Predictors of civic competence in Spanish students in Compulsory Secondary Education

10.4438/1988-592X-RE-2025-408-680

Pablo Javier Ortega-Rodríguez

Universidad Autónoma de Madrid

https://orcid.org/0000-0002-1128-2360

Introduction

The International Civic and Citizenship Education Study (ICCS) aims to understand how adolescents are prepared for their role as active and participatory citizens in society (Ministry of Education, Vocational Training and Sports, 2024a). Civic competence is defined as ‘the body of knowledge about political and social issues, critical thinking skills, and values, which are reflected in participation in the community’ (Guérin et al., 2013, p. 437).

The significance of ICCS 2022 lies in its uniqueness as the only international study to analyse how students engage with contemporary societal issues, such as social justice (Ministry of Education and Vocational Training, 2024).

Isac et al. (2019) and Pereira et al. (2015) have identified several key predictors of students’ civic competence, including gender, socioeconomic context, immigrant status, participation in community groups or organisations, and engagement with digital media.

Regarding gender—the first predictor—research has revealed significant differences in civic competence between boys and girls. Gómez and Suárez (2023) examined the relationship between educational practices and students’ civic knowledge in the ICCS 2016 across five Latin American and Caribbean countries (Chile, Colombia, Peru, the Dominican Republic, and Mexico). They found that girls consistently outperform boys in the cognitive component of the test, suggesting that girls possess a level of civic knowledge higher than that of boys. Similarly, Solhaug and Kristensen’s (2020) research on secondary school students in Denmark and Norway showed that girls scored higher in civic competence, suggesting greater engagement with social issues and increased participation in school activities that promote equity and social justice (Juanes & Jacott, 2020; Ten Dam et al., 2020). However, some studies (Cicognani et al., 2012; Marta et al., 2006) found no significant gender differences in civic competence, implying equal participation in school activities that promote social justice (Manganelli et al., 2014).

With regard to the second predictor—socioeconomic context—research has demonstrated its significant impact on academic performance (Berkowitz et al., 2017; Coleman et al., 1966). Deimel et al.’s (2020) study on the impact of socioeconomic context on civic education in Belgium, Denmark, Germany, and the Netherlands revealed that students from advantaged backgrounds received more civic education than those from disadvantaged contexts. This translated into a deeper understanding of democratic institutions and processes (Ye, 2018), as well as a greater intention to vote in adulthood (Castillo et al., 2015). Miranda (2023) assessed the relationship between socioeconomic context and civic participation among young people in the 2016 edition of the ICCS. The findings indicated that students from advantaged backgrounds are more willing to participate in society than those from disadvantaged backgrounds. This includes voting, attending demonstrations, and solving community problems (Carrasco et al., 2020). This research highlights the importance of factors determining students’ socioeconomic context, such as the number of books at home.

Regarding the third predictor—immigrant status—research has revealed a performance gap between native and immigrant students (Guerra et al., 2019; Teltemann & Schunck, 2017). Zhu and Chiu’s (2020) analysis of Danish students who participated in the ICCS 2016 showed that native families demonstrated higher civic knowledge than immigrant families, attributed to a stronger attachment to their country of residence (Azzolini, 2016; Choi & Cha, 2021). Conversely, Zhu et al. (2019) found that in Hong Kong, immigrant students exhibited greater civic knowledge than native ones, owing to higher parental expectations for academic progress. This emphasises the importance of family environment and school resources, such as textbooks (Goodman, 2021), in developing immigrant students’ civic literacy.

The fourth predictor—student participation in community groups or organisations—has been shown to impact civic competence performance (Maurissen, 2020; Myoung & Liou, 2022). Blaskó et al. (2019) analysed data from the ICCS 2016 and found that students with higher civic knowledge are more likely to participate in community groups and hold positive attitudes towards ethnic minorities and immigrants. In the German context, Deimel and Abs (2022) demonstrated a correlation between civic education and youth societal participation (Feitosa, 2020), emphasising the importance of student engagement in achieving transferable learning and competencies (Granizo et al., 2019).

The fifth predictor—student participation using digital media—indicates that Internet use favours the acquisition of civic competence. This process includes sharing information on social issues posted by others (Gleason & Von Gillern, 2018), searching for information on social issues, conducting responsible online research (Cabero-Almenara et al., 2019; Lauricella et al., 2020), and publishing content on specific topics such as climate change (Pangrazio & Sefton-Green, 2021), gender equality (Estanyol et al., 2023), and political news (Kim & Ellison, 2021; Ohme, 2019).

School ownership—the sixth predictor—has been shown to influence student performance significantly (Park & Holloway, 2017). The type of school and the approach to teaching social issues impact civic competence (Keating, 2016). Collado et al. (2015) analysed Chilean students’ performance in the ICCS 2009 and found that students from private schools outperformed those from public schools (Mizala & Torche, 2012). Nevertheless, Dijkstra et al. (2023) examined Dutch students’ civic competence in the ICCS 2016 and found no significant differences in performance between state and independent schools (Gil-Flores & García-Gómez, 2017).

International studies have synthesised these predictors’ influences. Kuang and Kennedy (2018) analysed Asian countries in the ICCS 2009 and found that the profile of a citizen with greater civic knowledge corresponds to a girl from a favourable socioeconomic background with high participation in community groups or organisations. Similarly, Trunk et al. (2022) focused on European countries in the ICCS 2016 and identified students’ socioeconomic level and immigrant status as significant predictors of civic education outcomes.

This theoretical foundation suggests the need to advance our understanding of the variables influencing civic competence in students at the Compulsory Secondary Education (Educación Secundaria Obligatoria; ESO) level. This knowledge is crucial as it affects how students comprehend and prepare for citizenship in a world where civic participation is continuously evolving (Ministry of Education and Vocational Training, 2024). Therefore, this research investigates the influence of student- and school-associated predictors on the civic competence performance of ESO students.

Method

This research employed a non-experimental design, as the variables could not be manipulated, and the participants could not be randomly assigned. It was an ex post facto study, analysing the phenomenon after it had occurred, without the ability to manipulate the independent variable (Kerlinger & Lee, 2002).

Sample

In the 2022 edition of the ICCS, each country selected a nationally representative sample to represent the study’s target population (14-year-old students) and make 95% confidence estimates. The participating sample comprised 3,519 Spanish students in the second year of ESO from 157 schools (Ministry of Education, Vocational Training and Sports, 2024b). The treatment of missing values included eliminating cases with one or more missing values. This study considered the Socioeconomic Index variable (Murillo et al., 2023), which comprises the mean of three variables (the highest educational level of the parents, their occupation, and the number of books at home). Substituting a lost value for an estimate affects the exact value of the Socioeconomic Index (Enders, 2010). The final sample comprised 3,487 Spanish students in the second year of ESO (50.2% boys; 49.8% girls).

Instruments

This study utilised the two ICCS 2022 assessment instruments: the student questionnaire and the school questionnaire. The first comprises 121 Likert-type, multiple-choice, and open-ended items on various aspects related to civic competence, such as student participation using digital media and in community groups or organisations. The second, completed by the headteachers, comprises 26 questions on the characteristics of the schools, as well as contextual variables related to civic participation at school (Ministry of Education, Vocational Training and Sports, 2024b).

Procedure

The ICCS establishes four levels of performance (A = 563 points or more, B = 479 to 562 points, C = 395 to 478 points, D = 311 to 394 points) (Ministry of Education, Vocational Training and Sports, 2024b).

The average performance in civic competence in Spain (510) is 73 points behind Taiwan, the highest-scoring country (583). Spanish students fall within Level B—they are familiar with the broad concept of representative democracy and understand the influence of citizenship on local communities, society, and the world.

The percentage of Spanish students at the higher achievement level (29%) is lower than expected due to the percentage of students at lower levels (60% with a medium level and 11% with a low level) (Ministry of Education, Vocational Training and Sports, 2024b).

The ICCS employs item response theory to assign scores to students, expressing achievement levels on a scale with a central benchmark of 500 points. For each student, the a posteriori distribution of the measured skill was obtained, from which five plausible values were extracted (Ministry of Education, Vocational Training and Sports, 2024b).

To calculate the dependent variable (civic competence performance), independent estimates were generated for each of the five plausible values available in the database, and average risk values were calculated.

From the ICCS 2022 database, the independent variables with the greatest impact on the dependent variable were selected by means of decision tree analysis. This approach reduces the number of independent variables and identifies those affecting civic competence. To this end, two methods were employed. The first was classification and regression trees (CART) (Strobl et al., 2009), a recursive partitioning procedure based on the ‘divide and conquer’ rule, which includes binary segmentations and a measure of independent variables’ relevance (Asensio et al., 2018; Hernández Orallo et al., 2004). The second was a chi-square automatic interaction detector (CHAID) used complementarily to identify the predictor with the strongest interaction with the dependent variable at each step.

The procedure comprised five phases. In the first phase, initial models were estimated using CART, with the five plausible values treated as dependent variables, while items from the student and school questionnaires served as independent variables (2 questionnaires x 5 plausible values = 10 models). Average risk values were calculated for each questionnaire, yielding 3,120.43 for the student body and 721.19 for schools. In the second phase, initial models were estimated using CHAID (another 10 models) to identify variables from each questionnaire interacting with the dependent variable. In the third phase, variables with a normalised importance of at least 10% were selected and identified in the CHAID models. In the fourth phase, predictive values for each questionnaire were calculated (0.65 for the student questionnaire and 0.52 for the school questionnaire). In the fifth phase, variables meeting the described criteria and endorsed by research referenced in the theoretical framework were selected.

This study used a hierarchical model to analyse the effect of a set of predictors on the dependent variable, respecting the nested structure of the data: students and schools (Tourón et al., 2023).

TABLE I. Variables included in the model

Source: compiled by the author

Table II shows the measures of centralisation and dispersion of the variables analysed.

TABLE II. Measures of centralisation and dispersion of variables

Source: compiled by the author

According to the data in Table II, the typical student who participated in the ICCS 2022 edition was male and native-born and hailed from a middle socioeconomic background. This student had been involved in community groups or organisations for over a year, used digital media at least once a week to engage with political and social issues, and attended a state school.

Two programs were used to perform the analyses: SPSS 29 to calculate the measures of central tendency and dispersion of the variables as well as the decision trees and MLwiN 2.36, which allowed the calculation of the estimates using the iterative generalized least squares procedure (Goldstein, 2003).

Results

The results of the Student’s t-test showed significant differences between the average scores for boys (504 points) and girls (519.66 points), with a critical level of 0.019.

The modelling process begins with the formulation of the null model, which does not include predictor variables but is essential because it establishes the basis for comparing it with the final model and reports on the initial variance at the two levels (Tourón et al., 2023). The null model is represented as follows (Martínez-Garrido & Murillo, 2014):

Yij=β0j+eij

β0j=β0+μ0j

μ0j∼N(0,σu02)

e0j∼N(0,σe2)

Table III shows the results of the null model. The fixed parameter reports the value of the intercept or the average performance observed in civic competence for the students who constituted the sample (507.62 points).

TABLE III. Null model estimation

Source: compiled by the author

The random part of the null model revealed the variances of the residuals at the two established levels. A parameter is considered significant (alpha = 0.05) if the ratio of the parameter estimate to its standard error is greater than 1.96 (Gaviria & Castro, 2004). In this analysis, the parameters were statistically significant at both levels, as demonstrated by the ratios between students (5,605.13/137.42 > 1.96) and between schools (1,799.81/236.25 > 1.96). These significant parameters indicated the existence of unexplained variance at both levels, which justified extending the model to account for as much of its variance as possible (Rodríguez-Mantilla et al., 2018).

The likelihood ratio for the null model is 40,318.25 with three parameters. The Akaike Information Criterion (AIC) for this model is 40,323.25, while the Bayesian Information Criterion (BIC) is 40,339.646. These three values will be compared with those of subsequent models to assess improvements in goodness of fit.

The intraclass correlation coefficient (ICC) represents the degree of variability between schools compared to that between students in the same school (Pardo et al., 2007).

ICC=1.799,81/(5.605,13+1.799,81)=0,2430

This value indicates that 24% of the variance is inter-school variance—the percentage of the variance not explained by the predictors can be attributed to the clustering variable at level 2. Table IV presents the results of estimating the model with the first-level explanatory variables.

TABLE IV. Model with explanatory variables at level 1

Source: compiled by the author

According to the data in Table IV, the value of the constant is 481.39 points, slightly lower than that in the null model. This is because the reference group in this model differed from that of the null model; it represents the expected performance of children from an average socioeconomic background in the sample. The explanatory variables included in the random part were significant for civic competence, as the standard error was greater than 1.96.

In terms of gender, girls outperformed boys in civic competence, with the estimated average increasing by 10.23 among females. For every unit increase in socioeconomic status, performance improved by 19.47 points.

Immigrant status also impacted performance, with immigrant students scoring lower than native ones. Specifically, being an immigrant resulted in a decrease of 16.09 points in performance.

Participation in community groups or organisations influenced performance. Students who engaged with a group, organisation, or team scored higher than those who did not. For each additional level of participation, performance increased by 5.13 points.

Similarly, participation in digital media activities affected performance. Students who regularly used digital media to engage with social issues performed better than those who rarely did or never used it. Each increase in frequency corresponded to a 3.58-point improvement in performance.

The values of the likelihood ratio, AIC, and BIC were lower compared to the null model, indicating an improvement in the model’s goodness of fit with the inclusion of level-1 explanatory variables. Table V presents the fixed and random components of the final model, including parameter values with standard errors shown in parentheses.

TABLE V. Final model with explanatory variables at levels 1 and 2

Source: compiled by the author

According to the parameters of the fixed part of the model, the average civic competence score was 488.81 points. This represents the expected performance of children from a socioeconomic context that is average for the sample. All parameters in the final model were statistically significant, as the ratio between the estimate and standard error was greater than 1.96 in each case (Gaviria & Castro, 2004).

In terms of gender, girls outperformed boys in civic competence, which explains the gender gap in favour of girls (boys = 504; girls = 519.66). Being female was associated with a performance boost of 8.87 points compared to boys.

Students from more advantaged socioeconomic backgrounds performed better in civic competence. For every point increase in the socioeconomic context, the average performance improved by 15.74 points. Immigrant status had a negative impact on performance, with immigrant students scoring 13.45 points lower than their native peers.

Participation in community organisations or groups had a positive effect on civic competence. For each additional point of participation, students’ scores increased by 3.08 points. Students who frequently used digital media to engage with social issues performed better than those who rarely or never did. For each increase in the frequency of digital media use, performance improved by 1.26 points. School type significantly affected performance—students attending independent schools scored 10.35 points higher than those from state schools.

To assess the model’s goodness of fit, the likelihood ratio of the null model was compared to that of the final model. The chi-square difference of 25,281.77 with six degrees of freedom was significant at the 0.01 level, confirming that the final model had a better fit. Additionally, the AIC and BIC values decreased, further validating the model’s improved fit.

The R² coefficient indicated the proportion of variance in the dependent variable explained by the predictors in the final model. Upon comparing the random parameters of this model with those of the null model (Snijders & Bosker, 2012), we found that the final model explained 27% of the variance between students (R²= 0.2726) and 53% of the variance between schools (R²= 0.5325). This highlights the substantial influence of school ownership on civic competence outcomes.

Discussion and Conclussions

This research analysed the influence of student- and school-related predictors on the civic competence performance of students in ESO. To this end, six key predictors were identified based on the theoretical framework and the CART procedure, as they had the greatest impact on the dependent variable.

The first independent variable—student gender—emerged as a significant predictor of civic competence. The results indicated that girls outperformed boys, aligning with prior research (Gómez & Suárez, 2023; Solhaug & Kristensen, 2020) and demonstrating that girls tend to have higher levels of civic knowledge and are more likely to participate in activities promoting equity and social justice at school (Juanes & Jacott, 2020; Kuang & Kennedy, 2018; Ten Dam et al., 2020). However, Cicognani et al. (2012) and Marta et al. (2006) found no significant gender differences in civic competence. These findings suggest a need for educational practices to address and reduce the gender gap to promote education based on social justice.

The second independent variable—students’ socioeconomic context—was also a significant predictor of civic competence, corroborating the results of previous studies (Berkowitz et al., 2017; Coleman et al., 1966), which found that students from advantaged backgrounds tend to perform better than those from disadvantaged backgrounds (Castillo, 2015; Deimel et al., 2020; Trunk et al., 2022). This may be due to the greater availability of resources in privileged households, which better support learning about democratic institutions and processes (Ye, 2018), leading to a higher willingness to participate in social justice activities (Carrasco, 2020; Miranda, 2023).

The third independent variable—immigrant status—revealed significant differences between native and immigrant students, with immigrant students performing worse in civic competence. This finding is consistent with other research (Guerra et al., 2019; Teltemann & Schunck, 2017) and is often attributed to native students having greater familiarity with the democratic processes of the country in which they reside (Azzolini, 2016; Choi & Cha, 2021). Nevertheless, these results vary across countries. For instance, Zhu et al. (2019) found that in Hong Kong, immigrant students outperform native students in civic competence, largely due to high parental expectations and a strong educational background at home and school (Goodman, 2021). This finding highlights the importance of civic education in fostering the inclusion and integration of immigrant students within schools.

Regarding the fourth independent variable—student participation in community groups or organisations—the results showed that it is a significant predictor of civic competence performance (Maurissen, 2020; Myoung & Liou, 2022). This is in accordance with the results of other studies (Blaskó et al., 2019; Deimel & Abs, 2022; Feitosa, 2020; Granizo et al., 2020), which demonstrated that students with higher levels of civic knowledge tend to have more positive attitudes towards ethnic minorities and immigrants and are more likely to engage in community groups or organisations.

The fifth independent variable—student participation using digital media—also proved to be a significant predictor of civic competence performance. This supports other research demonstrating the influence of various digital activities on civic competence, including sharing information on the Internet about social issues posted by others (Gleason & Von Gillern, 2018), searching for information safely and responsibly (Cabero-Almenara et al., 2019; Lauricella et al., 2020), and publishing content on the Internet on specific social issues, such as climate change (Pangrazio & Sefton-Green, 2021), gender equality (Estanyol et al., 2023), and politics (Kim & Ellison, 2021; Ohme, 2019).

The fourth and fifth conclusions underscore the critical role schools play in encouraging student participation in groups that promote civic education, with digital media serving as a key tool to drive engagement.

As for the sixth independent variable—school ownership—the results showed that it is a significant predictor of performance, consistent with other studies (Keating, 2016; Park & Holloway, 2017). The data revealed significant differences between state and independent schools, favouring the latter (Collado et al., 2015; Mizala & Torche, 2012). Nonetheless, some research suggested that there are no substantial differences between the two in terms of civic competence (Dijkstra et al., 2023; Gil-Flores & García-Gómez, 2017).

This study presents a series of conclusions and proposals aimed at enhancing civic competence among students in ESO in Spain. First, the disparity in civic competence between boys and girls highlights the need for further research into the gender gap in civic education. Second, the significant effect of socioeconomic context on civic competence suggests a strong need for collaboration between families and schools in promoting civic education. Third, the performance gap between native and immigrant students highlights the importance of developing targeted improvement plans in schools. These initiatives should aim at strengthening immigrant students’ understanding of civic processes and encourage their active participation. Fourth, the positive impact of student participation in community groups or organisations on civic competence suggests that schools should actively collaborate with local organisations. This would raise awareness of the benefits of involvement in groups aligned with students’ interests and concerns. Fifth, the influence of digital media in civic education activities indicates the need for schools to promote the responsible use of technology. Activities could include researching current events online and creating digital content that fosters civic engagement. Sixth, the significant differences in civic competence between students in state and independent schools highlight the need for increased resources in state schools, particularly those in disadvantaged areas, to strengthen civic education.

A limitation of this study is its exclusion of cases with one or more missing values, particularly when dealing with complex variables, such as the Socioeconomic Index of the students, which relies on averages of three independent factors. Analysing only complete cases ensure accurate and unbiased results.

This study opens new avenues for research, particularly on the gender gap, which disadvantages boys. Future studies should explore the reasons behind this inconsistency, such as the civic education received at home and boys’ participation in civic-promoting activities. Additionally, there is scope for further research into the differences between state and independent schools in terms of civic competence, focusing on factors such as the civic education activities conducted within each institution.

In conclusion, this work demonstrates the influence of various student- and school-level predictors on the civic competence of Spanish students in the second year of ESO who participated in the ICCS 2022. The findings contribute to understanding how these factors shape students’ roles as active citizens in society.

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Martínez-Garrido, C., & Murillo, F.J. (2014). Programas para la realización de modelos multinivel. Un análisis comparativo entre MLwiN, SPSS y Stata. Revista Electrónica de Metodología Aplicada, 19(2), 1-24. https://doi.org/10.17811/rema.19.2.2014.1-24

Maurissen, L. (2020). Political efficacy and interest as mediators of expected political participation among Belgian adolescents. Applied Developmental Science, 24(4), 339-353. https://doi.org/10.1080/10888691.2018.1507744

Ministerio de Educación y Formación Profesional. (23 de marzo de 2024). ICCS 2022. Marco de Evaluación. https://doi.org/bit.ly/49kGkxv

Ministerio de Educación, Formación Profesional y Deportes. (25 de marzo de 2024a). ICCS 2022. Estudio Internacional sobre Educación Cívica y Ciudadana. Módulo Europeo. https://bit.ly/43AKtMG

Ministerio de Educación, Formación Profesional y Deportes. (26 de marzo de 2024b).

ICCS 2022. Estudio Internacional sobre Educación Cívica y Ciudadana. Informe Español. https://bit.ly/3vs6hNE

Miranda, D. (2023). Socio-economic inequalities of families and their differential impact on types of political participation in the new generation of 24 countries. International Journal of Social Psychology, 38(3), 602-641. https://doi.org/10.1080/02134748.2023.2239576

Mizala, A., & Torche, F. (2012). Bringing the schools back in: The stratification of educational achievement in the Chilean voucher system. International Journal of Educational Development, 32(1), 132-144. https://doi.org/10.1016/j.ijedudev.2010.09.004

Murillo, F. J., Martínez-Garrido, C., & Graña, R. (2023). Segregación escolar por nivel socioeconómico en educación primaria en América Latina y el Caribe. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 21(1), 87-117. https://doi.org/10.15366/reice2023.21.1.005

Myoung, E., & Liou, P.Y. (2022). Systematic review of empirical studies on international large-scale assessments of civic and citizenship education. Scandinavian Journal of Educational Research, 66(7), 1269-1291. https://doi.org/10.1080/00313831.2022.2131903

Ohme, J. (2019). Updating citizenship? The effects of digital media use on citizenship understanding and political participation. Information, Communication & Society, 22(13), 1903-1928. https://doi.org/10.1080/1369118X.2018.1469657

Pangrazio, L., & Sefton-Green, J. (2021). Digital rights, digital citizenship and digital literacy: What´s the difference? Journal of New Approaches in Educational Research, 10(1), 15-27. https://doi.org/10.7821/naer.2021.1.616

Pardo, A., Ruiz, M.A. y San Martín, R. (2007). Cómo ajustar e interpretar modelos multinivel con SPSS. Psicothema, 19(2), 308-321.

Park, S., & Holloway, S.D. (2017). The effects of school-based parental involvement on academic achievement at the child and elementary school level: A longitudinal study. Journal of Educational Research, 110(1), 1-16. https://doi.org/10.1080/00220671.2015.1016600

Pereira, M.F., Fraile, M., & Rubal, M. (2015). Young and gapped? Political knowledge of girls and boys in Europe. Political Research Quarterly, 68(1), 63-76. https://doi.org/10.1177/1065912914554040

Rodríguez-Mantilla, J.M., Fernández-Díaz, M.J., & Jover, G. (2018). PISA 2015: Predictores del rendimiento en Ciencias en España. Revista de Educación, (380), 75-102. https://doi.org/10.4438/1988-592X-RE-2017-380-373

Snijders, T. A., & Bosker, R.J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modelling. Sage Publications.

Solhaug, T., & Kristensen, N.N. (2020). Gender and intercultural competence: analysis of intercultural competence among upper secondary school students in Denmark and Norway. Educational Psychology, 40(1), 120-140. https://doi.org/10.1080/01443410.2019.1646410

Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/10.1037/a0

Teltemann, J., & Schunck, R. (2017). Education systems, school segregation, and second-generation immigrants’ educational success: Evidence from a country-fixed effects approach using three waves of PISA. International Journal of Comparative Sociology, 57(6), 401-424. https://doi.org/10.1177/0020715216687348

Ten Dam, G., Dijkstra, A.B., Van der Veen, I., & Van Goethem, A. (2020). What do adolescents know about citizenship? Measuring student´s knowledge of the social and political aspects of citizenship. Social Sciences, 9(12), 1-24. https://doi.org/10.3390/socsci9120234

Tourón, J., López-González, E., Lizasoain, L., & Navarro, E. (2023). Análisis de datos y medida en educación. (Vol. II). UNIR Editorial.

Trunk, A., Mirazchiyski, E.K., & Mirazchiyski, P.V. (2022). Attitudes towards future unemployment and European cooperation to reduce unemployment among 8th graders in EU/European countries. Investigation in Health, Psychology and Education, 12(2), 218-235. https://doi.org/10.3390/ejihpe12020017

Ye, W. (2018). Socioeconomic status and out-of-school citizenship education in China´s Shanghai. Education and Urban Society, 50(7), 641-669. https://doi.org/0.1177/0013124517713609

Zhu, J., & Chiu, M.M. (2020). Immigrant students in Denmark: why are they disadvantaged in civic learning? Educational Psychology, 40(2), 207-226. https://doi.org/10.1080/01443410.2019.1657560

Zhu, J., Kennedy, K.J., Mok, M.M., & Halse, C.M. (2019). Chinese immigrant students in Hong Kong: Exploring performance and influences on their civic learning. Psych Journal, 8(4), 465-479. https://doi.org/10.1002/pchj.260

Contact address: Pablo Javier Ortega-Rodríguez. Universidad Autónoma de Madrid. Facultad de Formación de Profesorado y Educación. Departamento de Pedagogía. E-mail: pabloj.ortega@uam.es

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Manganelli, S., Lucidi, F., & Alivernini, F. (2014). Adolescents´ expected civic participation: The role of civic knowledge and efficacy beliefs. Journal of Adolescence, 37(5), 632-641. https://doi.org/10.1016/j.adolescence.2014.05.001

Marta, E., Guglielmetti, C., & Pozzi, M. (2006). Volunteerism during young adulthood: An italian investigation into motivational patterns. International Journal of Voluntary and Nonprofit Organizations, 17(3), 221-232. http://doi.org/10.1007/s11266-006-9015-3

Martínez-Garrido, C., & Murillo, F.J. (2014). Programas para la realización de modelos multinivel. Un análisis comparativo entre MLwiN, SPSS y Stata. Revista Electrónica de Metodología Aplicada, 19(2), 1-24. https://doi.org/10.17811/rema.19.2.2014.1-24

Maurissen, L. (2020). Political efficacy and interest as mediators of expected political participation among Belgian adolescents. Applied Developmental Science, 24(4), 339-353. https://doi.org/10.1080/10888691.2018.1507744

Ministerio de Educación y Formación Profesional. (23 de marzo de 2024). ICCS 2022. Marco de Evaluación. https://doi.org/bit.ly/49kGkxv

Ministerio de Educación, Formación Profesional y Deportes. (25 de marzo de 2024a). ICCS 2022. Estudio Internacional sobre Educación Cívica y Ciudadana. Módulo Europeo. https://bit.ly/43AKtMG Ministerio de Educación, Formación Profesional y Deportes. (26 de marzo de 2024b). ICCS 2022. Estudio Internacional sobre Educación Cívica y Ciudadana. Informe Español. https://bit.ly/3vs6hNE y/3vs6hNE

Miranda, D. (2023). Socio-economic inequalities of families and their differential impact on types of political participation in the new generation of 24 countries. International Journal of Social Psychology, 38(3), 602-641. https://doi.org/10.1080/02134748.2023.2239576

Mizala, A., & Torche, F. (2012). Bringing the schools back in: The stratification of educational achievement in the Chilean voucher system. International Journal of Educational Development, 32(1), 132-144. https://doi.org/10.1016/j.ijedudev.2010.09.004

Murillo, F. J., Martínez-Garrido, C., & Graña, R. (2023). Segregación escolar por nivel socioeconómico en educación primaria en América Latina y el Caribe. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 21(1), 87-117. https://doi.org/10.15366/reice2023.21.1.005

Myoung, E., & Liou, P.Y. (2022). Systematic review of empirical studies on international large-scale assessments of civic and citizenship education. Scandinavian Journal of Educational Research, 66(7), 1269-1291. https://doi.org/10.1080/00313831.2022.2131903

Ohme, J. (2019). Updating citizenship? The effects of digital media use on citizenship understanding and political participation. Information, Communication & Society, 22(13), 1903-1928. https://doi.org/10.1080/1369118X.2018.1469657

Pangrazio, L., & Sefton-Green, J. (2021). Digital rights, digital citizenship and digital literacy: What´s the difference? Journal of New Approaches in Educational Research, 10(1), 15-27. https://doi.org/10.7821/naer.2021.1.616

Pardo, A., Ruiz, M.A., & San Martín, R. (2007). Cómo ajustar e interpretar modelos multinivel con SPSS. Psicothema, 19(2), 308-321.

Park, S., & Holloway, S.D. (2017). The effects of school-based parental involvement on academic achievement at the child and elementary school level: A longitudinal study. Journal of Educational Research, 110(1), 1-16. https://doi.org/10.1080/00220671.2015.1016600

Pereira, M.F., Fraile, M., & Rubal, M. (2015). Young and gapped? Political knowledge of girls and boys in Europe. Political Research Quarterly, 68(1), 63-76. https://doi.org/10.1177/1065912914554040

Rodríguez-Mantilla, J.M., Fernández-Díaz, M.J., & Jover, G. (2018). PISA 2015: Predictores del rendimiento en Ciencias en España. Revista de Educación, (380), 75-102. https://doi.org/10.4438/1988-592X-RE-2017-380-373

Snijders, T. A., & Bosker, R.J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modelling. Sage Publications.

Solhaug, T., & Kristensen, N.N. (2020). Gender and intercultural competence: analysis of intercultural competence among upper secondary school students in Denmark and Norway. Educational Psychology, 40(1), 120-140. https://doi.org/10.1080/01443410.2019.1646410

Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/10.1037/a0

Teltemann, J., & Schunck, R. (2017). Education systems, school segregation, and second-generation immigrants’ educational success: Evidence from a country-fixed effects approach using three waves of PISA. International Journal of Comparative Sociology, 57(6), 401-424. https://doi.org/10.1177/0020715216687348

Ten Dam, G., Dijkstra, A.B., Van der Veen, I., & Van Goethem, A. (2020). What do adolescents know about citizenship? Measuring student´s knowledge of the social and political aspects of citizenship. Social Sciences, 9(12), 1-24. https://doi.org/10.3390/socsci9120234

Tourón, J., López-González, E., Lizasoain, L., & Navarro, E. (2023). Análisis de datos y medida en educación. (Vol. II). UNIR Editorial.

Trunk, A., Mirazchiyski, E.K., & Mirazchiyski, P.V. (2022). Attitudes towards future unemployment and European cooperation to reduce unemployment among 8^th graders in EU/European countries. Investigation in Health, Psychology and Education, 12(2), 218-235. https://doi.org/10.3390/ejihpe12020017

Ye, W. (2018). Socioeconomic status and out-of-school citizenship education in China´s Shanghai. Education and Urban Society, 50(7), 641-669. https://doi.org/0.1177/0013124517713609

Zhu, J., & Chiu, M.M. (2020). Immigrant students in Denmark: why are they disadvantaged in civic learning? Educational Psychology, 40(2), 207-226. https://doi.org/10.1080/01443410.2019.1657560

Zhu, J., Kennedy, K.J., Mok, M.M., & Halse, C.M. (2019). Chinese immigrant students in Hong Kong: Exploring performance and influences on their civic learning. Psych Journal, 8(4), 465-479. https://doi.org/10.1002/pchj.260