Brief description of each file in this directory listed below
Any files added to the "preprocessing" folder should be included in this list along with the relative link (./) of that file
- imports the large raw AX .csv file (combinedRawData/AXall.csv) and removes, renames and reformats columns so that data is basically ready for analysis
- recodes response variables so that 1= "left" or "v" and 2 = "down" or "n"
- replaces prolific IDs with subIDs
- output: "/Volumes/CAP/data/combinedData/AXallClean.csv"
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fixes the file format for all participants on phase 1 day 3 and one participant from phase 1 day 11 where there were issues with recording participant ID numbers.
- misformatted raw data files are in rawData/phase1/day#/axcpt/axcptMisformatted/
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combines raw axcpt data in 3 ways:
- across participants within each day (40 files)
- across days within each phase (2 files)
- across both phases (1 file)
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output:
- 1 .csv file per day per phase stored in the rawData/combinedRawData/combinedRawData/AXday directory (e.g. collateAXphase2day1)
- 1 .csv file per phase (each .csv file includes data from all days) stored in combinedRawData/AXphase directory (AXphase1.csv & AXphase2.csv)
- 1 .csv file for all the raw AX data (AXall.csv) in the rawData/combinedAXData directory
- Reformatted raw data files for all participants on phase 1 day 3 and one participant from phase 1 day 11 (stored in rawData/phase1/day#/axcpt).
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creates two participant lists for phase 1 and phase 2 based on the data files we have in rawData (its scrapes the prolific ID name from each file name)
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output includes both prolific ID and CAP sub ID (the latter we will use for analyses moving forward)
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This script also confirms that we have a file of the risky choice and axcpt task for all participants and that the prolific IDs are the same for risky choice and axcpt files.
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output:
- 1 .csv file per phase with columns {prolific ID, subID day, phase}
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fixes the file format for all participants on phase 1 day 3 and one participant from phase 1 day 11 where there were issues with recording participant ID numbers.
- misformatted raw data files are in rawData/phase1/day#/riskyChoice/riskyChoiceMisformatted/
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combines raw risky decision-making data in 3 ways:
- across participants within each day (40 files)
- across days within each phase (2 files)
- across both phases (1 file)
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output:
- 1 .csv file per day per phase stored in the rawData/combinedRawData/combinedRawData/RDMday directory (e.g. collateRDMphase2day1)
- 1 .csv file per phase (each .csv file includes data from all days) stored in combinedRawData/RDMphase directory (RDMphase1.csv & RDMphase2.csv)
- 1 .csv file for all the raw RDM data (RDMall.csv) in the rawData/combinedRawData directory
- Reformatted raw data files for all participants on phase 1 day 3 and one participant from phase 1 day 11 (stored in rawData/phase1/day#/riskyChoice).
- RDM dataset includes both gain-only and loss-only tasks
- imports the large raw RDM .csv file (combinedRawData/RDMall.csv) and removes, renames and reformats columns so that data is basically ready for analysis
- removes the trials from the loss task in phase 1 that were incorrectly recorded due to a coding error
- replaces prolific IDs with subIDs
- RDM dataset includes both gain-only and loss-only tasks
- output: "/Volumes/CAP/data/combinedData/RDMallClean.csv"
- imports the raw Qualtrics questionnaire .csv files for phase 1 and phase 2
- cleans up the column names so that the output will only have the new variable names
- pulls out qualtrics responses only from those who we have RDM and AXCPT data
- removes prolific ID numbers and adds subID variable
- saves two .csv files
- output: "/Volumes/CAP/data/combinedData/QualtricsPhase#_subID_notScored.csv"
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loads the cleaned but not scored qualtrics .csv files: "Volumes/CAP/data/combinedData/QualtricsPhase#_subID_notScored.csv"
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makes not of participants who should be excluded based on missed attention checks, inappropriate age responses and notes duplicate responses and missed data.
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Only subject IDs are used here, so this script can be shared online.
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output #1: qualtricsExclusion.csv (3 columns)
- Volumes/CAP/data/combinedData/rdmExclusion.csv
- only includes subIDs (not prolific IDs)
- 3 columns: subID, phase1exclude (1=yes; 0=no; NA= no data), phase2exclude (1=yes; 0=no; NA=no data)
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output #2: QualtricsCombined_subID_scored_noDuplicates.csv
- identical to QualtricsCombined_subID_scored.csv just with a duplicate response removed
There is also a text file that documents anomolies with qualtrics responses: "/Volumes/CAP/documentation/QualtricsExclusionCodeSummary.txt"
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loads RDM data (gain-only and loss-only) from phase 1 and phase 2 (data/combinedData/RDMallClean.csv)
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For each participant in each phase, reports on the 4 categories of criteria for potential exclusion
- miss trials, missed attention checks, p(Gamble), and subject-level glm (choice ~gain+safe)
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output # 1: .csv file with nrow=544 (1 for each participant) with 25 variables/columns
- Volumes/CAP/data/combinedData/RDMqa.csv
- only includes subIDs (not prolific IDs)
- variable information for this file is located in documentation/RDMqaColumnInfo.csv
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output # 2: .csv file with nrow=544 (1 for each participant) with 3 variables/columns
- Volumes/CAP/data/combinedData/rdmExclusion.csv
- only includes subIDs (not prolific IDs)
- 3 columns: subID, phase1exclude (1=yes; 0=no; NA= no data), phase2exclude (1=yes; 0=no; NA=no data)
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Note that exclusion is based on both gain and loss tasks.
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loads AX data from phase 1 and phase 2 (data/combinedData/AXallClean.csv)
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For each participants in each phase, reports whether their global performance was greater than or equal to 60%
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output #1: AXqa.csv
- 544 rows with 5 variables/columns
- only includes subIDs
- 5 columns: subIDs, PcntCorPhs1, MeetCritPhs1, PcntCorPhs2, MeetCritPhs2
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output #2: axExclusion.csv
- 544 rows with 3 variables
- only includes subIDs
- 3 columns: subID, phase1exclude (1=yes; 0=no; NA= no data), phase2exclude (1=yes; 0=no; NA=no data)
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Loads phase 1 and 2 qualtrics data
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Removes open response columns
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Creates attentionCheck_passed columns for if a participant passed the attention check or not
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Translates state/county responses into FIPS codes
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Scores STAI-S, STAI-T, PSS, UCLA-L
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Recodes non-open response covid, demographic, and ses questions to numerics
- Maintains the original string responses for clarity
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Outputs QualtricsCombined_subID_scored.csv
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Loads exclusion .csv files for RDM, AX, and Qualtrics
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Combines the above and creates two new csv files for each phase that contains exclusion for RDM, AX, and Qualtrics
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output #1: allExclusionPhase1.csv
- 544 rows with 5 variables {subID, day, rdmPhs1exclude, axPhs1exclude, qualPhs1exclude}
- 1= exclude, 0 = keep, NA = no data
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output #2: allExclusionPhase2.csv
- 544 rows with 5 variables {subID, day, rdmPhs2exclude, axPhs2exclude, qualPhs2exclude}
- 1= exclude, 0 = keep, NA = no data
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Loads 4 datasets:
- QualtricsCombined_subID_scored_noDuplicates.csv
- RDMallClean.csv
- AXallClean.csv
- stateQuartiles.csv
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Adds some of the Qualtrics data to the RDM and AX datasets
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Scored qualtrics responses, covid questions, location (fips), SES are added to RDM and AX but responses to each STAIS, STAIT, UCLA-L, PSS question are not included.
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Adds quartile information
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Creates separate datasets for gain-only and loss-ony RDM tasks, both of which will have qualtrics information.
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output #1: AXallClean_Qualtrics.csv
- 108091 rows with 63 variables
- all of AX data with scored qualtrics responses (544 participants, 2 phases)
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output #2: RDMallClean_Qualtrics.csv
- 124572 rows with 72 variables
- all of RDM data (both gain and loss tasks) with scored qualtrics responses (544 participants, 2 phases)
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output #3: RDMallCleanGain_Qualtrics.csv
- 107219 rows with 72 variables
- RDM GAIN-ONLY data with scored qualtrics responses (544 participants, 2 phases)
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output #4: RDMallCleanLoss_Qualtrics.csv
- 17353 rows with 72 variables
- RDM LOSS-ONLY data with scored qualtrics responses (544 participants, 2 phases)