This repository contains code and synthetic data for:
Martinovici, Ana, Rik Pieters, and Tulin Erdem (2022), "Attention Trajectories Capture Utility Accumulation and Predict Brand Choice", Journal of Marketing Research, https://doi.org/10.1177/00222437221141052
If you want to cite the content of this repository, please use:
Martinovici, Ana (2022), "Reproducibility package for Martinovici, Pieters, and Erdem (2022)", DOI: https://doi.org/10.25397/eur.21603288
To improve the likelihood of numerical reproducibility, this repository contains a makefile which specifies code execution order.
Sections 1-3 of Baker (2020) provide an introduction to GNU Make (Mecklenburg 2004) and basic concepts relevant to using a makefile. If you want to learn more about Make, I can recommend these freely-available resources :
In order to build the targets specified in the makefile, you need to have the following installed on your device: Make, R, RStudio, rstan, and tidyverse.
Open a terminal and type which make. If you see a message that says "which: no make" and then a series of paths, then you either do not have make installed, or Make is not added to Path (Environment Variables on Windows). If you get a message that shows a path to bin/make, then you have Make installed and added to Path.
After you clone the repository, open a terminal and check that the working directory is the root directory of the repository. In the terminal, type make test_target. The test is successful if it generates test_setup/stan_test.rds and test_target.
After you clone the repository, open a terminal and check that the working directory is the root directory of the repository. In the terminal, type make. This will start executing the code needed to produce the tables and figures that summarise results based on synthetic data. output/current_results contains these figures and tables.
Note that building all targets is time consuming and computationally intensive (e.g., estimation of the attention model component takes 5-6 days on a Windows machine with 64GB of RAM and Intel Xeon CPU E5-1620 V3 3.5Hz). See all_Make_steps.txt for the sequence in which the scripts are executed and the arguments provided to each script.
Baker, P. (2020). Using GNU make to manage the workflow of data analysis projects. Journal of Statistical Software, Code Snippets, 94 (1), 1–46. https://doi.org/10.18637/jss.v094.c01
Mecklenburg, R. (2004). Managing projects with GNU make (3rd ed.). O’Reilly Media, Inc.