Value Congruence and Social Capital in 30 European Social Survey Countries: Analytic Dataset and Analysis Code (ESS Round 11)

This record contains the analytic dataset, country-level data, analysis code and codebook accompanying the article on how basic human values (Schwartz, PVQ-21) relate to five components of social capital and their general index in 30 countries of the European Social Survey, Round 11 (N = 47,687 respondents). The study compares three competing models of the value–social capital relation (a coordination model, a level-priority model and a congruence-priority model) using person–country value congruence indices for each of the ten values, within-country centred value levels, country-level value means and multilevel models with random slopes. Contents data/ ess11_vc_sc_respondents.csv and .sav — respondent-level analytic sample (47,687 rows, 97 variables, 30 countries): ESS identifiers and weights, the 21 PVQ-21 items and 17 social-capital items in original ESS coding, ten value levels, country means, ten value congruence indices, the country value consensus index, MRAT, profile correlation, six composite social-capital scores (generalized trust, institutional trust, ethnic tolerance, informal contacts, perceived political efficacy, general index) and control variables. The SPSS version carries variable labels. ess11_vc_sc_countries.csv — 30 countries: analytic sample size, country means of the ten values, value consensus index, weighted country means of the composite scores and value levels. mlm_data.csv — model-ready dataset for the multilevel models (standardized outcomes and predictors, block indices, variables for the tests of the form of the relation and for the robustness checks). sc_items.csv — cleaned social-capital items for the measurement models in lavaan. scripts/ — the full pipeline from the raw ESS file to the tables of the article: 01 value levels and congruence indices (Python), 02 composite scores (Python), 02b measurement invariance and factor scores (R, lavaan), 03 model-ready dataset (Python), 04 main multilevel models (R, lme4/lmerTest), 05 form of the relation (two-slope models, comparison of turning-point specifications by AIC, jackknife), 06 robustness checks, contextual model and country-level correlation (R), 07 FDR correction (Python), 08 assembly of the deposited files (Python). CODEBOOK.csv — bilingual (English/Russian) description of every variable, with value ranges and construction rules. README.md — reproduction instructions and verification notes. LICENSE_AND_CITATION.md — terms of use and citation. Methods summary Value levels are means of the reversed PVQ-21 items (1–6, higher = more important). Respondents with more than five missing PVQ items or more than 16 identical responses were excluded (Schwartz screening rule), leaving 47,687 of 50,116 respondents. Country means use the ESS analysis weight. The value congruence index for value j is 1 − |x′ − M⁽⁻ⁱ⁾| / max(M⁽⁻ⁱ⁾, 1 − M⁽⁻ⁱ⁾), where x′ is the score normalized to [0, 1] and M⁽⁻ⁱ⁾ is the leave-one-out weighted country mean. The country value consensus index is based on the Euclidean distance of each respondent’s ten-value profile from the country mean profile. Composite social-capital scores are means of z-standardized items. Steps 1–3 of the pipeline were re-run on the deposited files and reproduce the published values exactly. Source data and reuse All ESS variables are taken from the ESS Round 11 integrated file, edition 4.1 (ESS ERIC, 2025, https://doi.org/10.21338/ess11e04_1), which is not redistributed here and is available from the ESS Data Portal. ESS data are distributed under a Creative Commons licence (CC BY-NC-SA 4.0 at the time of deposit), and this derived dataset is shared under the same terms. The authors’ code is released under the MIT licence.