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Measuring Implicit Motives With the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms.

This repository shares two openly available databases and the fully reproducible R code for the paper Schönbrodt et al. (2018): Measuring Implicit Motives With the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms. (for full citation, see below).

PSE story database

We provide a database of 183,415 German sentences, nested in 26,389 stories provided by 4,570 participants, which have been coded by experts using Winter's (1994) coding system for the implicit affiliation/intimacy, achievement, and power motives.

As we expect that the PSE database will grow or change over time, we put a version number on it and archive old versions. We urge researchers to always refer to the specific version number and the specific doi when the database is used in order to ensure reproducibility.

The story dataset is hosted on the PsychArchives repository: http://dx.doi.org/10.23668/psycharchives.2738.

The reference for the database is:

Schönbrodt, F. D., Hagemeyer, B., Brandstätter, V., Czikmantori, T., Gröpel, P., Hennecke, M., Israel, L. S. F., Janson, K., Kemper, N., Köllner, M., Kopp, P. M., Mojzisch, A., Müller-Hotop, R., Prüfer, J., Quirin, M., Scheidemann, B., Schiestel, L., Schulz-Hardt, S., Sust, L., Zygar-Hoffmann, C., Schultheiss, O. C. (2020). Database of Expert-Coded German PSE Stories. PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.2738

PSE picture database

A database of classic and new PSE picture stimuli. These files are stored on Open Science Framework: (https://osf.io/dj8g9/):

PSE picture database

The project also contains norm values for all pictures.

License and citation of the databases

Both the database on coded PSE stories and the picture database (https://osf.io/pqckn/) can be downloaded and reused freely under a CC-BY 4.0 license.

Please cite this publication if you use either database in your work (BibTex):

Schönbrodt, F. D., Hagemeyer, B., Brandstätter, V., Czikmantori, T., Gröpel, P., Hennecke, M., Israel, L. S. F., Janson, K., Kemper, N., Köllner, M., Kopp, P. M., Mojzisch, A., Müller-Hotop, R., Prüfer, J., Quirin, M., Scheidemann, B., Schiestel, L., Schulz-Hardt, S., Sust, L., Zygar-Hoffmann, C., & Schultheiss, O. C. (2019). Measuring implicit motives with the Picture Story Exercise (PSE): Databases of expert-coded german stories, pictures, and updated picture norms. Journal of Personality Assessment.

Codebook of the PSE story database

Variable name Data type Comment Values
row_id numeric Unique row id
study_id factor Identifier for the original study/data set
coding_lab factor Lab where the coders were trained Munich, Erlangen, Osnabrueck, Trier
scoring_type factor Second sentence rule applied? eachSentence, 2nd_sentence_rule
participant_id factor Unique person identifier
gender factor Gender m = male, f = female, NA = missing/other
age factor Age category age <= 25, 25 < age <= 35, 35 < age <= 45, 45 < age <= 55, age > 55
USID factor Unique story identifier
UTID factor Unique text identifier (each sentence is one 'text')
pic_id factor Unique picture identifier See https://osf.io/pqckn/wiki/home/
pic_position numeric Position of picture in PSE task. The number encodes the picture position of valid stories, and not the position of the presented picture (e.g., if the first story was empty, the second picture gets the position `1').
pic_order factor Picture order in PSE task fixed for all participants, or variable? fixed, variable
unit numeric Sentence number within each story
wc numeric Word count (at sentence level)
sc numeric Sentence count (at story level)
pow numeric Presence of power imagery 0 (absent) or 1 (present)
ach numeric Presence of achievement imagery 0 (absent) or 1 (present)
aff numeric Presence of affiliation/intimacy imagery 0 (absent) or 1 (present)
motclass factor Multiclass combination of aff, ach, and pow codings. All mixed codings are collapsed into the category 'mixed'. none, ach, aff, pow, mixed
motclassfull factor Multiclass combination of aff, ach, and pow codings with all possible combinations. none, ach, aff, pow, achpow, affach, affpow, affachpow
text character The text of the sentence (spell-checked).
text_original character The orginal text of the sentence, as provided by the participants.
holdout logical Part of hold out set? (For future machine learning purposes) TRUE / FALSE

Note that in study MK3 there was a longer break between pictures 1--4 and 5--8.

How to reproduce the analyses for the paper

The R code in folder /R accesses the PSE database and computes all descriptive and inferential statistics reported in the paper. The folder and data file structure follows the Psych-DS standard. You need to download the story database (i.e., the file "PSE_1.0_redacted_data.tsv") from PsyArchives and place the file in the /raw_data subfolder.

  • subfolders /raw_data and /story_database contain the current PSE database, and the codebook table.
  • subfolder /processed_data stores intermediate data objects generated by the scripts. These can safely be deleted; they are useful when you directly start with a later script without computing all previous steps. No cache invalidation check is performed, so take care with cached objects.
  • subfolder /export stores exported summary files (currently only the picture pull norm table as Excel file)
  • Scripts are numbered in the order they should be executed. Source 1-start.R to load all necessary libraries and the data file. R scripts 2-... to 7-... reproduce all reported results in the paper. R scripts 8-... to 10-... have some additional exploratory analyses.

The file AMC-Database.Rnw in folder Manuscript computes the reproducible manuscript.

Full Reference

Schönbrodt, F. D., Hagemeyer, B., Brandstätter, V., Czikmantori, T., Gröpel, P., Hennecke, M., Israel, L. S. F., Janson, K., Kemper, N., Köllner, M., Kopp, P. M., Mojzisch, A., Müller-Hotop, R., Prüfer, J., Quirin, M., Scheidemann, B., Schiestel, L., Schulz-Hardt, S., Sust, L., Zygar-Hoffmann, C., Schultheiss, O. C. (2018). Measuring Implicit Motives With the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms. Journal of Personality Assessment.