Work Description

Title: Data and Code for Exp Econ Paper On the Stability of Norms and Norm-following Propensity Open Access Deposited

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Methodology
  • Using a whole school year approach, our study design involved an investigation of the evolution of social networks and norms around smoking before and after a smoking prevention intervention. First, we conducted a cultural adaptation and a pilot study in three schools (one school in Northern Ireland and two schools in Colombia) with 312 pupils during 2018–2019. In 2019, in each country (Northern Ireland and Colombia) we studied ~600 pupils (aged 12–13 years/Year nine pupils in Northern Ireland and aged 12–15 years/Year seven in Colombia) in six secondary (i.e., post-primary) level schools (three receiving each intervention) exploring the contrasts between schools receiving different interventions and between countries (where norms are different). As part of the MECHANISMS study (Hunter et al. 2020), we collected repeated measures data on 1468 students aged 11-15 years old in 15 schools in and around Belfast, Northern Ireland and in Bogotá, Colombia. Participation was open to all students in a school-year group (approximately 100 students per school); uptake was approximately 90% at each location. These data provide us with two measures from each participant of a proxy for norm-following propensity and beliefs about injunctive norms in the dictator game, collected approximately 10 weeks apart. These measures were not expected to change between waves as they were collected as controls alongside a broad set of other measures including norms related to smoking/vaping, self-reports of smoking behavior and intentions, social networks, and personality traits. These control measures served two purposes. First, since the dictator game is the most widely studied game in the social norms literature and yields remarkably consistent responses, on average, in norm-elicitation experiments, we had strong priors about what the elicited norm (hereafter DGN) would look like, on average. Thus, deviations from this prior would serve as a sort of warning light to us regarding participants’ understanding of the norm elicitation procedure which was also being used to measure norms related to smoking. Second, and more important for the purposes of this paper, while we anticipated that the interventions would influence norms related to smoking – since they were designed to do so – we had no reason to expect the interventions to influence the DGN or in norm-following propensity since the interventions were not designed to influence either of these. Thus, our (null) hypotheses were that neither the DGN nor the norm-following propensity would change, on average over time. The data were processed using R scripts using R 4.3.1, all Stata Scripts were run using Stata 17.0, and all MPlus scripts were run using MPlus 8.8, on a 2023 MacBook Pro. SURVEY USED TO COLLECT DATA: The complete English version of our survey is included in the appendix of our paper.
Description
  • THE DATA: Unfortunately, we are unable to share our data for this project. Since we were working with a vulnerable population (children), we were asked by Queens University Belfast’s IRB-equivalent to include language in the consent documents indicating that the data would not be shared outside of the research team. Thus, the datasets generated during and/or analyzed during the current study are not publicly available as participants were informed that no-one outside of the research team would have access to the research data when they signed their consent forms. Thus, we provide Stata, R and Mplus scripts used to generate all tables and figures reported in the paper. Since we cannot share the raw study data, most of these files cannot be run, but in the interest of transparency we include the scripts so that our code can be checked. Since a major portion of the paper is the LTA modeling, we took an additional step there and generated simulated data that allows the R+Mplus scripts to be run. These runnable scripts and the simulated data are contained in the subfolder LTA_code_EXEC. For further information about the study datasets, please contact the authors (Emails:  Jennifer.Murray@qub.ac.uk;  ruth.hunter@qub.ac.uk)
Creator
Creator ORCID
Depositor
  • ekrupka@umich.edu
Contact information
Discipline
Funding agency
  • Other Funding Agency
Other Funding agency
  • Medical Research Council Population and Systems Medicine Board (reference number MR/R011176/1)
Keyword
Citations to related material
  • Kimbrough, E., Krupka, E., Kumar, R., Murray, J., and Ramalingam, A. (conditional accept). On the Stability of Norms and Norm-Following Propensity: A Cross Cultural Panel Study with Adolescents. Experimental Economics
Resource type
Last modified
  • 09/06/2024
Published
  • 09/06/2024
Language
DOI
  • https://doi.org/10.7302/w77t-za90
License
To Cite this Work:
Kimbrough, E., Murray, J., Sarmiento, O., Krupka, E., Ramalingam, A., Kee, F., Kumar, R., Sánchez-Franco, S., Hunter, R. (2024). Data and Code for Exp Econ Paper On the Stability of Norms and Norm-following Propensity [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/w77t-za90

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Files (Count: 13; Size: 2.99 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
README.txt 2024-01-05 2024-01-05 3.58 KB Open Access
Bonferroni_Corrected_diff_in_dif....xlsx 2024-01-05 2024-01-05 9.22 KB Open Access
DataAnalysis.do 2024-01-05 2024-01-05 22.9 KB Open Access
MakeAdditionalFigures.R 2024-01-05 2024-01-05 3.25 KB Open Access
NetworkStats.do 2024-01-05 2024-01-05 22.6 KB Open Access
(0) Simulate_Data.R 2024-01-05 2024-01-05 2.17 KB Open Access
(1) Execute_MPlus_scripts_mac.R 2024-01-05 2024-01-05 2.52 KB Open Access
(2) Produce_Figures_mac.R 2024-01-05 2024-01-05 6.31 KB Open Access
Mplus Run Models.log 2024-01-05 2024-01-05 420 Bytes Open Access
RI_LTA.png 2024-01-05 2024-01-05 85 KB Open Access
RI_LTA_5_save.dat 2024-01-05 2024-01-05 718 KB Open Access
RI_LTA_5Classes_realData.out 2024-01-05 2024-01-05 2.13 MB Open Access
trc_RI_LTA_Classes_realData.csv 2024-01-05 2024-01-05 172 Bytes Open Access

This file explains how to run the LTA code for Kimbrough et al. (2023) - "On the Stability of Norms and Norm-following Propensity: A Cross-Cultural Panel Study with Adolescents"

All R scripts were run using R 4.2.2, and all MPlus scripts were run using MPlus 8.8, on a 2023 MacBook Pro.

In this folder, you will find 5 main subfolders:
1) model_estimates_real - this contains model estimates derived from application of the 5-class RI-LTA model described in the paper to our raw data (the estimates are stored in a .out file and a .dat file) and a table from the paper showing the distribution of subjects across latent classes (a .csv file). The .dat file was modified ex post to remove the raw data which we are not allowed to share per our ethics approval. These files are used as an input to the (0) Simulate_Data.R script, which generates the file in the next subfolder:
2) simulated_data - this contains 1468 simulated choice patterns based on the model estimates in the model_estimates_real folder
3) input_files - this contains .inp files with MPlus scripts for the LTA and RI-LTA models, for 2-5 classes
4) output_files - this holds the .out and .dat files produced by running each of the MPlus scripts.
5) result_files - this holds figures and tables produced using the .out and .dat files in the output_files folder

The resp.dat file contains simulated individual-level responses to the norm-elicitation protocol at T1 and T2. Each row represents a single individual. Since we are unable to share our raw data due to IRB restrictions, we have produced simulated data for the same number of subjects according to the type distribution estimated from the data. See the file (0) Simulate_Data.R for details on how the simulated data were generated.

The .inp files run the Mplus analyses. They depend on the resp.dat file.

The key variables are contained in the first 22 columns of the resp.dat file. When read into MPlus, they are labelled r1-r11 (T1) and s1-s11 (T2), where the index 1 in the variable name corresponds to the action "give 0" and 11 corresponds to "give 10", r corresponds to the first time period and s refers to the second time period.

There are two script files which take this resp.dat file as an input and perform LTA and RI-LTA analyses, using an R package to run the MPlus scripts.

To run, you must have a working edition of MPlus version 8.4 or later with the Mixture Add-On (see: https://www.statmodel.com/pricing.shtml) and a recent copy of R installed from the CRAN archive.

Before running the scripts in R, make sure you install the MplusAutomation package and scales package using the "install.packages" command.

Step 1: Run (0) Simulate_Data.R

~ This generates the simulated data.

Step 2: Run (1) Execute_MPlus_scripts.R

~ This runs all the scripts from the .inp files in the input_files folder and produces the .out files and saves them in the output_files folder
- NOTE: RI_LTA_5Classes.inp may take a long time to run (the number of "starts" required to get the model to converge is lower for the simulated data than for our raw data).

~ The RI_LTA.png file depicts the main model reported in the paper. A latent random intercept f (with mean=0 and variance=1) and latent class variables c1 and c2 are modeled as together causing the distribution of responses to the dictator game norm elicitation (the eta variables, subscripted 0-10 for the amounts and superscripted 1-2 for the time period).

Step 3: Run (2) Produce_figures.R

~ This generates the figures and trp.csv and trc.csv files (which contain the transition matrices for the LTA model) in the relevant subfolders of the result_files folder.

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