined the manuscript for which one recommended minor revision and one recommended acceptance. I also went over the manuscript and agreed with the two reviewers to recommend minor revision. This resubmitted version has been greatly improved and only minor changes are needed. Looking forward to seeing the final version!

Thanks so much.

Respectfully yours,

Fred Ganotice

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: (No Response)

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I have read the revised manuscript with great pleasure and I the authors have successfully addressed all my comments. It has become a very nice paper that will make a real contribution to the field.

Reviewer #2: I appreciate the extensive revisions you have made and the effort to address the concerns regarding your measurement of PsyCap. Your detailed response highlights your commitment to improving the clarity and robustness of your study.

One thing I would like to highlight is the PsyCap measurement. I don't think the PsyCap measurement should be justified as stated in the discussion: "because (1) PsyCap is a second-order construct, (2) the subscales clearly load on one factor and are internally consistent, and (3) hope is somewhat captured in the way optimism and resilience are measured, the inadequate measurement of one indicator should not be problematic." (p.32).

This justification overlooks the importance of each component in maintaining the integrity of the PsyCap construct (see the discussion of similarities and differences among four dimensions of PsyCap in Lutherans & Youssef-Morgan 2017). Each of the elements contributes uniquely to the overall construct, and their interrelationships do not justify the exclusion of any single component. The integrity and theoretical foundation of PsyCap rest on the presence of all four factors, and omitting one undermines the validity of the construct.

The impact of not fully measuring PsyCap could potentially skew the study's findings and their implications. Although I understand the limitation of secondary data, this needs to be explicitly stated in the manuscript. If possible, I suggest adding a discussion of the expected outcomes that might result from the inclusion of the hope dimension. For instance, in relation to one of the main findings—that countries where governments allocate large funds to education systems (like Norway, Finland, and Denmark) have classes with lower prevalence of low cultural capital—each social and cultural context can provide different levels of hope for individuals. This variation in hope levels could potentially alter the latent classes and demonstrate different cross-cultural dynamics. By including hope, you could offer a more comprehensive analysis of how different components of PsyCap interact with educational and cultural policies across diverse contexts — for example.

**********

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Reviewer #1: Yes: Karen Schelleman-Offermans

Reviewer #2: No

**********

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10.1371/journal.pone.0310031.r006
Author response to Decision Letter 2
Submission Version3
24 Jul 2024

Reviewer #2:

[Reviewer #2]: I appreciate the extensive revisions you have made and the effort to address the concerns regarding your measurement of PsyCap. Your detailed response highlights your commitment to improving the clarity and robustness of your study.

[Authors:] We are grateful that Reviewer 2 sees the efforts we made to improve our study. Below, we address the final comment.

[Reviewer #2]: One thing I would like to highlight is the PsyCap measurement. I don't think the PsyCap measurement should be justified as stated in the discussion: "because (1) PsyCap is a second-order construct, (2) the subscales clearly load on one factor and are internally consistent, and (3) hope is somewhat captured in the way optimism and resilience are measured, the inadequate measurement of one indicator should not be problematic." (p.32).

This justification overlooks the importance of each component in maintaining the integrity of the PsyCap construct (see the discussion of similarities and differences among four dimensions of PsyCap in Lutherans & Youssef-Morgan 2017). Each of the elements contributes uniquely to the overall construct, and their interrelationships do not justify the exclusion of any single component. The integrity and theoretical foundation of PsyCap rest on the presence of all four factors, and omitting one undermines the validity of the construct.

The impact of not fully measuring PsyCap could potentially skew the study's findings and their implications. Although I understand the limitation of secondary data, this needs to be explicitly stated in the manuscript. If possible, I suggest adding a discussion of the expected outcomes that might result from the inclusion of the hope dimension.

For instance, in relation to one of the main findings—that countries where governments allocate large funds to education systems (like Norway, Finland, and Denmark) have classes with lower prevalence of low cultural capital—each social and cultural context can provide different levels of hope for individuals.

This variation in hope levels could potentially alter the latent classes and demonstrate different cross-cultural dynamics. By including hope, you could offer a more comprehensive analysis of how different components of PsyCap interact with educational and cultural policies across diverse contexts — for example.

[Authors:] Thank you for pointing our attention to the paper of Luthans & Youssef-Morgan [1] in the Annual Review of Organisational Psychology and Organisational Behavior. After careful reading of this paper, we altered our discussion. We now make an in-depth discussion of how our results can be skewed due to the lack of a direct measure of “hope” in our PsyCap construct. Moreover, we have deleted our original justification that Reviewer 2 mentions in their review, both in the discussion and in the methods section.

We added the following sentences to our discussion:

The lack of “hope” in our PsyCap construct can potentially skew our results as PsyCap is a second-order construct based on shared commonalities of first-order psychological resources (hope, resilience, efficacy, and optimism), but also based on their unique characteristics [1]. The discriminant validity of these constructs has been empirically established [2]. Unfortunately, the ESS data do not include a proxy for the ‘hope’ element of PsyCap. Given that we used PysCap to define clusters, our analysis may have missed a social class cluster distinctly characterized by waypower, which is unique to hope. This “overlooked” social class might also have a specific cross-country distribution as the level of hope varies between countries and may be related to policies like government spending on education and social services [3]. Whether such a cluster exists and how it is distributed over countries is an empirical question that we cannot answer with the available data. There are, however, certain indications that suggest that the component “hope” does not distinctly dominate profiles within populations. Indeed, studies based on person-centered latent profile analyses of the PsyCap components show a gradient in the profiles (ranging from low, to moderate to rich (general) PsyCap) [4–6] or in other cases the other PsyCap components (optimism or resilience) seem to dominate the profiles [7,8]. Our research shows that there is much to be gained by including PsyCap in comparative research and recommends that a more comprehensive measure for PsyCap is included in large-scale survey projects like the European Social Survey.

References

1. Luthans F, Youssef-Morgan CM. Psychological Capital: An Evidence-Based Positive Approach. Annu Rev Organ Psychol Organ Behav. 2017;4: 339–366. doi:10.1146/annurev-orgpsych-032516-113324

2. Luthans F, Youssef CM. Emerging Positive Organizational Behavior. J Manage. 2007;33: 321–349. doi:10.1177/0149206307300814

3. Krafft AM, Guse T, Slezackova A, editors. Hope across cultures: Lessons from the International Hope Barometer. Cham: Springer International Publishing; 2023. doi:10.1007/978-3-031-24412-4

4. Song L, Zhou Y, Zheng J, Wang Y, Li H, Feng X. Psychological capital of Chinese employees: Investigating its measurement and latent profiles. J Pacific Rim Psychol. 2024;18. doi:10.1177/18344909241254497

5. Gao Y, Yue Y, Li X. The relationship between psychological capital and work engagement of kindergarten teachers: A latent profile analysis. Front Psychol. 2023;14. doi:10.3389/fpsyg.2023.1084836

6. Teng M, Wang J, Jin M, Yuan Z, He H, Wang S, et al. Psychological capital among clinical nurses: A latent profile analysis. Int Nurs Rev. 2023. doi:10.1111/inr.12918

7. Bouckenooghe D, De Clercq D, Raja U. A person-centered, latent profile analysis of psychological capital. Aust J Manag. 2019;44: 91–108. doi:10.1177/0312896218775153

8. Geremias RL, Lopes MP, Soares AE. Psychological Capital Profiles and Their Relationship With Internal Learning in Teams of Undergraduate Students. Front Psychol. 2022;13. doi:10.3389/fpsyg.2022.776839

9. Platania S, Paolillo A. Validation and measurement invariance of the Compound PsyCap Scale (CPC-12): a short universal measure of psychological capital. An Psicol. 2022;38: 63–75. doi:10.6018/analesps.449651

10. Luthans F, Youssef-Morgan CM, Avolio BJ. Psychological Capital and Beyond. Oxford University Press; 2015.

Attachment Submitted filename: Response to reviewers.docx

10.1371/journal.pone.0310031.r007
Decision Letter 3
Ganotice Fraide Agustin Academic Editor
© 2024 Fraide Agustin Ganotice
2024
Fraide Agustin Ganotice
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version3
23 Aug 2024

Psychological capital and social class: A capital approach to understanding positive psychological states and their role in explaining social inequalities

PONE-D-23-17518R3

Dear Dr. De Moortel,

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10.1371/journal.pone.0310031.r008
Acceptance letter
Ganotice Fraide Agustin Academic Editor
© 2024 Fraide Agustin Ganotice
2024
Fraide Agustin Ganotice
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
29 Aug 2024

PONE-D-23-17518R3

PLOS ONE

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