DOI: 10.3886/e120596
title: COVID-19, Crises, and Public Support for the Rule of Law Teaching Modules

description: The COVID-19, Crises and Support for the Rule of Law Teaching Modules provide an engaging way for undergraduate students to investigate questions relating to the public compliance with and response to state actions meant to quell the viral spread. These modules will provide students an opportunity to critically engage questions surrounding public policy and state responses to the global pandemic, while honing their skills using the tools of social science. <br><br>The modules are based on research funded by the National Science Foundation, which contain original surveys of U.S. and German residents collected since the onset of the global crisis. The first two modules examine the shift in time spent outside the home before and after crisis onset, and in response to governmentally imposed stay-at-home orders. The third module shifts focus to the public’s (in)tolerance for non-compliance with mask-wearing ordinances in the United States. Through these modules, instructors and students can engage with an issue that is happening in real time, and explore how implicit biases may shape willingness to punish non-compliance with local mandates. <br><br>These modules are targeted towards advanced undergraduates in applied statistics or upper-division courses; students are asked to engage the use of descriptive statistics, data visualization, hypothesis testing, bivariate and multivariate regression. Further, modules two and three also include experimental components of the research design, which may lend themselves to discussions regarding observational vs. experimental research, causal identification, and potential threats to inference.<br><br>Each module includes the dataset, assignments, codebooks and documentation as well as the R and Stata code used to answer the questions. For an answer key, instructors may contact the authors directly, using their university affiliated email address. These data and teaching modules are based on data from Driscoll, Krehbiel and Nelson, 2020 “RAPID: COVID-19, Crises and Support for the Rule of Law,” National Science Foundation, SES-2027653, SES-2027664, SES-2027671. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.<br><br>Instructors interested in the answer key for these teaching modules should contact Taylor Chewning (tkc19@my.fsu.edu) via your institutional email address.<br><br><br>
description: N/A
description: Response Rates: For the data used in Module 1 (US MTurk, June 16 & 17, 2020), the recruitment text was posted on Mechanical Turk inviting respondents to answer a short survey about support for institutions and policies in the United States. We recruited 1046 respondents in total. Please see Collection Notes for additional details. <br><br>For the German data, 4729 respondents were interviewed who were then matched down to a sample of 4400 to produce the final wave 1 dataset. See Collection Notes and Sampling for more detail.<br><br>For the data used in Module 3 (US MTurk, June 3, 2020), the recruitment text was posted on Mechanical Turk inviting respondents to answer a short survey about support for institutions and policies in the United States. We recruited 1506 respondents in total. Please see Collection Notes for additional details. <br><br>
description: Presence of Common Scales: See codebook for relevant details.
description: web-based survey; The data contained in teaching module 1 (Germany/U.S. comparison) are based on an (unweighted) sample from of a nationally representative online survey in Germany, administered by YouGov between April 18 and May 15 of 2020. This study was cleared by the West Virginia Institutional Review Board (#2003938143). The U.S. data are a convenience sample of consenting U.S. adults, recruited from Amazon Mechanical Turk on June 16 & 17 of 2020. These studies was cleared by the Florida State University Institutional Review Board (STUDY00000445 & STUDY0000123). Of the 1046 respondents recruited to our survey, 275 were excluded from the analysis for failure to pass an attention check in our survey, and another 48 were excluded because they indicated they did not experience a stay at home order in their state. An additional 9 observations were dropped owing to missing data on the number of hours spent outside. The combined dataset (Germany and US observations) contains 1288 observations. <br><br>The data contained in teaching module 2 (Germany lock-downs and income) based on an (unweighted) sample from of a nationally representative survey in Germany, administered by YouGov between April 18 and May 15 of 2020. This study was cleared by the West Virginia Institutional Review Board (#2003938143).<br><br>The data contained in teaching module 3 (public evaluation of mask-wearing non-compliance) is based on a convenience sample of consenting U.S. adults, recruited from Amazon Mechanical Turk on June 3 of 2020. This study was cleared by the Pennsylvania State University Institutional Review Board (STUDY00014827 & STUDY00014842). Of the respondents, 150 were excluded from the analysis for failure to pass an attention check in our survey. An additional 10 observations were dropped owing to missing data on control variables (8 in gender, 1 in age, 1 in bornagain).
description: Persons aged 18 and over living in Germany with an internet connection.<br><br>Persons aged 18 and over living in the United States with an internet connection and seeking `hits' on Mechanical Turk (Amazon). . Smallest Geographic Unit: Country
description: For the Germany data, respondents were matched to a sampling frame on gender, age and education. The frame was constructed by a stratified sampling sampling form the 2018 Eurobarometer with selection within strata by weighted sampling (using the person weights on the public use file). The matched cases were weighted to the sampling frame using propensity scores. The matched cases and the frame were combined and a logistic regression was estimated for inclusion in the frame. The propensity score function included age, gender, years of education, and state. The propensity scores were grouped into deciles of the estimated propensity score in the frame and post-stratified according to these deciles. The weights were the post-stratified on 2017 General Election vote choice, and a stratification of gender, state, age (4-categories) and education (4-categories) to produce a final weight. In wave 2, YouGov recontacted all 4400 wave 1 respondents and achieved 3697 completed wave 2 interviews. YouGov prepared a wave 2 weight following the same procedures as in wave 1. <br><br>U.S. data were both convenience samples, recruited on Mechanical Turk.<br><br><br>