130 lines
4.1 KiB
Markdown
130 lines
4.1 KiB
Markdown
Data for the HMC (Human Machine Communication) project
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================
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# Variables
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An overview of all variables on item level can be found in the [item
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reference](item_refrence.md) and in the [EXCEL codebook](HMC_codebook.xlsx).
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These files show which variables have been collected in each wave.
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# Folder and file organisation
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## Folders
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* `01_raw_data` contains the downloaded files from Qualtrics
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* `02_anonymized_data` contains the anonymized data files (otherwise this can
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still be considered raw data)
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* `03_cleaned` contains data files with harmonized data names; additionally some
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incorrect variable names were fixed and double entries from subjects who did a
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wave two or more times were removed; see `cleaning.R` and below for more
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details
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## Files
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* `HMC_codebook.xlsx` contains all variable names for all waves, with the
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original descriptions as presented in Qualtrics and the original variable
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names as well as the harmonized variable names
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* `HMC_variables.xlsx` contains an overview of the variables, their origin, who
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wanted them in the data, etc. This file is for internal use and is not
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commited with the public version
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# Data collection and data files
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The data collection was done in Qualtrics. The following projects are on
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https://kmrc.qualtrics.com/:
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* `AI_Trends_Wave1`
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* `AI_Trends_Wave2`
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* `AI_Trends_Wave3`
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* `AI_Trends_Wave4`
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* `AI_Trends_Wave4_sample2`
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* `AI_Trends_Wave5`
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* `AI_Trends_Wave5_sample2`
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* `AI_Trends_Wave6`
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* `AI_Trends_Wave6_sample2`
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## Sample
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### Sample 2 data files
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After wave 3, we re-invited wave-1 participants for waves 4–6 to increase
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statistical power for questions that did not require participation in all six
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waves. This departed from our original plan to invite only participants from the
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immediately preceding wave because ongoing monitoring showed that many non-users
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remained non-users and that relatively few participants perceived AI as a social
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actor. To capture more contemporary usage and obtain sufficient variation for
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research questions filtering for individuals that perceived AI as a social
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actor, we broadened recruitment in wave 4 to all wave-1 participants. Sample 2
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therefore contains only participants with at least one missing wave.
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## Download settings in Qualtrics
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The data were downloaded from Qualtrics as CSV files with the following
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settings.
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### Overall
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- Download all fields
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- Export values
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### CSV
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- Recode seen but unanswered questions as -99
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- Recode seen but unanswered multi-value fields as 0
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- Split multi-value fields into columns
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# Data anonymization
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After download from Qualtrics, files were put in the respective folders for each
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wave in `03_data/01_raw_data/wave*`. The script
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`03_data/01_raw_data/anonymization.R` mostly removes the `PROLIFIC_IDs` from the
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data and adds an anonymized ID `subj_id` with entries `subj0001 - sub1009` to
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all data sets.
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Irrelevant columns -- mostly automatically created by Qualtrics -- are also
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removed. See `anonymization.R` for details.
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The anonymized data files are saved to `03_data/02_anonymized_data/` as
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CSV files with file names `HMC_<wave>_anonymized.csv`.
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# Data preprocessing
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After data anonymization, some more rudimentary preprocessing was done on the
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data with the script `03_data/02_anonymized_data/cleaning.R`. Especially,
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the original variable names in Qualtrics were harmonized so they all follow the
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same structure.
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The cleaned data files are saved to `03_data/03_cleaned_data/`as
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CSV files with file names `HMC_<wave>_cleaned.csv`.
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The following section gives an overview of the problems in the data, that needed
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some cleaning.
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## Problems
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### with variable names over waves
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* `trust_fav` and `Q161` and `Q162`
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* `obj_know` and `Q158`
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* the labels of the intention variables were swapped
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--> `int_use_bhvr_fav = int_use_bhvr_noUser` and vice versa
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* ...
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<!-- TODO: Add more details -->
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### with subjects
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* Two entries in wave 1: `subj0762`
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* Three entries in wave 3: `subj1009`
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* We kept the first entry for each subject
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# TODOs
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* Add more preprocessing steps like variable renaming?
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* Get age (and other descriptives?) for subj1008 and subj1009 from Profilic
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data?
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