**This project contains supplementary material for the following publication:** Mede, N. G. (2023). Variations of science-related populism in comparative perspective: A multilevel segmentation analysis of supporters and opponents of populist demands toward science. *International Journal of Comparative Sociology*. Advance online publication. https://doi.org/10.1177/00207152231200188 ______________ I share all materials needed to reproduce the results presented in the main article and the supplementary materials, i.e. the survey data, R code, figures in high resolution, as well as pre-computed <code>glca</code> and <code>brms</code> models. Please find the following folders and files: - **01_data**: Contains all data used in the analyses. - <code>svydata_AU.rds</code>: Austrian survey data (*n* = 1,528), includes relevant variables from the 21st wave of the *Austrian Corona Panel Project*. The full dataset, methodological details, and project information can be obtained at the [University of Vienna website][1]. - <code>svydata_DE.rds</code>: German survey data (*n* = 1,597), includes relevant variables from a survey colleagues and I conducted for a [research project][2] on social media communication in Germany and Taiwan. - <code>svydata_CH.rds</code>: Swiss survey data (*n* = 166), includes relevant variables from the COVID-19 Edition of the *Science Barometer Switzerland*. Methodological details and project information can be obtained at the [Science Barometer website][3]. - <code>svydata_TW.rds</code>: Taiwanese survey data (*n* = 1,307), includes relevant variables from a survey colleagues and I conducted for a [research project][2] on social media communication in Germany and Taiwan. - <code>wgm_2020.rds</code>: The complete data of the Wellcome Global Monitor 2020, which were used to assess levels of trust in science and trust in scientists in Austria, Germany, Switzerland, and Taiwan (see section “Research Questions and Study Design”). The data are also publicly available at the [Wellcome website][4]. <br> - **02_code**: Contains the R scripts used for the analyses. Should be executed in chronological order. - <code>01_setup.R</code>: Script to import and set up the data. - <code>02_sample-descriptives.R</code>: Script to inspect sample characteristics as well as descriptive statistics for science-related populist attitudes and the covariates. - <code>03_lcas.R</code>: Script to run the Fixed-Effects Latent Class Analyses and inspect goodness-of-fit crietria used for FE-LCA model selection. Precomputed FE-LCA models can be found in the folder “03_models”. - <code>04_class-description.R</code>: Script to examine the segments (labeling, sizes, sociodemographic and attitudinal characteristics) and plot Figures 1 and 3, which can be found in the folder “04_figures”. - <code>05_class-prediction.R</code>: Script to predict segment membership with the covariates, using Bayesian logistic regression, and plot Figure 2 and 4. Pre-computed regression models can be found in the folder “03_models”, Figure 2 and 4 can be found in the folder “04_figures”. - <code>06_secanalysis-wgm.R</code>: Script used to assess levels of trust in science and trust in scientists in Austria, Germany, Switzerland, and Taiwan based on the Wellcome Global Monitor 2020 data (see section “Research Questions and Study Design”). <br> - **03_models**: Contains pre-computed FE-LCA models fitted with the <code>[glca][5]</code> package and pre-computed Bayesian regression models fitted with the <code>[brms][6]</code> package. <br> - **04_figures**: Contains all figures in as vector graphics (PDF format). <br> - **05_appendix**: Contains the supplementary file (tables of additional analyses). _____________________ [1]: https://viecer.univie.ac.at/en/projects-and-cooperations/austrian-corona-panel-project/page/14/ [2]: https://osf.io/yrm8w/?view_only=36957601840042b989e0cab19575e293 [3]: https://wissenschaftsbarometer.ch/en/science-barometer-switzerland/ [4]: https://wellcome.org/reports/wellcome-global-monitor-covid-19/2020#downloads-6b45 [5]: https://cran.r-project.org/web/packages/glca/ [6]: https://cran.r-project.org/web/packages/brms/