The data are reported on in the paper "Sensitivity to sunk costs depends on attention to the delay" by Rebecca Kazinka, Angus W. MacDonald, & A. David Redish (University of Minnesota). Analyses were completed on Matlab 2019. We divided the data into two different folders, Preprocessed Data and Raw Data. Preprocessed Data contains everything needed to run the analyses in the paper. The data used in the paper are in two structures, X.mat (all trials for all eligible participants) and W.mat (summarized data by eligible participant). Analyses for the paper were conducted in the file GenerateFigures.m. It will call the other functions in the folder. If you would prefer to look at the data in a table, it is also available in X.csv and W.csv. Preprocessing analysis scripts and data are in the Raw Data folder. X and W structures were created using a script called WrapperWebSurfPhase3Analyses.m, which applies exclusionary criteria to remove any incomplete data. WrapperWebSurfMturkPaperTable1.m is used to summarize the demographic data and rates of participation for each stage. This function loops through the separate batches (described below) to summarize all of the data, and could be used to collect all participant data in the first output variable of the script. Individualized data can be found in the five folders located in the Raw Data Folder with dates. We collected data in 5 batches. Batches 1 & 2 were the attention check version, and 3-5 are the original version. The original .csv files from PGAdmin are also located in these files, as well as questionnaire data from qualtrics. A small amount of editing was done with the data .csv files to remove excess information from initializing the task that did not translate well from json to csv. RawMturkPreprocessing.m can be used to create the individualized data .mat files by batch, which are also located in the Raw Data folder. You will also need the following non-standard functions, which can be found as part of Matlab's AddOn packages. - mixed_between_within_anova - ColorBrewer - LOESS regression smoothing